

'''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     A__					:



    def __init__(    self						, UpperCamelCase__						, UpperCamelCase__=13						, UpperCamelCase__=7						, UpperCamelCase__=True						, UpperCamelCase__=True						, UpperCamelCase__=False						, UpperCamelCase__=True						, UpperCamelCase__=99						, UpperCamelCase__=32						, UpperCamelCase__=5						, UpperCamelCase__=4						, UpperCamelCase__=37						, UpperCamelCase__="gelu"						, UpperCamelCase__=0.1						, UpperCamelCase__=0.1						, UpperCamelCase__=512						, UpperCamelCase__=16						, UpperCamelCase__=2						, UpperCamelCase__=0.02						, UpperCamelCase__=3						, UpperCamelCase__=4						, UpperCamelCase__=None						, )  ->					Optional[Any]:





           '''simple docstring'''
           A_		    =					parent
           A_		    =					batch_size
           A_		    =					seq_length
           A_		    =					is_training
           A_		    =					use_input_mask
           A_		    =					use_token_type_ids
           A_		    =					use_labels
           A_		    =					vocab_size
           A_		    =					hidden_size
           A_		    =					num_hidden_layers
           A_		    =					num_attention_heads
           A_		    =					intermediate_size
           A_		    =					hidden_act
           A_		    =					hidden_dropout_prob
           A_		    =					attention_probs_dropout_prob
           A_		    =					max_position_embeddings
           A_		    =					type_vocab_size
           A_		    =					type_sequence_label_size
           A_		    =					initializer_range
           A_		    =					num_labels
           A_		    =					num_choices
           A_		    =					scope



    def     snake_case_	(    self				)  ->					Optional[Any]:





           '''simple docstring'''
           A_		    =					ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size				)

           A_		    =					None
           if self.use_input_mask:
                  A_		    =					random_attention_mask([self.batch_size, self.seq_length]				)

           A_		    =					None
           if self.use_token_type_ids:
                  A_		    =					ids_tensor([self.batch_size, self.seq_length]						, self.type_vocab_size				)

           A_		    =					None
           A_		    =					None
           A_		    =					None
           if self.use_labels:
                  A_		    =					ids_tensor([self.batch_size]						, self.type_sequence_label_size				)
                  A_		    =					ids_tensor([self.batch_size, self.seq_length]						, self.num_labels				)
                  A_		    =					ids_tensor([self.batch_size]						, self.num_choices				)

           A_		    =					self.get_config()

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



    def     snake_case_	(    self				)  ->					Dict:





           '''simple docstring'''
           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=_snake_case						, initializer_range=self.initializer_range						, )



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__				)  ->					Optional[Any]:





           '''simple docstring'''
           A_		    =					BioGptModel(config=_snake_case				)
           model.to(_snake_case				)
           model.eval()
           A_		    =					model(_snake_case						, attention_mask=_snake_case				)
           A_		    =					model(_snake_case				)
           self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)				)



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, )  ->					Optional[int]:





           '''simple docstring'''
           A_		    =					BioGptForCausalLM(config=_snake_case				)
           model.to(_snake_case				)
           model.eval()
           A_		    =					model(_snake_case						, attention_mask=_snake_case						, token_type_ids=_snake_case						, labels=_snake_case				)
           self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size)				)



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, *UpperCamelCase__				)  ->					str:





           '''simple docstring'''
           A_		    =					BioGptModel(config=_snake_case				)
           model.to(_snake_case				)
           model.eval()

           # create attention mask
           A_		    =					torch.ones(input_ids.shape						, dtype=torch.long						, device=_snake_case				)
           A_		    =					self.seq_length // 2
           A_		    =					0

           # first forward pass
           A_    ,	A_		    =					model(_snake_case						, attention_mask=_snake_case				).to_tuple()

           # create hypothetical next token and extent to next_input_ids
           A_		    =					ids_tensor((self.batch_size, 1)						, config.vocab_size				)

           # change a random masked slice from input_ids
           A_		    =					ids_tensor((1,)						, _snake_case				).item() + 1
           A_		    =					ids_tensor((self.batch_size, 1)						, config.vocab_size				).squeeze(-1				)
           A_		    =					random_other_next_tokens

           # append to next input_ids and attn_mask
           A_		    =					torch.cat([input_ids, next_tokens]						, dim=-1				)
           A_		    =					torch.cat(
               [attn_mask, torch.ones((attn_mask.shape[0], 1)						, dtype=torch.long						, device=_snake_case				)]						, dim=1						, )

           # get two different outputs
           A_		    =					model(_snake_case						, attention_mask=_snake_case				)["""last_hidden_state"""]
           A_		    =					model(_snake_case						, past_key_values=_snake_case						, attention_mask=_snake_case				)["""last_hidden_state"""]

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

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



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, *UpperCamelCase__				)  ->					str:





           '''simple docstring'''
           A_		    =					BioGptModel(config=_snake_case				).to(_snake_case				).eval()

           A_		    =					torch.ones(input_ids.shape						, dtype=torch.long						, device=_snake_case				)

           # first forward pass
           A_		    =					model(_snake_case						, attention_mask=_snake_case						, use_cache=_snake_case				)

           A_    ,	A_		    =					outputs.to_tuple()

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

           # append to next input_ids and
           A_		    =					torch.cat([input_ids, next_tokens]						, dim=-1				)
           A_		    =					torch.cat([attention_mask, next_attn_mask]						, dim=-1				)

           A_		    =					model(_snake_case						, attention_mask=_snake_case				)["""last_hidden_state"""]
           A_		    =					model(_snake_case						, attention_mask=_snake_case						, past_key_values=_snake_case				)[
               """last_hidden_state"""
           ]

           # select random slice
           A_		    =					ids_tensor((1,)						, output_from_past.shape[-1]				).item()
           A_		    =					output_from_no_past[:, -3:, random_slice_idx].detach()
           A_		    =					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(_snake_case						, _snake_case						, atol=1e-3				)				)



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, *UpperCamelCase__						, UpperCamelCase__=False				)  ->					str:





           '''simple docstring'''
           A_		    =					BioGptForCausalLM(_snake_case				)
           model.to(_snake_case				)
           if gradient_checkpointing:
                  model.gradient_checkpointing_enable()

           A_		    =					model(_snake_case						, labels=_snake_case				)
           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     snake_case_	(    self						, UpperCamelCase__						, *UpperCamelCase__				)  ->					str:





           '''simple docstring'''
           A_		    =					BioGptModel(_snake_case				)
           A_		    =					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     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, *UpperCamelCase__				)  ->					int:





           '''simple docstring'''
           A_		    =					self.num_labels
           A_		    =					BioGptForTokenClassification(_snake_case				)
           model.to(_snake_case				)
           model.eval()
           A_		    =					model(_snake_case						, attention_mask=_snake_case						, token_type_ids=_snake_case				)
           self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.num_labels)				)







    def     snake_case_	(    self				)  ->					List[str]:





           '''simple docstring'''
           A_		    =					self.prepare_config_and_inputs()
           (
               (
               A_       
           )    ,	(
               A_       
           )    ,	(
               A_       
           )    ,	(
               A_       
           )    ,	(
               A_       
           )    ,	(
               A_       
           )    ,	(
               A_       
           )    ,	       
           )		    =					config_and_inputs
           A_		    =					{"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict


@require_torch
class     A__					(					_snake_case  ,  _snake_case  ,  _snake_case  ,  unittest.TestCase	):
    lowercase		    =						(
        (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
        if is_torch_available()
        else ()
    )
    lowercase		    =						(BioGptForCausalLM,) if is_torch_available() else ()
    lowercase		    =						(
        {
            '''feature-extraction''': BioGptModel,
            '''text-classification''': BioGptForSequenceClassification,
            '''text-generation''': BioGptForCausalLM,
            '''token-classification''': BioGptForTokenClassification,
            '''zero-shot''': BioGptForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowercase		    =						False



    def     snake_case_	(    self				)  ->					Dict:





           '''simple docstring'''
           A_		    =					BioGptModelTester(self				)
           A_		    =					ConfigTester(self						, config_class=_snake_case						, hidden_size=37				)



    def     snake_case_	(    self				)  ->					Optional[Any]:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def     snake_case_	(    self				)  ->					List[str]:





           '''simple docstring'''
           A_		    =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_snake_case				)



    def     snake_case_	(    self				)  ->					Any:





           '''simple docstring'''
           A_		    =					self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  A_		    =					type
                  self.model_tester.create_and_check_model(*_snake_case				)



    def     snake_case_	(    self				)  ->					str:





           '''simple docstring'''
           A_		    =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_snake_case				)



    def     snake_case_	(    self				)  ->					Union[str, Any]:





           '''simple docstring'''
           A_		    =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_forward_and_backwards(*_snake_case						, gradient_checkpointing=_snake_case				)



    def     snake_case_	(    self				)  ->					List[str]:





           '''simple docstring'''
           A_		    =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_snake_case				)



    def     snake_case_	(    self				)  ->					Dict:





           '''simple docstring'''
           A_		    =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_weight_initialization(*_snake_case				)



    def     snake_case_	(    self				)  ->					Union[str, Any]:





           '''simple docstring'''
           A_		    =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_for_token_classification(*_snake_case				)



    @slow
    def     snake_case_	(    self				)  ->					Tuple:





           '''simple docstring'''
           A_		    =					BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""				)
           model.to(_snake_case				)
           A_		    =					BioGptTokenizer.from_pretrained("""microsoft/biogpt"""				)

           A_		    =					"""left"""

           # Define PAD Token = EOS Token = 50256
           A_		    =					tokenizer.eos_token
           A_		    =					model.config.eos_token_id

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

           A_		    =					tokenizer(_snake_case						, return_tensors="""pt"""						, padding=_snake_case				)
           A_		    =					inputs["""input_ids"""].to(_snake_case				)

           A_		    =					model.generate(
               input_ids=_snake_case						, attention_mask=inputs["""attention_mask"""].to(_snake_case				)						, )

           A_		    =					tokenizer(sentences[0]						, return_tensors="""pt"""				).input_ids.to(_snake_case				)
           A_		    =					model.generate(input_ids=_snake_case				)

           A_		    =					inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
           A_		    =					tokenizer(sentences[1]						, return_tensors="""pt"""				).input_ids.to(_snake_case				)
           A_		    =					model.generate(input_ids=_snake_case						, max_length=model.config.max_length - num_paddings				)

           A_		    =					tokenizer.batch_decode(_snake_case						, skip_special_tokens=_snake_case				)
           A_		    =					tokenizer.decode(output_non_padded[0]						, skip_special_tokens=_snake_case				)
           A_		    =					tokenizer.decode(output_padded[0]						, skip_special_tokens=_snake_case				)

           A_		    =					[
               """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(_snake_case						, _snake_case				)
           self.assertListEqual(_snake_case						, [non_padded_sentence, padded_sentence]				)



    @slow
    def     snake_case_	(    self				)  ->					Any:





           '''simple docstring'''


           for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_		    =					BioGptModel.from_pretrained(_snake_case				)
                  self.assertIsNotNone(_snake_case				)



    def     snake_case_	(    self				)  ->					List[Any]:





           '''simple docstring'''
           A_    ,	A_		    =					self.model_tester.prepare_config_and_inputs_for_common()
           A_		    =					3
           A_		    =					input_dict["""input_ids"""]
           A_		    =					input_ids.ne(1				).to(_snake_case				)
           A_		    =					ids_tensor([self.model_tester.batch_size]						, self.model_tester.type_sequence_label_size				)
           A_		    =					BioGptForSequenceClassification(_snake_case				)
           model.to(_snake_case				)
           model.eval()
           A_		    =					model(_snake_case						, attention_mask=_snake_case						, labels=_snake_case				)
           self.assertEqual(result.logits.shape						, (self.model_tester.batch_size, self.model_tester.num_labels)				)







    def     snake_case_	(    self				)  ->					Tuple:





           '''simple docstring'''
           A_    ,	A_		    =					self.model_tester.prepare_config_and_inputs_for_common()
           A_		    =					3
           A_		    =					"""multi_label_classification"""
           A_		    =					input_dict["""input_ids"""]
           A_		    =					input_ids.ne(1				).to(_snake_case				)
           A_		    =					ids_tensor(
               [self.model_tester.batch_size, config.num_labels]						, self.model_tester.type_sequence_label_size				).to(torch.float				)
           A_		    =					BioGptForSequenceClassification(_snake_case				)
           model.to(_snake_case				)
           model.eval()
           A_		    =					model(_snake_case						, attention_mask=_snake_case						, labels=_snake_case				)
           self.assertEqual(result.logits.shape						, (self.model_tester.batch_size, self.model_tester.num_labels)				)


@require_torch
class     A__					(					unittest.TestCase	):



    @slow
    def     snake_case_	(    self				)  ->					int:





           '''simple docstring'''
           A_		    =					BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""				)
           A_		    =					torch.tensor([[2, 4805, 9, 656, 21]]				)
           A_		    =					model(_snake_case				)[0]

           A_		    =					42384

           A_		    =					torch.Size((1, 5, vocab_size)				)
           self.assertEqual(output.shape						, _snake_case				)

           A_		    =					torch.tensor(
               [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]				)

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







    @slow
    def     snake_case_	(    self				)  ->					int:





           '''simple docstring'''
           A_		    =					BioGptTokenizer.from_pretrained("""microsoft/biogpt"""				)
           A_		    =					BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""				)
           model.to(_snake_case				)

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

           A_		    =					(
               """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(_snake_case						, _snake_case				)






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_ : Union[str, Any]    		=       {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Dict    		=       ["GPTNeoXTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       [
        "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
        "GPTNeoXForCausalLM",
        "GPTNeoXForQuestionAnswering",
        "GPTNeoXForSequenceClassification",
        "GPTNeoXForTokenClassification",
        "GPTNeoXLayer",
        "GPTNeoXModel",
        "GPTNeoXPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neox import (
            GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
            GPTNeoXLayer,
            GPTNeoXModel,
            GPTNeoXPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Optional[int]    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






