
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 		UpperCamelCase__  :
			def __init__(self			: str ,    snake_case_			: Tuple ,    snake_case_			: Tuple=1_3 ,    snake_case_			: Any=7 ,    snake_case_			: Dict=True ,    snake_case_			: str=True ,    snake_case_			: Any=False ,    snake_case_			: int=True ,    snake_case_			: Optional[int]=9_9 ,    snake_case_			: str=3_2 ,    snake_case_			: Optional[int]=5 ,    snake_case_			: Optional[Any]=4 ,    snake_case_			: Optional[int]=3_7 ,    snake_case_			: Dict="gelu" ,    snake_case_			: Tuple=0.1 ,    snake_case_			: List[str]=0.1 ,    snake_case_			: List[str]=5_1_2 ,    snake_case_			: Union[str, Any]=1_6 ,    snake_case_			: Dict=2 ,    snake_case_			: Tuple=0.02 ,    snake_case_			: List[str]=3 ,    snake_case_			: Union[str, Any]=4 ,    snake_case_			: Any=None ,    ):
				__a			:  Dict		    =  parent
				__a			:  Tuple		    =  batch_size
				__a			:  Optional[int]		    =  seq_length
				__a			:  Any		    =  is_training
				__a			:  List[Any]		    =  use_input_mask
				__a			:  Optional[Any]		    =  use_token_type_ids
				__a			:  List[str]		    =  use_labels
				__a			:  int		    =  vocab_size
				__a			:  Tuple		    =  hidden_size
				__a			:  int		    =  num_hidden_layers
				__a			:  Optional[int]		    =  num_attention_heads
				__a			:  str		    =  intermediate_size
				__a			:  List[Any]		    =  hidden_act
				__a			:  List[Any]		    =  hidden_dropout_prob
				__a			:  int		    =  attention_probs_dropout_prob
				__a			:  List[str]		    =  max_position_embeddings
				__a			:  Dict		    =  type_vocab_size
				__a			:  Tuple		    =  type_sequence_label_size
				__a			:  str		    =  initializer_range
				__a			:  int		    =  num_labels
				__a			:  Any		    =  num_choices
				__a			:  Union[str, Any]		    =  scope
			def 				lowerCAmelCase      (self			: str       ):
				__a			:  int		    =  ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size       )

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

				__a			:  int		    =  None
				if self.use_token_type_ids:
					__a			:  List[Any]		    =  ids_tensor([self.batch_size, self.seq_length] ,    self.type_vocab_size       )

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

				__a			:  Tuple		    =  self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 				lowerCAmelCase      (self			: Any       ):
				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 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: str ,    snake_case_			: Optional[Any] ,    snake_case_			: Union[str, Any] ,    snake_case_			: List[str] ,    snake_case_			: Optional[int] ,    snake_case_			: Any ,    snake_case_			: List[str]       ):
				__a			:  Union[str, Any]		    =  BioGptModel(config=SCREAMING_SNAKE_CASE_       )
				model.to(SCREAMING_SNAKE_CASE_       )
				model.eval()
				__a			:  Dict		    =  model(SCREAMING_SNAKE_CASE_ ,    attention_mask=SCREAMING_SNAKE_CASE_       )
				__a			:  int		    =  model(SCREAMING_SNAKE_CASE_       )
				self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, self.seq_length, self.hidden_size)       )
			def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: Optional[Any] ,    snake_case_			: List[str] ,    snake_case_			: Tuple ,    snake_case_			: int ,    snake_case_			: int ,    snake_case_			: int ,    snake_case_			: Optional[int] ,    snake_case_			: List[Any] ,    snake_case_			: Optional[int] ,    ):
				__a			:  List[str]		    =  BioGptForCausalLM(config=SCREAMING_SNAKE_CASE_       )
				model.to(SCREAMING_SNAKE_CASE_       )
				model.eval()
				__a			:  List[str]		    =  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 				lowerCAmelCase      (self			: Any ,    snake_case_			: Any ,    snake_case_			: str ,    snake_case_			: int ,    snake_case_			: Optional[Any] ,    snake_case_			: List[Any] ,    *snake_case_			: Tuple       ):
				__a			:  Optional[int]		    =  BioGptModel(config=SCREAMING_SNAKE_CASE_       )
				model.to(SCREAMING_SNAKE_CASE_       )
				model.eval()

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

				# first forward pass
				__a			:  Any		    =  model(SCREAMING_SNAKE_CASE_ ,    attention_mask=SCREAMING_SNAKE_CASE_       ).to_tuple()

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

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

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

				# get two different outputs
				__a			:  List[Any]		    =  model(SCREAMING_SNAKE_CASE_ ,    attention_mask=SCREAMING_SNAKE_CASE_       )["""last_hidden_state"""]
				__a			:  List[str]		    =  model(SCREAMING_SNAKE_CASE_ ,    past_key_values=SCREAMING_SNAKE_CASE_ ,    attention_mask=SCREAMING_SNAKE_CASE_       )["""last_hidden_state"""]

				# select random slice
				__a			:  List[Any]		    =  ids_tensor((1,) ,    output_from_past.shape[-1]       ).item()
				__a			:  Tuple		    =  output_from_no_past[:, -1, random_slice_idx].detach()
				__a			:  Optional[int]		    =  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 				lowerCAmelCase      (self			: Any ,    snake_case_			: Tuple ,    snake_case_			: List[Any] ,    snake_case_			: List[Any] ,    snake_case_			: Dict ,    snake_case_			: List[Any] ,    *snake_case_			: int       ):
				__a			:  List[Any]		    =  BioGptModel(config=SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       ).eval()

				__a			:  str		    =  torch.ones(input_ids.shape ,    dtype=torch.long ,    device=SCREAMING_SNAKE_CASE_       )

				# first forward pass
				__a			:  str		    =  model(SCREAMING_SNAKE_CASE_ ,    attention_mask=SCREAMING_SNAKE_CASE_ ,    use_cache=SCREAMING_SNAKE_CASE_       )

				__a			:  Optional[int]		    =  outputs.to_tuple()

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

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

				__a			:  Any		    =  model(SCREAMING_SNAKE_CASE_ ,    attention_mask=SCREAMING_SNAKE_CASE_       )["""last_hidden_state"""]
				__a			:  Any		    =  model(SCREAMING_SNAKE_CASE_ ,    attention_mask=SCREAMING_SNAKE_CASE_ ,    past_key_values=SCREAMING_SNAKE_CASE_       )[
				    """last_hidden_state"""
				]

				# select random slice
				__a			:  Tuple		    =  ids_tensor((1,) ,    output_from_past.shape[-1]       ).item()
				__a			:  Union[str, Any]		    =  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(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ,    atol=1E-3       )       )
			def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: Union[str, Any] ,    snake_case_			: int ,    snake_case_			: Union[str, Any] ,    snake_case_			: Any ,    snake_case_			: Optional[int] ,    *snake_case_			: List[str] ,    snake_case_			: Dict=False       ):
				__a			:  Optional[int]		    =  BioGptForCausalLM(SCREAMING_SNAKE_CASE_       )
				model.to(SCREAMING_SNAKE_CASE_       )
				if gradient_checkpointing:
					model.gradient_checkpointing_enable()

				__a			:  List[str]		    =  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 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: Tuple ,    *snake_case_			: Union[str, Any]       ):
				__a			:  Union[str, Any]		    =  BioGptModel(SCREAMING_SNAKE_CASE_       )
				__a			:  str		    =  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 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: Union[str, Any] ,    snake_case_			: str ,    snake_case_			: List[Any] ,    snake_case_			: int ,    snake_case_			: List[str] ,    *snake_case_			: Union[str, Any]       ):
				__a			:  Union[str, Any]		    =  self.num_labels
				__a			:  Optional[int]		    =  BioGptForTokenClassification(SCREAMING_SNAKE_CASE_       )
				model.to(SCREAMING_SNAKE_CASE_       )
				model.eval()
				__a			:  Tuple		    =  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 				lowerCAmelCase      (self			: Tuple       ):
				__a			:  List[str]		    =  self.prepare_config_and_inputs()
				(
				    __a							
				)			:  Dict		    =  config_and_inputs
				__a			:  List[str]		    =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
				return config, inputs_dict





@require_torch
class 		UpperCamelCase__  (	_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase   ,unittest.TestCase   ):
			_SCREAMING_SNAKE_CASE				:							Optional[int]     	=				(
			    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
			    if is_torch_available()
			    else ()
			)
			_SCREAMING_SNAKE_CASE				:							str     	=				(BioGptForCausalLM,) if is_torch_available() else ()
			_SCREAMING_SNAKE_CASE				:							Optional[int]     	=				(
			    {
			        'feature-extraction': BioGptModel,
			        'text-classification': BioGptForSequenceClassification,
			        'text-generation': BioGptForCausalLM,
			        'token-classification': BioGptForTokenClassification,
			        'zero-shot': BioGptForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			_SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				False
			def 				lowerCAmelCase      (self			: int       ):
				__a			:  int		    =  BioGptModelTester(self       )
				__a			:  Dict		    =  ConfigTester(self ,    config_class=SCREAMING_SNAKE_CASE_ ,    hidden_size=3_7       )
			def 				lowerCAmelCase      (self			: Union[str, Any]       ):
				self.config_tester.run_common_tests()
			def 				lowerCAmelCase      (self			: Dict       ):
				__a			:  Union[str, Any]		    =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )
			def 				lowerCAmelCase      (self			: int       ):
				__a			:  List[Any]		    =  self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					__a			:  Optional[Any]		    =  type
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )
			def 				lowerCAmelCase      (self			: Optional[Any]       ):
				__a			:  Tuple		    =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE_       )
			def 				lowerCAmelCase      (self			: Tuple       ):
				__a			:  List[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 				lowerCAmelCase      (self			: Optional[Any]       ):
				__a			:  Any		    =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE_       )
			def 				lowerCAmelCase      (self			: str       ):
				__a			:  List[Any]		    =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE_       )
			def 				lowerCAmelCase      (self			: int       ):
				__a			:  Union[str, Any]		    =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE_       )
			@slow
			def 				lowerCAmelCase      (self			: str       ):
				__a			:  List[str]		    =  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''       )
				model.to(SCREAMING_SNAKE_CASE_       )
				__a			:  List[str]		    =  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''       )

				__a			:  Union[str, Any]		    =  """left"""

				# Define PAD Token = EOS Token = 50256
				__a			:  List[Any]		    =  tokenizer.eos_token
				__a			:  Tuple		    =  model.config.eos_token_id

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

				__a			:  Optional[int]		    =  tokenizer(SCREAMING_SNAKE_CASE_ ,    return_tensors='''pt''' ,    padding=SCREAMING_SNAKE_CASE_       )
				__a			:  Optional[int]		    =  inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_       )

				__a			:  str		    =  model.generate(
				    input_ids=SCREAMING_SNAKE_CASE_ ,    attention_mask=inputs['''attention_mask'''].to(SCREAMING_SNAKE_CASE_       ) ,    )

				__a			:  int		    =  tokenizer(sentences[0] ,    return_tensors='''pt'''       ).input_ids.to(SCREAMING_SNAKE_CASE_       )
				__a			:  Union[str, Any]		    =  model.generate(input_ids=SCREAMING_SNAKE_CASE_       )

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

				__a			:  Optional[int]		    =  tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ ,    skip_special_tokens=SCREAMING_SNAKE_CASE_       )
				__a			:  str		    =  tokenizer.decode(output_non_padded[0] ,    skip_special_tokens=SCREAMING_SNAKE_CASE_       )
				__a			:  Optional[Any]		    =  tokenizer.decode(output_padded[0] ,    skip_special_tokens=SCREAMING_SNAKE_CASE_       )

				__a			:  Optional[int]		    =  [
				    """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 				lowerCAmelCase      (self			: Union[str, Any]       ):
				for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					__a			:  str		    =  BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )
			def 				lowerCAmelCase      (self			: Tuple       ):
				__a			:  List[str]		    =  self.model_tester.prepare_config_and_inputs_for_common()
				__a			:  Tuple		    =  3
				__a			:  Optional[int]		    =  input_dict["""input_ids"""]
				__a			:  Tuple		    =  input_ids.ne(1       ).to(SCREAMING_SNAKE_CASE_       )
				__a			:  Union[str, Any]		    =  ids_tensor([self.model_tester.batch_size] ,    self.model_tester.type_sequence_label_size       )
				__a			:  Any		    =  BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_       )
				model.to(SCREAMING_SNAKE_CASE_       )
				model.eval()
				__a			:  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)       )







			def 				lowerCAmelCase      (self			: Dict       ):
				__a			:  Optional[Any]		    =  self.model_tester.prepare_config_and_inputs_for_common()
				__a			:  Dict		    =  3
				__a			:  str		    =  """multi_label_classification"""
				__a			:  Optional[Any]		    =  input_dict["""input_ids"""]
				__a			:  Dict		    =  input_ids.ne(1       ).to(SCREAMING_SNAKE_CASE_       )
				__a			:  List[str]		    =  ids_tensor(
				    [self.model_tester.batch_size, config.num_labels] ,    self.model_tester.type_sequence_label_size       ).to(torch.float       )
				__a			:  Any		    =  BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_       )
				model.to(SCREAMING_SNAKE_CASE_       )
				model.eval()
				__a			:  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 		UpperCamelCase__  (	unittest.TestCase   ):
			@slow
			def 				lowerCAmelCase      (self			: int       ):
				__a			:  Optional[int]		    =  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''       )
				__a			:  str		    =  torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]       )
				__a			:  List[str]		    =  model(SCREAMING_SNAKE_CASE_       )[0]

				__a			:  str		    =  4_2_3_8_4

				__a			:  str		    =  torch.Size((1, 5, vocab_size)       )
				self.assertEqual(output.shape ,    SCREAMING_SNAKE_CASE_       )

				__a			:  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 				lowerCAmelCase      (self			: Optional[Any]       ):
				__a			:  int		    =  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''       )
				__a			:  Tuple		    =  BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''       )
				model.to(SCREAMING_SNAKE_CASE_       )

				torch.manual_seed(0       )
				__a			:  List[str]		    =  tokenizer('''COVID-19 is''' ,    return_tensors='''pt'''       ).to(SCREAMING_SNAKE_CASE_       )
				__a			:  List[Any]		    =  model.generate(
				    **SCREAMING_SNAKE_CASE_ ,    min_length=1_0_0 ,    max_length=1_0_2_4 ,    num_beams=5 ,    early_stopping=SCREAMING_SNAKE_CASE_ ,    )
				__a			:  Dict		    =  tokenizer.decode(output_ids[0] ,    skip_special_tokens=SCREAMING_SNAKE_CASE_       )

				__a			:  Tuple		    =  (
				    """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_       )


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				CustomTokenizer
   pass
