


'''simple docstring'''





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__							:




						def __init__(       self					:      Union[str, Any]				,    _a					:      Tuple				,    _a					:      Dict=13				,    _a					:      Optional[Any]=7				,    _a					:      List[str]=True				,    _a					:      Optional[int]=True				,    _a					:      str=False				,    _a					:      List[str]=True				,    _a					:      Optional[int]=99				,    _a					:      List[str]=32				,    _a					:      Optional[int]=5				,    _a					:      Dict=4				,    _a					:      int=37				,    _a					:      Dict="gelu"				,    _a					:      Dict=0.1				,    _a					:      str=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Tuple=16				,    _a					:      Tuple=2				,    _a					:      Dict=0.02				,    _a					:      Union[str, Any]=3				,    _a					:      Optional[Any]=4				,    _a					:      List[str]=None				,    )							->     Any:


										"""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 							__UpperCamelCase				(       self					:      List[str]  )							->     Union[str, Any]:


										"""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 							__UpperCamelCase				(       self					:      str  )							->     Any:


										"""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=_a				,    initializer_range=self.initializer_range				,    )




						def 							__UpperCamelCase				(       self					:      Union[str, Any]				,    _a					:      Any				,    _a					:      List[str]				,    _a					:      Optional[int]				,    _a					:      Union[str, Any]				,    _a					:      Dict				,    _a					:      Tuple				,    _a					:      Dict  )							->     Any:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Optional[int]				,    _a					:      Union[str, Any]				,    _a					:      Optional[Any]				,    _a					:      Union[str, Any]				,    _a					:      Dict				,    _a					:      int				,    _a					:      int				,    _a					:      str				,    _a					:      Dict				,    )							->     Optional[int]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    _a					:      List[Any]				,    _a					:      List[str]				,    _a					:      Any				,    _a					:      str				,    _a					:      Optional[int]				,    *_a					:      int  )							->     Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BioGptModel(config=_a  )
										model.to(_a  )
										model.eval()

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

										# first forward pass
										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a  ).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,)				,    _a  ).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=_a  )]				,    dim=1				,    )

										# get two different outputs
										_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a  )['''last_hidden_state''']
										_SCREAMING_SNAKE_CASE			  =model(_a				,    past_key_values=_a				,    attention_mask=_a  )['''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(_a				,    _a				,    atol=1E-3  )  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Optional[Any]				,    _a					:      List[str]				,    _a					:      Optional[int]				,    _a					:      Optional[Any]				,    _a					:      Union[str, Any]				,    *_a					:      str  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BioGptModel(config=_a  ).to(_a  ).eval()

										_SCREAMING_SNAKE_CASE			  =torch.ones(input_ids.shape				,    dtype=torch.long				,    device=_a  )

										# first forward pass
										_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a				,    use_cache=_a  )

										_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(_a				,    attention_mask=_a  )['''last_hidden_state''']
										_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a				,    past_key_values=_a  )[
										    '''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(_a				,    _a				,    atol=1E-3  )  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      str				,    _a					:      Optional[Any]				,    _a					:      Dict				,    _a					:      Tuple				,    _a					:      List[str]				,    *_a					:      List[Any]				,    _a					:      str=False  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BioGptForCausalLM(_a  )
										model.to(_a  )
										if gradient_checkpointing:
														model.gradient_checkpointing_enable()

										_SCREAMING_SNAKE_CASE			  =model(_a				,    labels=_a  )
										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					:      List[Any]				,    _a					:      str				,    *_a					:      Optional[Any]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BioGptModel(_a  )
										_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 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      Tuple				,    _a					:      str				,    _a					:      Optional[int]				,    _a					:      str				,    _a					:      Dict				,    *_a					:      str  )							->     int:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Any:


										"""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 						A__							(		UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						(
						    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase             =						(BioGptForCausalLM,) if is_torch_available() else ()
						UpperCAmelCase             =						(
						    {
						        """feature-extraction""": BioGptModel,
						        """text-classification""": BioGptForSequenceClassification,
						        """text-generation""": BioGptForCausalLM,
						        """token-classification""": BioGptForTokenClassification,
						        """zero-shot""": BioGptForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase             =						False




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BioGptModelTester(self  )
										_SCREAMING_SNAKE_CASE			  =ConfigTester(self				,    config_class=_a				,    hidden_size=37  )




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


										"""simple docstring"""





										self.config_tester.run_common_tests()




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a  )




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


										"""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(*_a  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_a  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_forward_and_backwards(*_a				,    gradient_checkpointing=_a  )




						def 							__UpperCamelCase				(       self					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_a  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_weight_initialization(*_a  )




						def 							__UpperCamelCase				(       self					:      int  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_for_token_classification(*_a  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''  )
										model.to(_a  )
										_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(_a				,    return_tensors='''pt'''				,    padding=_a  )
										_SCREAMING_SNAKE_CASE			  =inputs['''input_ids'''].to(_a  )

										_SCREAMING_SNAKE_CASE			  =model.generate(
										    input_ids=_a				,    attention_mask=inputs['''attention_mask'''].to(_a  )				,    )

										_SCREAMING_SNAKE_CASE			  =tokenizer(sentences[0]				,    return_tensors='''pt'''  ).input_ids.to(_a  )
										_SCREAMING_SNAKE_CASE			  =model.generate(input_ids=_a  )

										_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(_a  )
										_SCREAMING_SNAKE_CASE			  =model.generate(input_ids=_a				,    max_length=model.config.max_length - num_paddings  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.batch_decode(_a				,    skip_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =tokenizer.decode(output_non_padded[0]				,    skip_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =tokenizer.decode(output_padded[0]				,    skip_special_tokens=_a  )

										_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(_a				,    _a  )
										self.assertListEqual(_a				,    [non_padded_sentence, padded_sentence]  )




						@slow
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[Any]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      str  )							->     int:


										"""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(_a  )
										_SCREAMING_SNAKE_CASE			  =ids_tensor([self.model_tester.batch_size]				,    self.model_tester.type_sequence_label_size  )
										_SCREAMING_SNAKE_CASE			  =BioGptForSequenceClassification(_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a				,    labels=_a  )
										self.assertEqual(result.logits.shape				,    (self.model_tester.batch_size, self.model_tester.num_labels)  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     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			  ='''multi_label_classification'''
										_SCREAMING_SNAKE_CASE			  =input_dict['''input_ids''']
										_SCREAMING_SNAKE_CASE			  =input_ids.ne(1  ).to(_a  )
										_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(_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =model(_a				,    attention_mask=_a				,    labels=_a  )
										self.assertEqual(result.logits.shape				,    (self.model_tester.batch_size, self.model_tester.num_labels)  )

@require_torch
class 						A__							(		unittest.TestCase  ):




						@slow
						def 							__UpperCamelCase				(       self					:      int  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''  )
										_SCREAMING_SNAKE_CASE			  =torch.tensor([[2, 4805, 9, 656, 21]]  )
										_SCREAMING_SNAKE_CASE			  =model(_a  )[0]

										_SCREAMING_SNAKE_CASE			  =4_2384

										_SCREAMING_SNAKE_CASE			  =torch.Size((1, 5, vocab_size)  )
										self.assertEqual(output.shape				,    _a  )

										_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]				,    _a				,    atol=1E-4  )  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BioGptTokenizer.from_pretrained('''microsoft/biogpt'''  )
										_SCREAMING_SNAKE_CASE			  =BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''  )
										model.to(_a  )

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

										_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(_a				,    _a  )



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["image_processor", "tokenizer"]
						UpperCAmelCase             =						"ViTImageProcessor"
						UpperCAmelCase             =						("CLIPTokenizer", "CLIPTokenizerFast")




						def __init__(       self					:      int				,    _a					:      int=None				,    _a					:      Optional[int]=None				,    **_a					:      str  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
														    ''' instead.'''				,    _a				,    )
														_SCREAMING_SNAKE_CASE			  =kwargs.pop('''feature_extractor'''  )

										_SCREAMING_SNAKE_CASE			  =image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError('''You need to specify an `image_processor`.'''  )
										if tokenizer is None:
														raise ValueError('''You need to specify a `tokenizer`.'''  )

										super().__init__(_a				,    _a  )




						def __call__(       self					:      List[str]				,    _a					:      Tuple=None				,    _a					:      Optional[Any]=None				,    _a					:      List[Any]=None				,    _a					:      List[Any]=None				,    **_a					:      List[Any]  )							->     Optional[Any]:


										"""simple docstring"""





										if text is None and visual_prompt is None and images is None:
														raise ValueError('''You have to specify either text, visual prompt or images.'''  )

										if text is not None and visual_prompt is not None:
														raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''  )

										if text is not None:
														_SCREAMING_SNAKE_CASE			  =self.tokenizer(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if images is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''pixel_values''': image_features.pixel_values,
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										elif text is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  =image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										elif visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										else:
														return BatchEncoding(data=dict(**_a  )				,    tensor_type=_a  )




						def 							__UpperCamelCase				(       self					:      Any				,    *_a					:      str				,    **_a					:      List[Any]  )							->     Tuple:


										"""simple docstring"""





										return self.tokenizer.batch_decode(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    *_a					:      str				,    **_a					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





										return self.tokenizer.decode(*_a				,    **_a  )




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     int:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,    _a				,    )
										return self.image_processor_class




						@property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Union[str, Any]:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,    _a				,    )
										return self.image_processor