

import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__a  :Union[str, Any]										=       get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
      from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__a  :Optional[int]										=       25_6047
__a  :Optional[int]										=       25_6145

@require_sentencepiece
@require_tokenizers
class    _a (       snake_case__			,							unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase    :       int							=    NllbTokenizer
      _lowerCamelCase    :       Tuple							=    NllbTokenizerFast
      _lowerCamelCase    :       Union[str, Any]							=    True
      _lowerCamelCase    :       Dict							=    True
      _lowerCamelCase    :       Optional[Any]							=    {}





      def 						__A     (					self				:							Union[str, Any]     ):
            super().setUp()

            # We have a SentencePiece fixture for testing
            A_		   =							NllbTokenizer(UpperCAmelCase		,       keep_accents=UpperCAmelCase     )
            tokenizer.save_pretrained(self.tmpdirname     )





      def 						__A     (					self				:							Dict     ):
            A_		   =							NllbTokenizer(UpperCAmelCase		,       keep_accents=UpperCAmelCase     )

            A_		   =							tokenizer.tokenize("This is a test"     )
            self.assertListEqual(UpperCAmelCase		,       ["▁This", "▁is", "▁a", "▁t", "est"]     )

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(UpperCAmelCase     )		,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,       )

            A_		   =							tokenizer.tokenize("I was born in 92000, and this is falsé."     )
            self.assertListEqual(
                UpperCAmelCase		,       [
                    SPIECE_UNDERLINE + "I",
                    SPIECE_UNDERLINE + "was",
                    SPIECE_UNDERLINE + "b",
                    "or",
                    "n",
                    SPIECE_UNDERLINE + "in",
                    SPIECE_UNDERLINE + "",
                    "9",
                    "2",
                    "0",
                    "0",
                    "0",
                    ",",
                    SPIECE_UNDERLINE + "and",
                    SPIECE_UNDERLINE + "this",
                    SPIECE_UNDERLINE + "is",
                    SPIECE_UNDERLINE + "f",
                    "al",
                    "s",
                    "é",
                    ".",
                ]		,       )
            A_		   =							tokenizer.convert_tokens_to_ids(UpperCAmelCase     )
            self.assertListEqual(
                UpperCAmelCase		,       [
                    value + tokenizer.fairseq_offset
                    for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                ]		,       )

            A_		   =							tokenizer.convert_ids_to_tokens(UpperCAmelCase     )
            self.assertListEqual(
                UpperCAmelCase		,       [
                    SPIECE_UNDERLINE + "I",
                    SPIECE_UNDERLINE + "was",
                    SPIECE_UNDERLINE + "b",
                    "or",
                    "n",
                    SPIECE_UNDERLINE + "in",
                    SPIECE_UNDERLINE + "",
                    "<unk>",
                    "2",
                    "0",
                    "0",
                    "0",
                    ",",
                    SPIECE_UNDERLINE + "and",
                    SPIECE_UNDERLINE + "this",
                    SPIECE_UNDERLINE + "is",
                    SPIECE_UNDERLINE + "f",
                    "al",
                    "s",
                    "<unk>",
                    ".",
                ]		,       )





      def 						__A     (					self				:							List[Any]     ):
            A_		   =							(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                        A_		   =							self.rust_tokenizer_class.from_pretrained(UpperCAmelCase		,       **UpperCAmelCase     )
                        A_		   =							self.tokenizer_class.from_pretrained(UpperCAmelCase		,       **UpperCAmelCase     )

                        A_		   =							tempfile.mkdtemp()

                        A_		   =							tokenizer_r.save_pretrained(UpperCAmelCase     )
                        A_		   =							tokenizer_p.save_pretrained(UpperCAmelCase     )

                        # Checks it save with the same files + the tokenizer.json file for the fast one
                        self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )
                        A_		   =							tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
                        self.assertSequenceEqual(UpperCAmelCase		,       UpperCAmelCase     )

                        # Checks everything loads correctly in the same way
                        A_		   =							tokenizer_r.from_pretrained(UpperCAmelCase     )
                        A_		   =							tokenizer_p.from_pretrained(UpperCAmelCase     )

                        # Check special tokens are set accordingly on Rust and Python
                        for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(UpperCAmelCase		,       UpperCAmelCase     )     )

                        shutil.rmtree(UpperCAmelCase     )

                        # Save tokenizer rust, legacy_format=True
                        A_		   =							tempfile.mkdtemp()

                        A_		   =							tokenizer_r.save_pretrained(UpperCAmelCase		,       legacy_format=UpperCAmelCase     )
                        A_		   =							tokenizer_p.save_pretrained(UpperCAmelCase     )

                        # Checks it save with the same files
                        self.assertSequenceEqual(UpperCAmelCase		,       UpperCAmelCase     )

                        # Checks everything loads correctly in the same way
                        A_		   =							tokenizer_r.from_pretrained(UpperCAmelCase     )
                        A_		   =							tokenizer_p.from_pretrained(UpperCAmelCase     )

                        # Check special tokens are set accordingly on Rust and Python
                        for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(UpperCAmelCase		,       UpperCAmelCase     )     )

                        shutil.rmtree(UpperCAmelCase     )

                        # Save tokenizer rust, legacy_format=False
                        A_		   =							tempfile.mkdtemp()

                        A_		   =							tokenizer_r.save_pretrained(UpperCAmelCase		,       legacy_format=UpperCAmelCase     )
                        A_		   =							tokenizer_p.save_pretrained(UpperCAmelCase     )

                        # Checks it saved the tokenizer.json file
                        self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )

                        # Checks everything loads correctly in the same way
                        A_		   =							tokenizer_r.from_pretrained(UpperCAmelCase     )
                        A_		   =							tokenizer_p.from_pretrained(UpperCAmelCase     )

                        # Check special tokens are set accordingly on Rust and Python
                        for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(UpperCAmelCase		,       UpperCAmelCase     )     )

                        shutil.rmtree(UpperCAmelCase     )





      @require_torch
      def 						__A     (					self				:							Tuple     ):
            if not self.test_seqaseq:
                  return

            A_		   =							self.get_tokenizers()
            for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
                        # Longer text that will definitely require truncation.
                        A_		   =							[
                            " UN Chief Says There Is No Military Solution in Syria",
                            " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
                            " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
                            " will only worsen the violence and misery for millions of people.",
                        ]
                        A_		   =							[
                            "Şeful ONU declară că nu există o soluţie militară în Siria",
                            "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
                            " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
                            " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
                        ]
                        try:
                              A_		   =							tokenizer.prepare_seqaseq_batch(
                                  src_texts=UpperCAmelCase		,       tgt_texts=UpperCAmelCase		,       max_length=3		,       max_target_length=10		,       return_tensors="pt"		,       src_lang="eng_Latn"		,       tgt_lang="ron_Latn"		,       )
                        except NotImplementedError:
                              return
                        self.assertEqual(batch.input_ids.shape[1]		,       3     )
                        self.assertEqual(batch.labels.shape[1]		,       10     )
                        # max_target_length will default to max_length if not specified
                        A_		   =							tokenizer.prepare_seqaseq_batch(
                            UpperCAmelCase		,       tgt_texts=UpperCAmelCase		,       max_length=3		,       return_tensors="pt"     )
                        self.assertEqual(batch.input_ids.shape[1]		,       3     )
                        self.assertEqual(batch.labels.shape[1]		,       3     )

                        A_		   =							tokenizer.prepare_seqaseq_batch(
                            src_texts=UpperCAmelCase		,       max_length=3		,       max_target_length=10		,       return_tensors="pt"     )
                        self.assertEqual(batch_encoder_only.input_ids.shape[1]		,       3     )
                        self.assertEqual(batch_encoder_only.attention_mask.shape[1]		,       3     )
                        self.assertNotIn("decoder_input_ids"		,       UpperCAmelCase     )





      @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."     )
      def 						__A     (					self				:							List[Any]     ):
            pass







      def 						__A     (					self				:							Union[str, Any]     ):
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                        A_		   =							[AddedToken("<special>"		,       lstrip=UpperCAmelCase     )]

                        A_		   =							self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase		,       additional_special_tokens=UpperCAmelCase		,       **UpperCAmelCase     )
                        A_		   =							tokenizer_r.encode("Hey this is a <special> token"     )

                        A_		   =							tokenizer_r.encode("<special>"		,       add_special_tokens=UpperCAmelCase     )[0]

                        self.assertTrue(special_token_id in r_output     )

                        if self.test_slow_tokenizer:
                              A_		   =							self.rust_tokenizer_class.from_pretrained(
                                  UpperCAmelCase		,       additional_special_tokens=UpperCAmelCase		,       **UpperCAmelCase		,       )
                              A_		   =							self.tokenizer_class.from_pretrained(
                                  UpperCAmelCase		,       additional_special_tokens=UpperCAmelCase		,       **UpperCAmelCase     )

                              A_		   =							tokenizer_p.encode("Hey this is a <special> token"     )

                              A_		   =							tokenizer_cr.encode("Hey this is a <special> token"     )

                              self.assertEqual(UpperCAmelCase		,       UpperCAmelCase     )
                              self.assertEqual(UpperCAmelCase		,       UpperCAmelCase     )
                              self.assertTrue(special_token_id in p_output     )
                              self.assertTrue(special_token_id in cr_output     )




@require_torch
@require_sentencepiece
@require_tokenizers
class    _a (       unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase    :       Dict							=    "facebook/nllb-200-distilled-600M"
      _lowerCamelCase    :       Optional[int]							=    [
          " UN Chief Says There Is No Military Solution in Syria",
          " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
      ]
      _lowerCamelCase    :       str							=    [
          "Şeful ONU declară că nu există o soluţie militară în Siria",
          "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
          " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
          " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
      ]
      _lowerCamelCase    :       str							=    [
          2_5_6_0_4_7,
          1_6_2_9_7,
          1_3_4_4_0_8,
          8_1_6_5,
          2_4_8_0_6_6,
          1_4_7_3_4,
          9_5_0,
          1_1_3_5,
          1_0_5_7_2_1,
          3_5_7_3,
          8_3,
          2_7_3_5_2,
          1_0_8,
          4_9_4_8_6,
          2,
      ]





      @classmethod
      def 						__A     (					cls				:							int     ):
            A_		   =							NllbTokenizer.from_pretrained(
                cls.checkpoint_name		,       src_lang="eng_Latn"		,       tgt_lang="ron_Latn"     )
            A_		   =							1
            return cls





      def 						__A     (					self				:							Any     ):
            self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]		,       256001     )
            self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]		,       256002     )
            self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]		,       256057     )





      def 						__A     (					self				:							Union[str, Any]     ):
            A_		   =							self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
            self.assertListEqual(self.expected_src_tokens		,       UpperCAmelCase     )





      def 						__A     (					self				:							Tuple     ):
            self.assertIn(UpperCAmelCase		,       self.tokenizer.all_special_ids     )
            # fmt: off
            A_		   =							[RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
            # fmt: on

            A_		   =							self.tokenizer.decode(UpperCAmelCase		,       skip_special_tokens=UpperCAmelCase     )
            A_		   =							self.tokenizer.decode(generated_ids[1:]		,       skip_special_tokens=UpperCAmelCase     )
            self.assertEqual(UpperCAmelCase		,       UpperCAmelCase     )
            self.assertNotIn(self.tokenizer.eos_token		,       UpperCAmelCase     )





      def 						__A     (					self				:							Optional[int]     ):
            A_		   =							["this is gunna be a long sentence " * 20]
            assert isinstance(src_text[0]		,       UpperCAmelCase     )
            A_		   =							10
            A_		   =							self.tokenizer(UpperCAmelCase		,       max_length=UpperCAmelCase		,       truncation=UpperCAmelCase     ).input_ids[0]
            self.assertEqual(ids[-1]		,       2     )
            self.assertEqual(ids[0]		,       UpperCAmelCase     )
            self.assertEqual(len(UpperCAmelCase     )		,       UpperCAmelCase     )





      def 						__A     (					self				:							Dict     ):
            self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]     )		,       [256203, 3]     )





      def 						__A     (					self				:							Optional[Any]     ):
            A_		   =							tempfile.mkdtemp()
            A_		   =							self.tokenizer.fairseq_tokens_to_ids
            self.tokenizer.save_pretrained(UpperCAmelCase     )
            A_		   =							NllbTokenizer.from_pretrained(UpperCAmelCase     )
            self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,       UpperCAmelCase     )





      @require_torch
      def 						__A     (					self				:							Dict     ):
            A_		   =							self.tokenizer(
                self.src_text		,       text_target=self.tgt_text		,       padding=UpperCAmelCase		,       truncation=UpperCAmelCase		,       max_length=len(self.expected_src_tokens     )		,       return_tensors="pt"		,       )
            A_		   =							shift_tokens_right(
                batch["labels"]		,       self.tokenizer.pad_token_id		,       self.tokenizer.lang_code_to_id["ron_Latn"]     )

            self.assertIsInstance(UpperCAmelCase		,       UpperCAmelCase     )

            self.assertEqual((2, 15)		,       batch.input_ids.shape     )
            self.assertEqual((2, 15)		,       batch.attention_mask.shape     )
            A_		   =							batch.input_ids.tolist()[0]
            self.assertListEqual(self.expected_src_tokens		,       UpperCAmelCase     )
            self.assertEqual(UpperCAmelCase		,       batch.decoder_input_ids[0, 0]     )  # EOS
            # Test that special tokens are reset
            self.assertEqual(self.tokenizer.prefix_tokens		,       [EN_CODE]     )
            self.assertEqual(self.tokenizer.suffix_tokens		,       [self.tokenizer.eos_token_id]     )





      def 						__A     (					self				:							str     ):
            A_		   =							self.tokenizer(self.src_text		,       padding=UpperCAmelCase		,       truncation=UpperCAmelCase		,       max_length=3		,       return_tensors="pt"     )
            A_		   =							self.tokenizer(
                text_target=self.tgt_text		,       padding=UpperCAmelCase		,       truncation=UpperCAmelCase		,       max_length=10		,       return_tensors="pt"     )
            A_		   =							targets["input_ids"]
            A_		   =							shift_tokens_right(
                UpperCAmelCase		,       self.tokenizer.pad_token_id		,       decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]		,       )

            self.assertEqual(batch.input_ids.shape[1]		,       3     )
            self.assertEqual(batch.decoder_input_ids.shape[1]		,       10     )





      @require_torch
      def 						__A     (					self				:							List[Any]     ):
            A_		   =							self.tokenizer._build_translation_inputs(
                "A test"		,       return_tensors="pt"		,       src_lang="eng_Latn"		,       tgt_lang="fra_Latn"     )

            self.assertEqual(
                nested_simplify(UpperCAmelCase     )		,       {
                    # A, test, EOS, en_XX
                    "input_ids": [[256047, 70, 7356, 2]],
                    "attention_mask": [[1, 1, 1, 1]],
                    # ar_AR
                    "forced_bos_token_id": 256057,
                }		,       )







      @require_torch
      def 						__A     (					self				:							Any     ):
            A_		   =							True
            A_		   =							self.tokenizer(
                "UN Chief says there is no military solution in Syria"		,       src_lang="eng_Latn"		,       tgt_lang="fra_Latn"     )
            self.assertEqual(
                inputs.input_ids		,       [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]     )

            A_		   =							False
            A_		   =							self.tokenizer(
                "UN Chief says there is no military solution in Syria"		,       src_lang="eng_Latn"		,       tgt_lang="fra_Latn"     )
            self.assertEqual(
                inputs.input_ids		,       [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]     )
'''simple docstring'''




import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      BioGptForCausalLM,
      BioGptForSequenceClassification,
      BioGptForTokenClassification,
      BioGptModel,
      BioGptTokenizer,
  )
  from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				List[Any]						,					A						:				Dict						,					A						:				Optional[Any]=13						,					A						:				Optional[Any]=7						,					A						:				Union[str, Any]=True						,					A						:				Optional[Any]=True						,					A						:				int=False						,					A						:				str=True						,					A						:				Optional[Any]=99						,					A						:				Union[str, Any]=32						,					A						:				int=5						,					A						:				Tuple=4						,					A						:				Union[str, Any]=37						,					A						:				Dict="gelu"						,					A						:				Union[str, Any]=0.1						,					A						:				str=0.1						,					A						:				Union[str, Any]=512						,					A						:				int=16						,					A						:				List[str]=2						,					A						:				Tuple=0.02						,					A						:				int=3						,					A						:				List[str]=4						,					A						:				str=None						,					):
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Any							      =    batch_size
             _UpperCAmelCase     :  int							      =    seq_length
             _UpperCAmelCase     :  Union[str, Any]							      =    is_training
             _UpperCAmelCase     :  Any							      =    use_input_mask
             _UpperCAmelCase     :  Optional[Any]							      =    use_token_type_ids
             _UpperCAmelCase     :  str							      =    use_labels
             _UpperCAmelCase     :  Union[str, Any]							      =    vocab_size
             _UpperCAmelCase     :  Tuple							      =    hidden_size
             _UpperCAmelCase     :  Union[str, Any]							      =    num_hidden_layers
             _UpperCAmelCase     :  Optional[Any]							      =    num_attention_heads
             _UpperCAmelCase     :  Union[str, Any]							      =    intermediate_size
             _UpperCAmelCase     :  Union[str, Any]							      =    hidden_act
             _UpperCAmelCase     :  List[Any]							      =    hidden_dropout_prob
             _UpperCAmelCase     :  List[Any]							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  Optional[int]							      =    max_position_embeddings
             _UpperCAmelCase     :  str							      =    type_vocab_size
             _UpperCAmelCase     :  str							      =    type_sequence_label_size
             _UpperCAmelCase     :  int							      =    initializer_range
             _UpperCAmelCase     :  Optional[Any]							      =    num_labels
             _UpperCAmelCase     :  List[str]							      =    num_choices
             _UpperCAmelCase     :  List[str]							      =    scope




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Dict							      =    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size       )

             _UpperCAmelCase     :  Union[str, Any]							      =    None
             if self.use_input_mask:
                    _UpperCAmelCase     :  List[Any]							      =    random_attention_mask([self.batch_size, self.seq_length]       )

             _UpperCAmelCase     :  Any							      =    None
             if self.use_token_type_ids:
                    _UpperCAmelCase     :  List[str]							      =    ids_tensor([self.batch_size, self.seq_length]						,					self.type_vocab_size       )

             _UpperCAmelCase     :  Optional[int]							      =    None
             _UpperCAmelCase     :  Any							      =    None
             _UpperCAmelCase     :  Optional[int]							      =    None
             if self.use_labels:
                    _UpperCAmelCase     :  Optional[Any]							      =    ids_tensor([self.batch_size]						,					self.type_sequence_label_size       )
                    _UpperCAmelCase     :  Tuple							      =    ids_tensor([self.batch_size, self.seq_length]						,					self.num_labels       )
                    _UpperCAmelCase     :  List[str]							      =    ids_tensor([self.batch_size]						,					self.num_choices       )

             _UpperCAmelCase     :  List[Any]							      =    self.get_config()

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




      def      _A	(       self						:				Dict       ):
             return BioGptConfig(
                 vocab_size=self.vocab_size						,					hidden_size=self.hidden_size						,					num_hidden_layers=self.num_hidden_layers						,					num_attention_heads=self.num_attention_heads						,					intermediate_size=self.intermediate_size						,					hidden_act=self.hidden_act						,					hidden_dropout_prob=self.hidden_dropout_prob						,					attention_probs_dropout_prob=self.attention_probs_dropout_prob						,					max_position_embeddings=self.max_position_embeddings						,					type_vocab_size=self.type_vocab_size						,					is_decoder=A						,					initializer_range=self.initializer_range						,					)




      def      _A	(       self						:				int						,					A						:				List[Any]						,					A						:				Any						,					A						:				int						,					A						:				Union[str, Any]						,					A						:				Dict						,					A						:				List[Any]						,					A						:				Dict       ):
             _UpperCAmelCase     :  List[str]							      =    BioGptModel(config=A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Tuple							      =    model(A						,					attention_mask=A       )
             _UpperCAmelCase     :  int							      =    model(A       )
             self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)       )




      def      _A	(       self						:				List[Any]						,					A						:				str						,					A						:				List[Any]						,					A						:				Dict						,					A						:				List[Any]						,					A						:				List[str]						,					A						:				Union[str, Any]						,					A						:				int						,					A						:				List[str]						,					A						:				Dict						,					):
             _UpperCAmelCase     :  Optional[int]							      =    BioGptForCausalLM(config=A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Optional[int]							      =    model(A						,					attention_mask=A						,					token_type_ids=A						,					labels=A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)       )




      def      _A	(       self						:				List[Any]						,					A						:				str						,					A						:				str						,					A						:				str						,					A						:				Any						,					A						:				List[str]						,					*A						:				Optional[int]       ):
             _UpperCAmelCase     :  str							      =    BioGptModel(config=A       )
             model.to(A       )
             model.eval()

             # create attention mask
             _UpperCAmelCase     :  List[Any]							      =    torch.ones(input_ids.shape						,					dtype=torch.long						,					device=A       )
             _UpperCAmelCase     :  Optional[int]							      =    self.seq_length // 2
             _UpperCAmelCase     :  List[Any]							      =    0

             # first forward pass
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    model(A						,					attention_mask=A       ).to_tuple()

             # create hypothetical next token and extent to next_input_ids
             _UpperCAmelCase     :  List[str]							      =    ids_tensor((self.batch_size, 1)						,					config.vocab_size       )

             # change a random masked slice from input_ids
             _UpperCAmelCase     :  List[str]							      =    ids_tensor((1,)						,					A       ).item() + 1
             _UpperCAmelCase     :  str							      =    ids_tensor((self.batch_size, 1)						,					config.vocab_size       ).squeeze(-1       )
             _UpperCAmelCase     :  Any							      =    random_other_next_tokens

             # append to next input_ids and attn_mask
             _UpperCAmelCase     :  List[Any]							      =    torch.cat([input_ids, next_tokens]						,					dim=-1       )
             _UpperCAmelCase     :  Optional[int]							      =    torch.cat(
                 [attn_mask, torch.ones((attn_mask.shape[0], 1)						,					dtype=torch.long						,					device=A       )]						,					dim=1						,					)

             # get two different outputs
             _UpperCAmelCase     :  List[Any]							      =    model(A						,					attention_mask=A       )["last_hidden_state"]
             _UpperCAmelCase     :  Optional[Any]							      =    model(A						,					past_key_values=A						,					attention_mask=A       )["last_hidden_state"]

             # select random slice
             _UpperCAmelCase     :  Any							      =    ids_tensor((1,)						,					output_from_past.shape[-1]       ).item()
             _UpperCAmelCase     :  Optional[Any]							      =    output_from_no_past[:, -1, random_slice_idx].detach()
             _UpperCAmelCase     :  Any							      =    output_from_past[:, 0, random_slice_idx].detach()

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(A						,					A						,					atol=1E-3       )       )




      def      _A	(       self						:				int						,					A						:				Dict						,					A						:				str						,					A						:				Dict						,					A						:				Union[str, Any]						,					A						:				Any						,					*A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptModel(config=A       ).to(A       ).eval()

             _UpperCAmelCase     :  List[Any]							      =    torch.ones(input_ids.shape						,					dtype=torch.long						,					device=A       )

             # first forward pass
             _UpperCAmelCase     :  Union[str, Any]							      =    model(A						,					attention_mask=A						,					use_cache=A       )

             _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    outputs.to_tuple()

             # create hypothetical multiple next token and extent to next_input_ids
             _UpperCAmelCase     :  str							      =    ids_tensor((self.batch_size, 3)						,					config.vocab_size       )
             _UpperCAmelCase     :  Any							      =    ids_tensor((self.batch_size, 3)						,					2       )

             # append to next input_ids and
             _UpperCAmelCase     :  Optional[Any]							      =    torch.cat([input_ids, next_tokens]						,					dim=-1       )
             _UpperCAmelCase     :  Dict							      =    torch.cat([attention_mask, next_attn_mask]						,					dim=-1       )

             _UpperCAmelCase     :  Any							      =    model(A						,					attention_mask=A       )["last_hidden_state"]
             _UpperCAmelCase     :  Dict							      =    model(A						,					attention_mask=A						,					past_key_values=A       )[
                 "last_hidden_state"
             ]

             # select random slice
             _UpperCAmelCase     :  Dict							      =    ids_tensor((1,)						,					output_from_past.shape[-1]       ).item()
             _UpperCAmelCase     :  Dict							      =    output_from_no_past[:, -3:, random_slice_idx].detach()
             _UpperCAmelCase     :  Any							      =    output_from_past[:, :, random_slice_idx].detach()

             self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(A						,					A						,					atol=1E-3       )       )




      def      _A	(       self						:				Optional[Any]						,					A						:				Tuple						,					A						:				List[str]						,					A						:				Tuple						,					A						:				Dict						,					A						:				List[Any]						,					*A						:				Tuple						,					A						:				List[str]=False       ):
             _UpperCAmelCase     :  Optional[int]							      =    BioGptForCausalLM(A       )
             model.to(A       )
             if gradient_checkpointing:
                    model.gradient_checkpointing_enable()

             _UpperCAmelCase     :  Union[str, Any]							      =    model(A						,					labels=A       )
             self.parent.assertEqual(result.loss.shape						,					()       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)       )
             result.loss.backward()




      def      _A	(       self						:				Optional[Any]						,					A						:				Any						,					*A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Tuple							      =    BioGptModel(A       )
             _UpperCAmelCase     :  int							      =    model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers       )
             for key in model.state_dict().keys():
                    if "c_proj" in key and "weight" in key:
                           self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]       ) - model_std       )						,					0.001       )
                           self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]       ) - 0.0       )						,					0.01       )




      def      _A	(       self						:				Optional[int]						,					A						:				Dict						,					A						:				Tuple						,					A						:				Optional[int]						,					A						:				int						,					A						:				List[str]						,					*A						:				Dict       ):
             _UpperCAmelCase     :  Any							      =    self.num_labels
             _UpperCAmelCase     :  Any							      =    BioGptForTokenClassification(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Optional[int]							      =    model(A						,					attention_mask=A						,					token_type_ids=A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)       )





      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Dict							      =    self.prepare_config_and_inputs()
             (
                 (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  
             )     :  List[str]							      =    config_and_inputs
             _UpperCAmelCase     :  Optional[int]							      =    {"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict

@require_torch
class 	lowerCamelCase_	(snake_case__							, snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  List[str]						=						(
          (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
          if is_torch_available()
          else ()
      )
      __UpperCamelCase:  List[str]						=						(BioGptForCausalLM,) if is_torch_available() else ()
      __UpperCamelCase:  str						=						(
          {
              "feature-extraction": BioGptModel,
              "text-classification": BioGptForSequenceClassification,
              "text-generation": BioGptForCausalLM,
              "token-classification": BioGptForTokenClassification,
              "zero-shot": BioGptForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase:  Union[str, Any]						=						False




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    BioGptModelTester(self       )
             _UpperCAmelCase     :  str							      =    ConfigTester(self						,					config_class=A						,					hidden_size=37       )




      def      _A	(       self						:				Union[str, Any]       ):
             self.config_tester.run_common_tests()




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Any							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    _UpperCAmelCase     :  Tuple							      =    type
                    self.model_tester.create_and_check_model(*A       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_forward_and_backwards(*A						,					gradient_checkpointing=A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_weight_initialization(*A       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_for_token_classification(*A       )




      @slow
      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
             model.to(A       )
             _UpperCAmelCase     :  Tuple							      =    BioGptTokenizer.from_pretrained("microsoft/biogpt"       )

             _UpperCAmelCase     :  str							      =    "left"

             # Define PAD Token = EOS Token = 50256
             _UpperCAmelCase     :  Any							      =    tokenizer.eos_token
             _UpperCAmelCase     :  int							      =    model.config.eos_token_id

             # use different length sentences to test batching
             _UpperCAmelCase     :  Any							      =    [
                 "Hello, my dog is a little",
                 "Today, I",
             ]

             _UpperCAmelCase     :  Tuple							      =    tokenizer(A						,					return_tensors="pt"						,					padding=A       )
             _UpperCAmelCase     :  Optional[Any]							      =    inputs["input_ids"].to(A       )

             _UpperCAmelCase     :  Any							      =    model.generate(
                 input_ids=A						,					attention_mask=inputs["attention_mask"].to(A       )						,					)

             _UpperCAmelCase     :  int							      =    tokenizer(sentences[0]						,					return_tensors="pt"       ).input_ids.to(A       )
             _UpperCAmelCase     :  List[Any]							      =    model.generate(input_ids=A       )

             _UpperCAmelCase     :  List[Any]							      =    inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
             _UpperCAmelCase     :  int							      =    tokenizer(sentences[1]						,					return_tensors="pt"       ).input_ids.to(A       )
             _UpperCAmelCase     :  int							      =    model.generate(input_ids=A						,					max_length=model.config.max_length - num_paddings       )

             _UpperCAmelCase     :  Dict							      =    tokenizer.batch_decode(A						,					skip_special_tokens=A       )
             _UpperCAmelCase     :  Any							      =    tokenizer.decode(output_non_padded[0]						,					skip_special_tokens=A       )
             _UpperCAmelCase     :  Optional[int]							      =    tokenizer.decode(output_padded[0]						,					skip_special_tokens=A       )

             _UpperCAmelCase     :  str							      =    [
                 "Hello, my dog is a little bit bigger than a little bit.",
                 "Today, I have a good idea of how to use the information",
             ]
             self.assertListEqual(A						,					A       )
             self.assertListEqual(A						,					[non_padded_sentence, padded_sentence]       )




      @slow
      def      _A	(       self						:				str       ):
             for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _UpperCAmelCase     :  Optional[Any]							      =    BioGptModel.from_pretrained(A       )
                    self.assertIsNotNone(A       )




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  int							      =    self.model_tester.prepare_config_and_inputs_for_common()
             _UpperCAmelCase     :  str							      =    3
             _UpperCAmelCase     :  List[str]							      =    input_dict["input_ids"]
             _UpperCAmelCase     :  Dict							      =    input_ids.ne(1       ).to(A       )
             _UpperCAmelCase     :  List[Any]							      =    ids_tensor([self.model_tester.batch_size]						,					self.model_tester.type_sequence_label_size       )
             _UpperCAmelCase     :  List[str]							      =    BioGptForSequenceClassification(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  List[str]							      =    model(A						,					attention_mask=A						,					labels=A       )
             self.assertEqual(result.logits.shape						,					(self.model_tester.batch_size, self.model_tester.num_labels)       )





      def      _A	(       self						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[Any]							      =    self.model_tester.prepare_config_and_inputs_for_common()
             _UpperCAmelCase     :  int							      =    3
             _UpperCAmelCase     :  Dict							      =    "multi_label_classification"
             _UpperCAmelCase     :  Optional[Any]							      =    input_dict["input_ids"]
             _UpperCAmelCase     :  Optional[int]							      =    input_ids.ne(1       ).to(A       )
             _UpperCAmelCase     :  Tuple							      =    ids_tensor(
                 [self.model_tester.batch_size, config.num_labels]						,					self.model_tester.type_sequence_label_size       ).to(torch.float       )
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptForSequenceClassification(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Tuple							      =    model(A						,					attention_mask=A						,					labels=A       )
             self.assertEqual(result.logits.shape						,					(self.model_tester.batch_size, self.model_tester.num_labels)       )

@require_torch
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      @slow
      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
             _UpperCAmelCase     :  List[str]							      =    torch.tensor([[2, 4805, 9, 656, 21]]       )
             _UpperCAmelCase     :  List[Any]							      =    model(A       )[0]

             _UpperCAmelCase     :  int							      =    42384

             _UpperCAmelCase     :  int							      =    torch.Size((1, 5, vocab_size)       )
             self.assertEqual(output.shape						,					A       )

             _UpperCAmelCase     :  Any							      =    torch.tensor(
                 [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]       )

             self.assertTrue(torch.allclose(output[:, :3, :3]						,					A						,					atol=1E-4       )       )





      @slow
      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  str							      =    BioGptTokenizer.from_pretrained("microsoft/biogpt"       )
             _UpperCAmelCase     :  Tuple							      =    BioGptForCausalLM.from_pretrained("microsoft/biogpt"       )
             model.to(A       )

             torch.manual_seed(0       )
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer("COVID-19 is"						,					return_tensors="pt"       ).to(A       )
             _UpperCAmelCase     :  Dict							      =    model.generate(
                 **A						,					min_length=100						,					max_length=1024						,					num_beams=5						,					early_stopping=A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer.decode(output_ids[0]						,					skip_special_tokens=A       )

             _UpperCAmelCase     :  List[str]							      =    (
                 "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
                 " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
                 " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
                 " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
                 " more than 800,000 deaths."
             )
             self.assertEqual(A						,					A       )

