


from typing import TYPE_CHECKING

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


__a	 =      {
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """MCTCTForCTC""",
     """MCTCTModel""",
     """MCTCTPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
 from .feature_extraction_mctct import MCTCTFeatureExtractor
 from .processing_mctct import MCTCTProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
 import sys

 __a	 =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





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    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=1_3    ,    __SCREAMING_SNAKE_CASE=7    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=9_9    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=5    ,    __SCREAMING_SNAKE_CASE=4    ,    __SCREAMING_SNAKE_CASE=3_7    ,    __SCREAMING_SNAKE_CASE="gelu"    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=5_1_2    ,    __SCREAMING_SNAKE_CASE=1_6    ,    __SCREAMING_SNAKE_CASE=2    ,    __SCREAMING_SNAKE_CASE=0.02    ,    __SCREAMING_SNAKE_CASE=3    ,    __SCREAMING_SNAKE_CASE=4    ,    __SCREAMING_SNAKE_CASE=None    ,    )	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					parent
           UpperCamelCase__     : str											=					batch_size
           UpperCamelCase__     : List[Any]											=					seq_length
           UpperCamelCase__     : List[Any]											=					is_training
           UpperCamelCase__     : Any											=					use_input_mask
           UpperCamelCase__     : Dict											=					use_token_type_ids
           UpperCamelCase__     : List[str]											=					use_labels
           UpperCamelCase__     : Optional[int]											=					vocab_size
           UpperCamelCase__     : Union[str, Any]											=					hidden_size
           UpperCamelCase__     : int											=					num_hidden_layers
           UpperCamelCase__     : Union[str, Any]											=					num_attention_heads
           UpperCamelCase__     : Union[str, Any]											=					intermediate_size
           UpperCamelCase__     : Optional[int]											=					hidden_act
           UpperCamelCase__     : Optional[Any]											=					hidden_dropout_prob
           UpperCamelCase__     : Optional[Any]											=					attention_probs_dropout_prob
           UpperCamelCase__     : Union[str, Any]											=					max_position_embeddings
           UpperCamelCase__     : Optional[Any]											=					type_vocab_size
           UpperCamelCase__     : int											=					type_sequence_label_size
           UpperCamelCase__     : Tuple											=					initializer_range
           UpperCamelCase__     : Optional[Any]											=					num_labels
           UpperCamelCase__     : List[str]											=					num_choices
           UpperCamelCase__     : Union[str, Any]											=					scope






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




           """simple docstring"""







           UpperCamelCase__     : Dict											=					ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size	)

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

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

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

           UpperCamelCase__     : Dict											=					self.get_config()

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






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




           """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=__SCREAMING_SNAKE_CASE    ,    initializer_range=self.initializer_range    ,    )






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




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					BioGptModel(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					model(__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    )	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptForCausalLM(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : Union[str, Any]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    token_type_ids=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.vocab_size)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : str											=					BioGptModel(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()

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

           # first forward pass
           UpperCamelCase__					,UpperCamelCase__     : Optional[int]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	).to_tuple()

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

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

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

           # get two different outputs
           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)['''last_hidden_state''']
           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    past_key_values=__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)['''last_hidden_state''']

           # select random slice
           UpperCamelCase__     : Optional[int]											=					ids_tensor((1,)    ,    output_from_past.shape[-1]	).item()
           UpperCamelCase__     : Tuple											=					output_from_no_past[:, -1, random_slice_idx].detach()
           UpperCamelCase__     : str											=					output_from_past[:, 0, random_slice_idx].detach()

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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					BioGptModel(config=__SCREAMING_SNAKE_CASE	).to(__SCREAMING_SNAKE_CASE	).eval()

           UpperCamelCase__     : Union[str, Any]											=					torch.ones(input_ids.shape    ,    dtype=torch.long    ,    device=__SCREAMING_SNAKE_CASE	)

           # first forward pass
           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    use_cache=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__					,UpperCamelCase__     : List[Any]											=					outputs.to_tuple()

           # create hypothetical multiple next token and extent to next_input_ids
           UpperCamelCase__     : List[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__     : Tuple											=					torch.cat([attention_mask, next_attn_mask]    ,    dim=-1	)

           UpperCamelCase__     : str											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE	)['''last_hidden_state''']
           UpperCamelCase__     : int											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    past_key_values=__SCREAMING_SNAKE_CASE	)[
               '''last_hidden_state'''
           ]

           # select random slice
           UpperCamelCase__     : Union[str, Any]											=					ids_tensor((1,)    ,    output_from_past.shape[-1]	).item()
           UpperCamelCase__     : Union[str, Any]											=					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(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-3	)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					BioGptForCausalLM(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           if gradient_checkpointing:
                 model.gradient_checkpointing_enable()

           UpperCamelCase__     : Tuple											=					model(__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : str											=					BioGptModel(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : 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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.num_labels
           UpperCamelCase__     : Any											=					BioGptForTokenClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    token_type_ids=__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.num_labels)	)






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




           """simple docstring"""







           UpperCamelCase__     : int											=					self.prepare_config_and_inputs()
           (
               (
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,
           )     : List[str]											=					config_and_inputs
           UpperCamelCase__     : Optional[Any]											=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict




@require_torch
class 					_lowerCamelCase			(   UpperCamelCase_						,				UpperCamelCase_						,				UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(
         (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		 =	(BioGptForCausalLM,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		 =	(
         {
             '''feature-extraction''': BioGptModel,
             '''text-classification''': BioGptForSequenceClassification,
             '''text-generation''': BioGptForCausalLM,
             '''token-classification''': BioGptForTokenClassification,
             '''zero-shot''': BioGptForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE_		 =	False






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptModelTester(self	)
           UpperCamelCase__     : Dict											=					ConfigTester(self    ,    config_class=__SCREAMING_SNAKE_CASE    ,    hidden_size=3_7	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           self.config_tester.run_common_tests()






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




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 UpperCamelCase__     : Optional[Any]											=					type
                 self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_forward_and_backwards(*__SCREAMING_SNAKE_CASE    ,    gradient_checkpointing=__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : str											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_weight_initialization(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : int											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_for_token_classification(*__SCREAMING_SNAKE_CASE	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''	)
           model.to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					BioGptTokenizer.from_pretrained('''microsoft/biogpt'''	)

           UpperCamelCase__     : Optional[Any]											=					'''left'''

           # Define PAD Token = EOS Token = 50256
           UpperCamelCase__     : Optional[int]											=					tokenizer.eos_token
           UpperCamelCase__     : List[str]											=					model.config.eos_token_id

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

           UpperCamelCase__     : Dict											=					tokenizer(__SCREAMING_SNAKE_CASE    ,    return_tensors='''pt'''    ,    padding=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					inputs['''input_ids'''].to(__SCREAMING_SNAKE_CASE	)

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

           UpperCamelCase__     : int											=					tokenizer(sentences[0]    ,    return_tensors='''pt'''	).input_ids.to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					model.generate(input_ids=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Tuple											=					inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
           UpperCamelCase__     : List[Any]											=					tokenizer(sentences[1]    ,    return_tensors='''pt'''	).input_ids.to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					model.generate(input_ids=__SCREAMING_SNAKE_CASE    ,    max_length=model.config.max_length - num_paddings	)

           UpperCamelCase__     : Any											=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					tokenizer.decode(output_non_padded[0]    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					tokenizer.decode(output_padded[0]    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Any											=					[
               '''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(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    [non_padded_sentence, padded_sentence]	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__     : Optional[int]											=					BioGptModel.from_pretrained(__SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Dict											=					self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__     : int											=					3
           UpperCamelCase__     : Tuple											=					input_dict['''input_ids''']
           UpperCamelCase__     : List[Any]											=					input_ids.ne(1	).to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					ids_tensor([self.model_tester.batch_size]    ,    self.model_tester.type_sequence_label_size	)
           UpperCamelCase__     : Optional[int]											=					BioGptForSequenceClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : int											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.assertEqual(result.logits.shape    ,    (self.model_tester.batch_size, self.model_tester.num_labels)	)






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




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : List[str]											=					self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase__     : List[str]											=					3
           UpperCamelCase__     : List[Any]											=					'''multi_label_classification'''
           UpperCamelCase__     : List[str]											=					input_dict['''input_ids''']
           UpperCamelCase__     : Tuple											=					input_ids.ne(1	).to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					ids_tensor(
               [self.model_tester.batch_size, config.num_labels]    ,    self.model_tester.type_sequence_label_size	).to(torch.float	)
           UpperCamelCase__     : List[str]											=					BioGptForSequenceClassification(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           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  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''	)
           UpperCamelCase__     : Union[str, Any]											=					torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]	)
           UpperCamelCase__     : Tuple											=					model(__SCREAMING_SNAKE_CASE	)[0]

           UpperCamelCase__     : Optional[Any]											=					4_2_3_8_4

           UpperCamelCase__     : List[Any]											=					torch.Size((1, 5, vocab_size)	)
           self.assertEqual(output.shape    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					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]    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-4	)	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BioGptTokenizer.from_pretrained('''microsoft/biogpt'''	)
           UpperCamelCase__     : List[Any]											=					BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''	)
           model.to(__SCREAMING_SNAKE_CASE	)

           torch.manual_seed(0	)
           UpperCamelCase__     : Dict											=					tokenizer('''COVID-19 is'''    ,    return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					model.generate(
               **__SCREAMING_SNAKE_CASE    ,    min_length=1_0_0    ,    max_length=1_0_2_4    ,    num_beams=5    ,    early_stopping=__SCREAMING_SNAKE_CASE    ,    )
           UpperCamelCase__     : int											=					tokenizer.decode(output_ids[0]    ,    skip_special_tokens=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Union[str, 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(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)


