



'''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			:  Any						,      __snake_case			:  int						,      __snake_case			:  List[str]=13						,      __snake_case			:  Optional[int]=7						,      __snake_case			:  Optional[int]=True						,      __snake_case			:  Optional[Any]=True						,      __snake_case			:  Any=False						,      __snake_case			:  Dict=True						,      __snake_case			:  Union[str, Any]=99						,      __snake_case			:  Optional[int]=32						,      __snake_case			:  Any=5						,      __snake_case			:  List[Any]=4						,      __snake_case			:  Optional[int]=37						,      __snake_case			:  str="gelu"						,      __snake_case			:  Any=0.1						,      __snake_case			:  List[str]=0.1						,      __snake_case			:  int=5_12						,      __snake_case			:  Union[str, Any]=16						,      __snake_case			:  int=2						,      __snake_case			:  Union[str, Any]=0.02						,      __snake_case			:  Optional[int]=3						,      __snake_case			:  Tuple=4						,      __snake_case			:  Any=None						,      ):
      a						: List[str]					       =					parent
      a						: int					       =					batch_size
      a						: Optional[Any]					       =					seq_length
      a						: Dict					       =					is_training
      a						: Tuple					       =					use_input_mask
      a						: Any					       =					use_token_type_ids
      a						: Optional[int]					       =					use_labels
      a						: Tuple					       =					vocab_size
      a						: Dict					       =					hidden_size
      a						: Optional[int]					       =					num_hidden_layers
      a						: Union[str, Any]					       =					num_attention_heads
      a						: int					       =					intermediate_size
      a						: Dict					       =					hidden_act
      a						: Tuple					       =					hidden_dropout_prob
      a						: List[Any]					       =					attention_probs_dropout_prob
      a						: List[str]					       =					max_position_embeddings
      a						: int					       =					type_vocab_size
      a						: Union[str, Any]					       =					type_sequence_label_size
      a						: str					       =					initializer_range
      a						: Optional[Any]					       =					num_labels
      a						: List[str]					       =					num_choices
      a						: Optional[Any]					       =					scope







    def       lowercase_	(				self			:  Any     ):
      a						: Optional[int]					       =					ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size     )

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

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

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

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

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







    def       lowercase_	(				self			:  Optional[int]     ):
      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       lowercase_	(				self			:  List[Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  List[Any]     ):
      a						: List[str]					       =					BioGptModel(config=__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: Any					       =					model(__snake_case						,      attention_mask=__snake_case     )
      a						: str					       =					model(__snake_case     )
      self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  List[str]						,      __snake_case			:  List[str]						,      __snake_case			:  int						,      __snake_case			:  Optional[int]						,      __snake_case			:  Any						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  str						,      __snake_case			:  Optional[int]						,      ):
      a						: Tuple					       =					BioGptForCausalLM(config=__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: Optional[Any]					       =					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       lowercase_	(				self			:  Dict						,      __snake_case			:  List[str]						,      __snake_case			:  int						,      __snake_case			:  List[str]						,      __snake_case			:  List[str]						,      __snake_case			:  Optional[Any]						,      *__snake_case			:  str     ):
      a						: str					       =					BioGptModel(config=__snake_case     )
      model.to(__snake_case     )
      model.eval()

      # create attention mask
      a						: Dict					       =					torch.ones(input_ids.shape						,      dtype=torch.long						,      device=__snake_case     )
      a						: Union[str, Any]					       =					self.seq_length // 2
      a						: int					       =					0

      # first forward pass
      a       ,     a						: List[Any]					       =					model(__snake_case						,      attention_mask=__snake_case     ).to_tuple()

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

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

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

      # get two different outputs
      a						: List[str]					       =					model(__snake_case						,      attention_mask=__snake_case     )['last_hidden_state']
      a						: str					       =					model(__snake_case						,      past_key_values=__snake_case						,      attention_mask=__snake_case     )['last_hidden_state']

      # select random slice
      a						: Union[str, Any]					       =					ids_tensor((1,)						,      output_from_past.shape[-1]     ).item()
      a						: Union[str, Any]					       =					output_from_no_past[:, -1, random_slice_idx].detach()
      a						: int					       =					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       lowercase_	(				self			:  int						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Dict						,      __snake_case			:  Optional[int]						,      __snake_case			:  str						,      __snake_case			:  Any						,      *__snake_case			:  Tuple     ):
      a						: Tuple					       =					BioGptModel(config=__snake_case     ).to(__snake_case     ).eval()

      a						: Optional[int]					       =					torch.ones(input_ids.shape						,      dtype=torch.long						,      device=__snake_case     )

      # first forward pass
      a						: int					       =					model(__snake_case						,      attention_mask=__snake_case						,      use_cache=__snake_case     )

      a       ,     a						: Dict					       =					outputs.to_tuple()

      # create hypothetical multiple next token and extent to next_input_ids
      a						: Optional[Any]					       =					ids_tensor((self.batch_size, 3)						,      config.vocab_size     )
      a						: List[Any]					       =					ids_tensor((self.batch_size, 3)						,      2     )

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

      a						: List[str]					       =					model(__snake_case						,      attention_mask=__snake_case     )['last_hidden_state']
      a						: Optional[int]					       =					model(__snake_case						,      attention_mask=__snake_case						,      past_key_values=__snake_case     )[
          'last_hidden_state'
      ]

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







    def       lowercase_	(				self			:  Any						,      __snake_case			:  Optional[int]						,      __snake_case			:  Optional[int]						,      __snake_case			:  List[str]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Dict						,      *__snake_case			:  Any						,      __snake_case			:  Optional[int]=False     ):
      a						: int					       =					BioGptForCausalLM(__snake_case     )
      model.to(__snake_case     )
      if gradient_checkpointing:
        model.gradient_checkpointing_enable()

      a						: str					       =					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       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Dict						,      *__snake_case			:  Optional[int]     ):
      a						: Union[str, Any]					       =					BioGptModel(__snake_case     )
      a						: Dict					       =					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       lowercase_	(				self			:  Dict						,      __snake_case			:  Optional[Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  int						,      __snake_case			:  Dict						,      __snake_case			:  Any						,      *__snake_case			:  Tuple     ):
      a						: List[str]					       =					self.num_labels
      a						: Union[str, Any]					       =					BioGptForTokenClassification(__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: Optional[int]					       =					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       lowercase_	(				self			:  Tuple     ):
      a						: List[Any]					       =					self.prepare_config_and_inputs()
      (
          (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     (
          a
      )       ,     
      )						: Optional[int]					       =					config_and_inputs
      a						: List[str]					       =					{'input_ids': input_ids, 'attention_mask': input_mask}
      return config, inputs_dict

@require_torch
class 							a__( lowerCamelCase__		,			lowerCamelCase__		,			lowerCamelCase__		,			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       lowercase_	(				self			:  Optional[Any]     ):
      a						: str					       =					BioGptModelTester(self     )
      a						: Tuple					       =					ConfigTester(self						,      config_class=__snake_case						,      hidden_size=37     )







    def       lowercase_	(				self			:  str     ):
      self.config_tester.run_common_tests()







    def       lowercase_	(				self			:  Tuple     ):
      a						: List[Any]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case     )







    def       lowercase_	(				self			:  int     ):
      a						: Tuple					       =					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        a						: int					       =					type
        self.model_tester.create_and_check_model(*__snake_case     )







    def       lowercase_	(				self			:  Any     ):
      a						: List[str]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__snake_case     )







    def       lowercase_	(				self			:  str     ):
      a						: str					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_forward_and_backwards(*__snake_case						,      gradient_checkpointing=__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Tuple					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__snake_case     )







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Any					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_weight_initialization(*__snake_case     )







    def       lowercase_	(				self			:  List[str]     ):
      a						: List[Any]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_for_token_classification(*__snake_case     )







    @slow
    def       lowercase_	(				self			:  List[str]     ):
      a						: Dict					       =					BioGptForCausalLM.from_pretrained('microsoft/biogpt'     )
      model.to(__snake_case     )
      a						: Tuple					       =					BioGptTokenizer.from_pretrained('microsoft/biogpt'     )

      a						: Dict					       =					'left'

      # Define PAD Token = EOS Token = 50256
      a						: Dict					       =					tokenizer.eos_token
      a						: int					       =					model.config.eos_token_id

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

      a						: str					       =					tokenizer(__snake_case						,      return_tensors='pt'						,      padding=__snake_case     )
      a						: Union[str, Any]					       =					inputs['input_ids'].to(__snake_case     )

      a						: int					       =					model.generate(
          input_ids=__snake_case						,      attention_mask=inputs['attention_mask'].to(__snake_case     )						,      )

      a						: List[Any]					       =					tokenizer(sentences[0]						,      return_tensors='pt'     ).input_ids.to(__snake_case     )
      a						: Optional[Any]					       =					model.generate(input_ids=__snake_case     )

      a						: Optional[int]					       =					inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
      a						: List[str]					       =					tokenizer(sentences[1]						,      return_tensors='pt'     ).input_ids.to(__snake_case     )
      a						: Union[str, Any]					       =					model.generate(input_ids=__snake_case						,      max_length=model.config.max_length - num_paddings     )

      a						: Tuple					       =					tokenizer.batch_decode(__snake_case						,      skip_special_tokens=__snake_case     )
      a						: Optional[Any]					       =					tokenizer.decode(output_non_padded[0]						,      skip_special_tokens=__snake_case     )
      a						: Optional[int]					       =					tokenizer.decode(output_padded[0]						,      skip_special_tokens=__snake_case     )

      a						: List[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(__snake_case						,      __snake_case     )
      self.assertListEqual(__snake_case						,      [non_padded_sentence, padded_sentence]     )







    @slow
    def       lowercase_	(				self			:  Union[str, Any]     ):
      for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						: Optional[Any]					       =					BioGptModel.from_pretrained(__snake_case     )
        self.assertIsNotNone(__snake_case     )







    def       lowercase_	(				self			:  Dict     ):
      a       ,     a						: List[str]					       =					self.model_tester.prepare_config_and_inputs_for_common()
      a						: int					       =					3
      a						: List[str]					       =					input_dict['input_ids']
      a						: Dict					       =					input_ids.ne(1     ).to(__snake_case     )
      a						: List[str]					       =					ids_tensor([self.model_tester.batch_size]						,      self.model_tester.type_sequence_label_size     )
      a						: List[Any]					       =					BioGptForSequenceClassification(__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: List[str]					       =					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       lowercase_	(				self			:  Tuple     ):
      a       ,     a						: List[Any]					       =					self.model_tester.prepare_config_and_inputs_for_common()
      a						: int					       =					3
      a						: str					       =					'multi_label_classification'
      a						: Tuple					       =					input_dict['input_ids']
      a						: Any					       =					input_ids.ne(1     ).to(__snake_case     )
      a						: Optional[Any]					       =					ids_tensor(
          [self.model_tester.batch_size, config.num_labels]						,      self.model_tester.type_sequence_label_size     ).to(torch.float     )
      a						: Dict					       =					BioGptForSequenceClassification(__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: Dict					       =					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       lowercase_	(				self			:  Dict     ):
      a						: int					       =					BioGptForCausalLM.from_pretrained('microsoft/biogpt'     )
      a						: Optional[int]					       =					torch.tensor([[2, 48_05, 9, 6_56, 21]]     )
      a						: Tuple					       =					model(__snake_case     )[0]

      a						: int					       =					4_23_84

      a						: Union[str, Any]					       =					torch.Size((1, 5, vocab_size)     )
      self.assertEqual(output.shape						,      __snake_case     )

      a						: str					       =					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       lowercase_	(				self			:  Union[str, Any]     ):
      a						: Union[str, Any]					       =					BioGptTokenizer.from_pretrained('microsoft/biogpt'     )
      a						: Union[str, Any]					       =					BioGptForCausalLM.from_pretrained('microsoft/biogpt'     )
      model.to(__snake_case     )

      torch.manual_seed(0     )
      a						: List[str]					       =					tokenizer('COVID-19 is'						,      return_tensors='pt'     ).to(__snake_case     )
      a						: Optional[Any]					       =					model.generate(
          **__snake_case						,      min_length=1_00						,      max_length=10_24						,      num_beams=5						,      early_stopping=__snake_case						,      )
      a						: Dict					       =					tokenizer.decode(output_ids[0]						,      skip_special_tokens=__snake_case     )

      a						: Optional[Any]					       =					(
          '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     )




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase: Any							=						{
    'configuration_poolformer': [
        'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'PoolFormerConfig',
        'PoolFormerOnnxConfig',
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: List[str]							=						['PoolFormerFeatureExtractor']
     lowerCAmelCase: Tuple							=						['PoolFormerImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: str							=						[
         'POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'PoolFormerForImageClassification',
         'PoolFormerModel',
         'PoolFormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_poolformer import (
         POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         PoolFormerConfig,
         PoolFormerOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_poolformer import PoolFormerFeatureExtractor
          from .image_processing_poolformer import PoolFormerImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_poolformer import (
              POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              PoolFormerForImageClassification,
              PoolFormerModel,
              PoolFormerPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase: Dict							=						_LazyModule(__name__, globals()['__file__'], _import_structure)