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 __snake_case       :




   def __init__(					self    :       Optional[int] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       str=13 ,			_lowercase    :       Tuple=7 ,			_lowercase    :       List[str]=True ,			_lowercase    :       str=True ,			_lowercase    :       Tuple=False ,			_lowercase    :       Optional[Any]=True ,			_lowercase    :       Tuple=99 ,			_lowercase    :       int=32 ,			_lowercase    :       int=5 ,			_lowercase    :       Union[str, Any]=4 ,			_lowercase    :       Optional[Any]=37 ,			_lowercase    :       Optional[int]="gelu" ,			_lowercase    :       int=0.1 ,			_lowercase    :       Dict=0.1 ,			_lowercase    :       Union[str, Any]=5_12 ,			_lowercase    :       List[str]=16 ,			_lowercase    :       Any=2 ,			_lowercase    :       Optional[Any]=0.02 ,			_lowercase    :       Union[str, Any]=3 ,			_lowercase    :       Optional[Any]=4 ,			_lowercase    :       Tuple=None ,			):



     """simple docstring"""






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




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size   )

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

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

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

     SCREAMING_SNAKE_CASE__    							=   self.get_config()

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




   def 			__a		(					self    :       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=_lowercase ,			initializer_range=self.initializer_range ,			)




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Any ,			_lowercase    :       List[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptModel(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase   )
     self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)   )




   def 			__a		(					self    :       Tuple ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Tuple ,			_lowercase    :       Tuple ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       str ,			_lowercase    :       Tuple ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[int] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptForCausalLM(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase ,			labels=_lowercase   )
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str ,			_lowercase    :       Tuple ,			_lowercase    :       str ,			_lowercase    :       List[Any] ,			*_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptModel(config=_lowercase   )
     model.to(_lowercase   )
     model.eval()

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

     # first forward pass
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase   ).to_tuple()

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

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

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

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

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

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




   def 			__a		(					self    :       Dict ,			_lowercase    :       Optional[int] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict ,			_lowercase    :       List[Any] ,			*_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptModel(config=_lowercase   ).to(_lowercase   ).eval()

     SCREAMING_SNAKE_CASE__    							=   torch.ones(input_ids.shape ,			dtype=torch.long ,			device=_lowercase   )

     # first forward pass
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase ,			use_cache=_lowercase   )

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase   )["""last_hidden_state"""]
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase ,			past_key_values=_lowercase   )[
         """last_hidden_state"""
     ]

     # select random slice
     SCREAMING_SNAKE_CASE__    							=   ids_tensor((1,) ,			output_from_past.shape[-1]   ).item()
     SCREAMING_SNAKE_CASE__    							=   output_from_no_past[:, -3:, random_slice_idx].detach()
     SCREAMING_SNAKE_CASE__    							=   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(_lowercase ,			_lowercase ,			atol=1E-3   )   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       str ,			_lowercase    :       List[str] ,			_lowercase    :       Any ,			_lowercase    :       str ,			_lowercase    :       str ,			*_lowercase    :       Dict ,			_lowercase    :       Tuple=False   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptForCausalLM(_lowercase   )
     model.to(_lowercase   )
     if gradient_checkpointing:
       model.gradient_checkpointing_enable()

     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			labels=_lowercase   )
     self.parent.assertEqual(result.loss.shape ,			()   )
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)   )
     result.loss.backward()




   def 			__a		(					self    :       Tuple ,			_lowercase    :       Tuple ,			*_lowercase    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptModel(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   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   ) ,			0.0_01   )
         self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]   ) - 0.0   ) ,			0.01   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       str ,			_lowercase    :       int ,			_lowercase    :       int ,			_lowercase    :       List[Any] ,			_lowercase    :       str ,			*_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.num_labels
     SCREAMING_SNAKE_CASE__    							=   BioGptForTokenClassification(_lowercase   )
     model.to(_lowercase   )
     model.eval()
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase ,			attention_mask=_lowercase ,			token_type_ids=_lowercase   )
     self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  (
         SCREAMING_SNAKE_CASE__
     )							,  
     )    							=   config_and_inputs
     SCREAMING_SNAKE_CASE__    							=   {"""input_ids""": input_ids, """attention_mask""": input_mask}
     return config, inputs_dict







@require_torch
class __snake_case       (		lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							(
       (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
       if is_torch_available()
       else ()
   )
   lowerCAmelCase_														=							(BioGptForCausalLM,) if is_torch_available() else ()
   lowerCAmelCase_														=							(
       {
           "feature-extraction": BioGptModel,
           "text-classification": BioGptForSequenceClassification,
           "text-generation": BioGptForCausalLM,
           "token-classification": BioGptForTokenClassification,
           "zero-shot": BioGptForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCAmelCase_														=							False




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptModelTester(self   )
     SCREAMING_SNAKE_CASE__    							=   ConfigTester(self ,			config_class=_lowercase ,			hidden_size=37   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowercase   )




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
       SCREAMING_SNAKE_CASE__    							=   type
       self.model_tester.create_and_check_model(*_lowercase   )




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_lowercase   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_forward_and_backwards(*_lowercase ,			gradient_checkpointing=_lowercase   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_lowercase   )




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_weight_initialization(*_lowercase   )




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_for_token_classification(*_lowercase   )




   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""   )
     model.to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   BioGptTokenizer.from_pretrained("""microsoft/biogpt"""   )

     SCREAMING_SNAKE_CASE__    							=   """left"""

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

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

     SCREAMING_SNAKE_CASE__    							=   tokenizer(_lowercase ,			return_tensors="""pt""" ,			padding=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   inputs["""input_ids"""].to(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   model.generate(
         input_ids=_lowercase ,			attention_mask=inputs["""attention_mask"""].to(_lowercase   ) ,			)

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

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

     SCREAMING_SNAKE_CASE__    							=   tokenizer.batch_decode(_lowercase ,			skip_special_tokens=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   tokenizer.decode(output_non_padded[0] ,			skip_special_tokens=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   tokenizer.decode(output_padded[0] ,			skip_special_tokens=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   [
         """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(_lowercase ,			_lowercase   )
     self.assertListEqual(_lowercase ,			[non_padded_sentence, padded_sentence]   )




   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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







@require_torch
class __snake_case       (		unittest.TestCase	):




   @slow
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""   )
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([[2, 48_05, 9, 6_56, 21]]   )
     SCREAMING_SNAKE_CASE__    							=   model(_lowercase   )[0]

     SCREAMING_SNAKE_CASE__    							=   4_23_84

     SCREAMING_SNAKE_CASE__    							=   torch.Size((1, 5, vocab_size)   )
     self.assertEqual(output.shape ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor(
         [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]   )

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




   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BioGptTokenizer.from_pretrained("""microsoft/biogpt"""   )
     SCREAMING_SNAKE_CASE__    							=   BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""   )
     model.to(_lowercase   )

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__    							=   tokenizer("""COVID-19 is""" ,			return_tensors="""pt"""   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   model.generate(
         **_lowercase ,			min_length=1_00 ,			max_length=10_24 ,			num_beams=5 ,			early_stopping=_lowercase ,			)
     SCREAMING_SNAKE_CASE__    							=   tokenizer.decode(output_ids[0] ,			skip_special_tokens=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   (
         """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(_lowercase ,			_lowercase   )

from __future__ import annotations



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list			)							->      float:







  """simple docstring"""





  if not nums:
    raise ValueError("""List is empty"""			)
  return sum(__UpperCamelCase			) / len(__UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
