
'''simple docstring'''
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class  UpperCAmelCase_					(						snake_case__     ,    unittest.TestCase       ):
 UpperCAmelCase_		     =    BartTokenizer
 UpperCAmelCase_		     =    BartTokenizerFast
 UpperCAmelCase_		     =    True
 UpperCAmelCase_		     =    filter_roberta_detectors





 def 							snake_case__		(     self):
     super().setUp()
     snake_case_			:     Tuple        =    [
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "\u0120",
         "\u0120l",
         "\u0120n",
         "\u0120lo",
         "\u0120low",
         "er",
         "\u0120lowest",
         "\u0120newer",
         "\u0120wider",
         "<unk>",
     ]
     snake_case_			:     str        =    dict(zip(lowercase_			,							range(len(lowercase_))))
     snake_case_			:     List[Any]        =    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
     snake_case_			:     Optional[int]        =    {"unk_token": "<unk>"}

     snake_case_			:     str        =    os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["vocab_file"])
     snake_case_			:     Any        =    os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["merges_file"])
     with open(self.vocab_file			,							"w"			,							encoding="utf-8") as fp:
         fp.write(json.dumps(lowercase_) + "\n")
     with open(self.merges_file			,							"w"			,							encoding="utf-8") as fp:
         fp.write("\n".join(lowercase_))





 def 							snake_case__		(     self			,							**lowercase_):
     kwargs.update(self.special_tokens_map)
     return self.tokenizer_class.from_pretrained(self.tmpdirname			,							**lowercase_)





 def 							snake_case__		(     self			,							**lowercase_):
     kwargs.update(self.special_tokens_map)
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,							**lowercase_)





 def 							snake_case__		(     self			,							lowercase_):
     return "lower newer", "lower newer"





 @cached_property
 def 							snake_case__		(     self):
     return BartTokenizer.from_pretrained("facebook/bart-large")





 @cached_property
 def 							snake_case__		(     self):
     return BartTokenizerFast.from_pretrained("facebook/bart-large")





 @require_torch
 def 							snake_case__		(     self):
     snake_case_			:     str        =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
     snake_case_			:     Optional[Any]        =    [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case_			:     Union[str, Any]        =    tokenizer(lowercase_			,							max_length=len(lowercase_)			,							padding=lowercase_			,							return_tensors="pt")
         self.assertIsInstance(lowercase_			,							lowercase_)

         self.assertEqual((2, 9)			,							batch.input_ids.shape)
         self.assertEqual((2, 9)			,							batch.attention_mask.shape)
         snake_case_			:     List[str]        =    batch.input_ids.tolist()[0]
         self.assertListEqual(lowercase_			,							lowercase_)
         # Test that special tokens are reset





 @require_torch
 def 							snake_case__		(     self):
     snake_case_			:     List[Any]        =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case_			:     Dict        =    tokenizer(lowercase_			,							padding=lowercase_			,							return_tensors="pt")
         # check if input_ids are returned and no labels
         self.assertIn("input_ids"			,							lowercase_)
         self.assertIn("attention_mask"			,							lowercase_)
         self.assertNotIn("labels"			,							lowercase_)
         self.assertNotIn("decoder_attention_mask"			,							lowercase_)





 @require_torch
 def 							snake_case__		(     self):
     snake_case_			:     List[str]        =    [
         "Summary of the text.",
         "Another summary.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case_			:     Optional[Any]        =    tokenizer(text_target=lowercase_			,							max_length=32			,							padding="max_length"			,							return_tensors="pt")
         self.assertEqual(32			,							targets["input_ids"].shape[1])





 @require_torch
 def 							snake_case__		(     self):
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case_			:     Tuple        =    tokenizer(
             ["I am a small frog" * 10_24, "I am a small frog"]			,							padding=lowercase_			,							truncation=lowercase_			,							return_tensors="pt")
         self.assertIsInstance(lowercase_			,							lowercase_)
         self.assertEqual(batch.input_ids.shape			,							(2, 10_24))





 @require_torch
 def 							snake_case__		(     self):
     snake_case_			:     Union[str, Any]        =    ["A long paragraph for summarization."]
     snake_case_			:     int        =    [
         "Summary of the text.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case_			:     Tuple        =    tokenizer(lowercase_			,							return_tensors="pt")
         snake_case_			:     Union[str, Any]        =    tokenizer(text_target=lowercase_			,							return_tensors="pt")
         snake_case_			:     Optional[int]        =    inputs["input_ids"]
         snake_case_			:     Dict        =    targets["input_ids"]
         self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
         self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
         self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
         self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())





 def 							snake_case__		(     self):
     pass





 def 							snake_case__		(     self):
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
             snake_case_			:     Any        =    self.rust_tokenizer_class.from_pretrained(lowercase_			,							**lowercase_)
             snake_case_			:     List[Any]        =    self.tokenizer_class.from_pretrained(lowercase_			,							**lowercase_)
             snake_case_			:     List[Any]        =    "A, <mask> AllenNLP sentence."
             snake_case_			:     Optional[int]        =    tokenizer_r.encode_plus(lowercase_			,							add_special_tokens=lowercase_			,							return_token_type_ids=lowercase_)
             snake_case_			:     List[str]        =    tokenizer_p.encode_plus(lowercase_			,							add_special_tokens=lowercase_			,							return_token_type_ids=lowercase_)

             # token_type_ids should put 0 everywhere
             self.assertEqual(sum(tokens_r["token_type_ids"])			,							sum(tokens_p["token_type_ids"]))

             # attention_mask should put 1 everywhere, so sum over length should be 1
             self.assertEqual(
                 sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])			,							sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])			,							)

             snake_case_			:     Optional[Any]        =    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
             snake_case_			:     List[str]        =    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

             # Rust correctly handles the space before the mask while python doesnt
             self.assertSequenceEqual(tokens_p["input_ids"]			,							[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
             self.assertSequenceEqual(tokens_r["input_ids"]			,							[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

             self.assertSequenceEqual(
                 lowercase_			,							["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
             self.assertSequenceEqual(
                 lowercase_			,							["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])






'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


a_           =    logging.get_logger(__name__)

a_           =    {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class  UpperCAmelCase_					(						snake_case__       ):
 UpperCAmelCase_		     =    """blenderbot"""
 UpperCAmelCase_		     =    ["""past_key_values"""]
 UpperCAmelCase_		     =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





 def __init__(     self			,							lowercase_=80_08			,							lowercase_=1_28			,							lowercase_=2			,							lowercase_=1_02_40			,							lowercase_=32			,							lowercase_=24			,							lowercase_=1_02_40			,							lowercase_=32			,							lowercase_=0.0			,							lowercase_=0.0			,							lowercase_=True			,							lowercase_=True			,							lowercase_="gelu"			,							lowercase_=25_60			,							lowercase_=0.1			,							lowercase_=0.0			,							lowercase_=0.0			,							lowercase_=0.02			,							lowercase_=1			,							lowercase_=False			,							lowercase_=0			,							lowercase_=1			,							lowercase_=2			,							lowercase_=3			,							lowercase_=2			,							**lowercase_			,							):
     snake_case_			:     str        =    vocab_size
     snake_case_			:     str        =    max_position_embeddings
     snake_case_			:     Optional[Any]        =    d_model
     snake_case_			:     Any        =    encoder_ffn_dim
     snake_case_			:     Optional[Any]        =    encoder_layers
     snake_case_			:     List[Any]        =    encoder_attention_heads
     snake_case_			:     List[Any]        =    decoder_ffn_dim
     snake_case_			:     Optional[int]        =    decoder_layers
     snake_case_			:     Optional[int]        =    decoder_attention_heads
     snake_case_			:     Any        =    dropout
     snake_case_			:     List[str]        =    attention_dropout
     snake_case_			:     int        =    activation_dropout
     snake_case_			:     Dict        =    activation_function
     snake_case_			:     Any        =    init_std
     snake_case_			:     Union[str, Any]        =    encoder_layerdrop
     snake_case_			:     Optional[int]        =    decoder_layerdrop
     snake_case_			:     Any        =    use_cache
     snake_case_			:     List[str]        =    encoder_layers
     snake_case_			:     str        =    scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         pad_token_id=lowercase_			,							bos_token_id=lowercase_			,							eos_token_id=lowercase_			,							is_encoder_decoder=lowercase_			,							decoder_start_token_id=lowercase_			,							encoder_no_repeat_ngram_size=lowercase_			,							forced_eos_token_id=lowercase_			,							**lowercase_			,							)






class  UpperCAmelCase_					(						snake_case__       ):





 @property
 def 							snake_case__		(     self):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     int        =    OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ])
         if self.use_past:
             snake_case_			:     Any        =    {0: "batch"}
             snake_case_			:     Optional[int]        =    {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
             snake_case_			:     int        =    {0: "batch", 1: "decoder_sequence"}
             snake_case_			:     int        =    {0: "batch", 1: "decoder_sequence"}
         if self.use_past:
             self.fill_with_past_key_values_(lowercase_			,							direction="inputs")
     elif self.task == "causal-lm":
         snake_case_			:     Tuple        =    OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ])
         if self.use_past:
             snake_case_      ,			snake_case_			:     Any        =    self.num_layers
             for i in range(lowercase_):
                 snake_case_			:     Dict        =    {0: "batch", 2: "past_sequence + sequence"}
                 snake_case_			:     Dict        =    {0: "batch", 2: "past_sequence + sequence"}
     else:
         snake_case_			:     str        =    OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                 ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
             ])

     return common_inputs





 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 							snake_case__		(     self):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     Optional[int]        =    super().outputs
     else:
         snake_case_			:     Dict        =    super(lowercase_			,							self).outputs
         if self.use_past:
             snake_case_      ,			snake_case_			:     Any        =    self.num_layers
             for i in range(lowercase_):
                 snake_case_			:     str        =    {0: "batch", 2: "past_sequence + sequence"}
                 snake_case_			:     int        =    {0: "batch", 2: "past_sequence + sequence"}
     return common_outputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     snake_case_			:     List[Any]        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)
     # Generate decoder inputs
     snake_case_			:     int        =    seq_length if not self.use_past else 1
     snake_case_			:     List[str]        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)
     snake_case_			:     str        =    {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
     snake_case_			:     Any        =    dict(**lowercase_			,							**lowercase_)

     if self.use_past:
         if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
         else:
             import torch
         snake_case_      ,			snake_case_			:     int        =    common_inputs["input_ids"].shape
         snake_case_			:     str        =    common_inputs["decoder_input_ids"].shape[1]
         snake_case_      ,			snake_case_			:     Tuple        =    self.num_attention_heads
         snake_case_			:     str        =    (
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         snake_case_			:     Dict        =    decoder_seq_length
         snake_case_			:     List[Any]        =    (
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )
         snake_case_			:     Optional[Any]        =    torch.cat(
             [common_inputs["decoder_attention_mask"], torch.ones(lowercase_			,							lowercase_)]			,							dim=1)
         snake_case_			:     Tuple        =    []
         snake_case_      ,			snake_case_			:     Tuple        =    self.num_layers

         for _ in range(lowercase_):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(lowercase_),
                     torch.zeros(lowercase_),
                     torch.zeros(lowercase_),
                     torch.zeros(lowercase_),
                 ))
     return common_inputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     snake_case_			:     str        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)

     if self.use_past:
         if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
         else:
             import torch
         snake_case_      ,			snake_case_			:     Optional[Any]        =    common_inputs["input_ids"].shape
         snake_case_			:     List[str]        =    seqlen
         snake_case_      ,			snake_case_			:     Optional[int]        =    self.num_layers
         snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.num_attention_heads
         snake_case_			:     Optional[int]        =    (
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         snake_case_			:     Dict        =    common_inputs["attention_mask"].dtype
         snake_case_			:     Optional[Any]        =    torch.cat(
             [common_inputs["attention_mask"], torch.ones(lowercase_			,							lowercase_			,							dtype=lowercase_)]			,							dim=1)
         snake_case_			:     Tuple        =    [
             (torch.zeros(lowercase_), torch.zeros(lowercase_)) for _ in range(lowercase_)
         ]
     return common_inputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     # Copied from OnnxConfig.generate_dummy_inputs
     # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     snake_case_			:     Any        =    compute_effective_axis_dimension(
         lowercase_			,							fixed_dimension=OnnxConfig.default_fixed_batch			,							num_token_to_add=0)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     snake_case_			:     Tuple        =    tokenizer.num_special_tokens_to_add(lowercase_)
     snake_case_			:     Tuple        =    compute_effective_axis_dimension(
         lowercase_			,							fixed_dimension=OnnxConfig.default_fixed_sequence			,							num_token_to_add=lowercase_)

     # Generate dummy inputs according to compute batch and sequence
     snake_case_			:     Optional[int]        =    [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
     snake_case_			:     Tuple        =    dict(tokenizer(lowercase_			,							return_tensors=lowercase_))
     return common_inputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     Optional[Any]        =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             lowercase_			,							batch_size=lowercase_			,							seq_length=lowercase_			,							is_pair=lowercase_			,							framework=lowercase_)

     elif self.task == "causal-lm":
         snake_case_			:     Dict        =    self._generate_dummy_inputs_for_causal_lm(
             lowercase_			,							batch_size=lowercase_			,							seq_length=lowercase_			,							is_pair=lowercase_			,							framework=lowercase_)
     else:
         snake_case_			:     Dict        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_			,							batch_size=lowercase_			,							seq_length=lowercase_			,							is_pair=lowercase_			,							framework=lowercase_)

     return common_inputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     int        =    super()._flatten_past_key_values_(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)
     else:
         snake_case_			:     List[Any]        =    super(lowercase_			,							self)._flatten_past_key_values_(
             lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_):
     if direction not in ["inputs", "outputs"]:
         raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given')

     snake_case_			:     Union[str, Any]        =    "past_key_values" if direction == "inputs" else "present"
     snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.num_layers

     snake_case_			:     List[Any]        =    "past_encoder_sequence"
     snake_case_			:     str        =    "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

     for i in range(lowercase_):
         snake_case_			:     Union[str, Any]        =    {0: "batch", 2: decoder_sequence}
         snake_case_			:     str        =    {0: "batch", 2: decoder_sequence}
         snake_case_			:     Any        =    {0: "batch", 2: encoder_sequence}
         snake_case_			:     Dict        =    {0: "batch", 2: encoder_sequence}




