
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:

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=13	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=99	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=37	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=5_12	,      SCREAMING_SNAKE_CASE_=16	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=0.02	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    seq_length
       UpperCamelCase__				=    is_training
       UpperCamelCase__				=    use_input_mask
       UpperCamelCase__				=    use_token_type_ids
       UpperCamelCase__				=    use_labels
       UpperCamelCase__				=    vocab_size
       UpperCamelCase__				=    hidden_size
       UpperCamelCase__				=    num_hidden_layers
       UpperCamelCase__				=    num_attention_heads
       UpperCamelCase__				=    intermediate_size
       UpperCamelCase__				=    hidden_act
       UpperCamelCase__				=    hidden_dropout_prob
       UpperCamelCase__				=    attention_probs_dropout_prob
       UpperCamelCase__				=    max_position_embeddings
       UpperCamelCase__				=    type_vocab_size
       UpperCamelCase__				=    type_sequence_label_size
       UpperCamelCase__				=    initializer_range
       UpperCamelCase__				=    num_labels
       UpperCamelCase__				=    num_choices
       UpperCamelCase__				=    scope







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

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

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

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

       UpperCamelCase__				=    self.get_config()

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







  def 	UpperCAmelCase_ (self						):
       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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    BioGptModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)







  def 	UpperCAmelCase_ (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_	,      ):
       UpperCamelCase__				=    BioGptForCausalLM(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    BioGptModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()

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

       # first forward pass
       UpperCamelCase__		,	UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						).to_tuple()

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

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

       # append to next input_ids and attn_mask
       UpperCamelCase__				=    torch.cat([input_ids, next_tokens]	,      dim=-1						)
       UpperCamelCase__				=    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__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						)["""last_hidden_state"""]
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      past_key_values=SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						)["""last_hidden_state"""]

       # select random slice
       UpperCamelCase__				=    ids_tensor((1,)	,      output_from_past.shape[-1]						).item()
       UpperCamelCase__				=    output_from_no_past[:, -1, random_slice_idx].detach()
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    BioGptModel(config=SCREAMING_SNAKE_CASE_						).to(SCREAMING_SNAKE_CASE_						).eval()

       UpperCamelCase__				=    torch.ones(input_ids.shape	,      dtype=torch.long	,      device=SCREAMING_SNAKE_CASE_						)

       # first forward pass
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      use_cache=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__		,	UpperCamelCase__				=    outputs.to_tuple()

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

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

       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_						)["""last_hidden_state"""]
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      attention_mask=SCREAMING_SNAKE_CASE_	,      past_key_values=SCREAMING_SNAKE_CASE_						)[
           """last_hidden_state"""
       ]

       # select random slice
       UpperCamelCase__				=    ids_tensor((1,)	,      output_from_past.shape[-1]						).item()
       UpperCamelCase__				=    output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False						):
       UpperCamelCase__				=    BioGptForCausalLM(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       if gradient_checkpointing:
            model.gradient_checkpointing_enable()

       UpperCamelCase__				=    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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    BioGptModel(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.num_labels
       UpperCamelCase__				=    BioGptForTokenClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	(
           UpperCamelCase__
       )		,	
       )				=    config_and_inputs
       UpperCamelCase__				=    {"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict






@require_torch
class      __A(    __lowerCamelCase    ,				__lowerCamelCase    ,				__lowerCamelCase    ,				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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    BioGptModelTester(self						)
       UpperCamelCase__				=    ConfigTester(self	,      config_class=SCREAMING_SNAKE_CASE_	,      hidden_size=37						)







  def 	UpperCAmelCase_ (self						):
       self.config_tester.run_common_tests()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            UpperCamelCase__				=    type
            self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE_						)







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
       model.to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    BioGptTokenizer.from_pretrained("""microsoft/biogpt"""						)

       UpperCamelCase__				=    """left"""

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

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

       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      padding=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    model.generate(
           input_ids=SCREAMING_SNAKE_CASE_	,      attention_mask=inputs["""attention_mask"""].to(SCREAMING_SNAKE_CASE_						)	,      )

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

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

       UpperCamelCase__				=    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(output_non_padded[0]	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(output_padded[0]	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    [
           """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 	UpperCAmelCase_ (self						):
       for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__				=    BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE_						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase__				=    3
       UpperCamelCase__				=    input_dict["""input_ids"""]
       UpperCamelCase__				=    input_ids.ne(1						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    ids_tensor([self.model_tester.batch_size]	,      self.model_tester.type_sequence_label_size						)
       UpperCamelCase__				=    BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase__				=    3
       UpperCamelCase__				=    """multi_label_classification"""
       UpperCamelCase__				=    input_dict["""input_ids"""]
       UpperCamelCase__				=    input_ids.ne(1						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    ids_tensor(
           [self.model_tester.batch_size, config.num_labels]	,      self.model_tester.type_sequence_label_size						).to(torch.float						)
       UpperCamelCase__				=    BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    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      __A(    unittest.TestCase			):

  """simple docstring"""







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
       UpperCamelCase__				=    torch.tensor([[2, 48_05, 9, 6_56, 21]]						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)[0]

       UpperCamelCase__				=    4_23_84

       UpperCamelCase__				=    torch.Size((1, 5, vocab_size)						)
       self.assertEqual(output.shape	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    BioGptTokenizer.from_pretrained("""microsoft/biogpt"""						)
       UpperCamelCase__				=    BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""						)
       model.to(SCREAMING_SNAKE_CASE_						)

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

       UpperCamelCase__				=    (
           """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_						)







def 						__magic_name__			(   __a      :	int	,		__a      :	int						):



     '''simple docstring'''




     while a != 0:
          UpperCamelCase__		,	UpperCamelCase__				=    b % a, a
     return b






def 						__magic_name__			(   __a      :	int	,		__a      :	int						):



     '''simple docstring'''




     if gcd(__a	,		__a						) != 1:
          UpperCamelCase__				=    f"mod inverse of {a!r} and {m!r} does not exist"
          raise ValueError(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    1, 0, a
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    0, 1, m
     while va != 0:
          UpperCamelCase__				=    ua // va
          UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
     return ua % m





