




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_       :






				def __init__(			self      ,snake_case__      ,snake_case__=13      ,snake_case__=7      ,snake_case__=True      ,snake_case__=True      ,snake_case__=False      ,snake_case__=True      ,snake_case__=99      ,snake_case__=32      ,snake_case__=5      ,snake_case__=4      ,snake_case__=37      ,snake_case__="gelu"      ,snake_case__=0.1      ,snake_case__=0.1      ,snake_case__=512      ,snake_case__=16      ,snake_case__=2      ,snake_case__=0.02      ,snake_case__=3      ,snake_case__=4      ,snake_case__=None      ,):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      parent
							SCREAMING_SNAKE_CASE_   : List[str]					  =      batch_size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      seq_length
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      is_training
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      use_input_mask
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      use_token_type_ids
							SCREAMING_SNAKE_CASE_   : int					  =      use_labels
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      hidden_size
							SCREAMING_SNAKE_CASE_   : int					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : Tuple					  =      intermediate_size
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : str					  =      hidden_dropout_prob
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : Tuple					  =      type_vocab_size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      type_sequence_label_size
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : str					  =      num_labels
							SCREAMING_SNAKE_CASE_   : int					  =      num_choices
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      scope






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      None
							if self.use_input_mask:
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      random_attention_mask([self.batch_size, self.seq_length] )

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

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

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.get_config()

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






				def 		snake_case	(			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=snake_case__      ,initializer_range=self.initializer_range      ,)






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : int					  =      BioGptModel(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Tuple					  =      model(snake_case__      ,attention_mask=snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      model(snake_case__ )
							self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      BioGptForCausalLM(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model(snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,labels=snake_case__ )
							self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size) )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,*snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Any					  =      BioGptModel(config=snake_case__ )
							model.to(snake_case__ )
							model.eval()

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

							# first forward pass
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      model(snake_case__      ,attention_mask=snake_case__ ).to_tuple()

							# create hypothetical next token and extent to next_input_ids
							SCREAMING_SNAKE_CASE_   : List[str]					  =      ids_tensor((self.batch_size, 1)      ,config.vocab_size )

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

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

							# get two different outputs
							SCREAMING_SNAKE_CASE_   : int					  =      model(snake_case__      ,attention_mask=snake_case__ )['last_hidden_state']
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      model(snake_case__      ,past_key_values=snake_case__      ,attention_mask=snake_case__ )['last_hidden_state']

							# select random slice
							SCREAMING_SNAKE_CASE_   : str					  =      ids_tensor((1,)      ,output_from_past.shape[-1] ).item()
							SCREAMING_SNAKE_CASE_   : Dict					  =      output_from_no_past[:, -1, random_slice_idx].detach()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      output_from_past[:, 0, random_slice_idx].detach()

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,*snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      BioGptModel(config=snake_case__ ).to(snake_case__ ).eval()

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

							# first forward pass
							SCREAMING_SNAKE_CASE_   : Any					  =      model(snake_case__      ,attention_mask=snake_case__      ,use_cache=snake_case__ )

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : int					  =      outputs.to_tuple()

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

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

							SCREAMING_SNAKE_CASE_   : Tuple					  =      model(snake_case__      ,attention_mask=snake_case__ )['last_hidden_state']
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model(snake_case__      ,attention_mask=snake_case__      ,past_key_values=snake_case__ )[
							    'last_hidden_state'
							]

							# select random slice
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      ids_tensor((1,)      ,output_from_past.shape[-1] ).item()
							SCREAMING_SNAKE_CASE_   : str					  =      output_from_no_past[:, -3:, random_slice_idx].detach()
							SCREAMING_SNAKE_CASE_   : str					  =      output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,*snake_case__      ,snake_case__=False ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      BioGptForCausalLM(snake_case__ )
							model.to(snake_case__ )
							if gradient_checkpointing:
										model.gradient_checkpointing_enable()

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model(snake_case__      ,labels=snake_case__ )
							self.parent.assertEqual(result.loss.shape      ,() )
							self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size) )
							result.loss.backward()






				def 		snake_case	(			self      ,snake_case__      ,*snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      BioGptModel(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      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 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,*snake_case__ ):
							SCREAMING_SNAKE_CASE_   : str					  =      self.num_labels
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      BioGptForTokenClassification(snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model(snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__ )
							self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels) )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      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_
							)  ,    
							)   : Any					  =      config_and_inputs
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class 				lowerCAmelCase_       (					lowerCamelCase_						,     lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase							):
				__a     :       Dict						=     (
				    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
				    if is_torch_available()
				    else ()
				)
				__a     :       List[Any]						=     (BioGptForCausalLM,) if is_torch_available() else ()
				__a     :       Union[str, Any]						=     (
				    {
				        "feature-extraction": BioGptModel,
				        "text-classification": BioGptForSequenceClassification,
				        "text-generation": BioGptForCausalLM,
				        "token-classification": BioGptForTokenClassification,
				        "zero-shot": BioGptForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__a     :       int						=     False






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      BioGptModelTester(self )
							SCREAMING_SNAKE_CASE_   : int					  =      ConfigTester(self      ,config_class=snake_case__      ,hidden_size=37 )






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






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      type
										self.model_tester.create_and_check_model(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_attention_mask_past(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*snake_case__      ,gradient_checkpointing=snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_past_large_inputs(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*snake_case__ )






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
							model.to(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      BioGptTokenizer.from_pretrained('microsoft/biogpt' )

							SCREAMING_SNAKE_CASE_   : Dict					  =      'left'

							# Define PAD Token = EOS Token = 50256
							SCREAMING_SNAKE_CASE_   : str					  =      tokenizer.eos_token
							SCREAMING_SNAKE_CASE_   : str					  =      model.config.eos_token_id

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

							SCREAMING_SNAKE_CASE_   : str					  =      tokenizer(snake_case__      ,return_tensors='pt'      ,padding=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      inputs['input_ids'].to(snake_case__ )

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

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

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

							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer.batch_decode(snake_case__      ,skip_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Dict					  =      tokenizer.decode(output_non_padded[0]      ,skip_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      tokenizer.decode(output_padded[0]      ,skip_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Dict					  =      [
							    'Hello, my dog is a little bit bigger than a little bit.',
							    'Today, I have a good idea of how to use the information',
							]
							self.assertListEqual(snake_case__      ,snake_case__ )
							self.assertListEqual(snake_case__      ,[non_padded_sentence, padded_sentence] )






				@slow
				def 		snake_case	(			self ):
							for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      BioGptModel.from_pretrained(snake_case__ )
										self.assertIsNotNone(snake_case__ )






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






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      3
							SCREAMING_SNAKE_CASE_   : str					  =      'multi_label_classification'
							SCREAMING_SNAKE_CASE_   : List[str]					  =      input_dict['input_ids']
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      input_ids.ne(1 ).to(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]      ,self.model_tester.type_sequence_label_size ).to(torch.float )
							SCREAMING_SNAKE_CASE_   : str					  =      BioGptForSequenceClassification(snake_case__ )
							model.to(snake_case__ )
							model.eval()
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      model(snake_case__      ,attention_mask=snake_case__      ,labels=snake_case__ )
							self.assertEqual(result.logits.shape      ,(self.model_tester.batch_size, self.model_tester.num_labels) )







@require_torch
class 				lowerCAmelCase_       (					unittest.TestCase							):






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch.tensor([[2, 4805, 9, 656, 21]] )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      model(snake_case__ )[0]

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      42384

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

							SCREAMING_SNAKE_CASE_   : int					  =      torch.tensor(
							    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )

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






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      BioGptTokenizer.from_pretrained('microsoft/biogpt' )
							SCREAMING_SNAKE_CASE_   : Dict					  =      BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
							model.to(snake_case__ )

							torch.manual_seed(0 )
							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer('COVID-19 is'      ,return_tensors='pt' ).to(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      model.generate(
							    **snake_case__      ,min_length=100      ,max_length=1024      ,num_beams=5      ,early_stopping=snake_case__      ,)
							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer.decode(output_ids[0]      ,skip_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      (
							    'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
							    ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
							    ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
							    ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
							    ' more than 800,000 deaths.'
							)
							self.assertEqual(snake_case__      ,snake_case__ )










# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCamelCase__           =			subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
UpperCamelCase__           =			subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode("utf-8").split()

UpperCamelCase__           =			"|".join(sys.argv[1:])
UpperCamelCase__           =			re.compile(rf"""^({joined_dirs}).*?\.py$""")

UpperCamelCase__           =			[x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")




