
"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


_a														=		logging.get_logger(__name__)



def 				lowerCamelCase__		(      __snake_case     )							->		List[List[ImageInput]]:


					"""simple docstring"""





					if isinstance(__snake_case,					(list, tuple)     ) and isinstance(videos[0],					(list, tuple)     ) and is_valid_image(videos[0][0]     ):
										return videos

					elif isinstance(__snake_case,					(list, tuple)     ) and is_valid_image(videos[0]     ):
										return [videos]

					elif is_valid_image(__snake_case     ):
										return [[videos]]

					raise ValueError(F'''Could not make batched video from {videos}'''     )






class    _UpperCAmelCase(   lowerCamelCase     ):
	lowercase__      =				['pixel_values']



	def __init__( self    ,		__a = True    ,		__a = None    ,		__a = PILImageResampling.BILINEAR    ,		__a = True    ,		__a = None    ,		__a = True    ,		__a = 1 / 2_55    ,		__a = True    ,		__a = None    ,		__a = None    ,		**__a    ,		)	->	None:






						'''simple docstring'''
						super().__init__(**__a)
						_UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 2_24}
						_UpperCamelCase										=  get_size_dict(__a    ,		default_to_square=__a)
						_UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
						_UpperCamelCase										=  get_size_dict(__a    ,		param_name='''crop_size''')

						_UpperCamelCase										=  do_resize
						_UpperCamelCase										=  size
						_UpperCamelCase										=  do_center_crop
						_UpperCamelCase										=  crop_size
						_UpperCamelCase										=  resample
						_UpperCamelCase										=  do_rescale
						_UpperCamelCase										=  rescale_factor
						_UpperCamelCase										=  do_normalize
						_UpperCamelCase										=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_UpperCamelCase										=  image_std if image_std is not None else IMAGENET_STANDARD_STD



	def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a = PILImageResampling.BILINEAR    ,		__a = None    ,		**__a    ,		)	->	np.ndarray:






						'''simple docstring'''
						_UpperCamelCase										=  get_size_dict(__a    ,		default_to_square=__a)
						if "shortest_edge" in size:
											_UpperCamelCase										=  get_resize_output_image_size(__a    ,		size['''shortest_edge''']    ,		default_to_square=__a)
						elif "height" in size and "width" in size:
											_UpperCamelCase										=  (size['''height'''], size['''width'''])
						else:
											raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
						return resize(__a    ,		size=__a    ,		resample=__a    ,		data_format=__a    ,		**__a)



	def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a = None    ,		**__a    ,		)	->	np.ndarray:






						'''simple docstring'''
						_UpperCamelCase										=  get_size_dict(__a)
						if "height" not in size or "width" not in size:
											raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''')
						return center_crop(__a    ,		size=(size['''height'''], size['''width'''])    ,		data_format=__a    ,		**__a)



	def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a = None    ,		**__a    ,		)	->	int:






						'''simple docstring'''
						return rescale(__a    ,		scale=__a    ,		data_format=__a    ,		**__a)



	def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		**__a    ,		)	->	np.ndarray:






						'''simple docstring'''
						return normalize(__a    ,		mean=__a    ,		std=__a    ,		data_format=__a    ,		**__a)



	def 						UpperCAmelCase							( self    ,		__a    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = ChannelDimension.FIRST    ,		)	->	np.ndarray:






						'''simple docstring'''
						if do_resize and size is None or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.''')

						if do_center_crop and crop_size is None:
											raise ValueError('''Crop size must be specified if do_center_crop is True.''')

						if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

						if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

						# All transformations expect numpy arrays.
						_UpperCamelCase										=  to_numpy_array(__a)

						if do_resize:
											_UpperCamelCase										=  self.resize(image=__a    ,		size=__a    ,		resample=__a)

						if do_center_crop:
											_UpperCamelCase										=  self.center_crop(__a    ,		size=__a)

						if do_rescale:
											_UpperCamelCase										=  self.rescale(image=__a    ,		scale=__a)

						if do_normalize:
											_UpperCamelCase										=  self.normalize(image=__a    ,		mean=__a    ,		std=__a)

						_UpperCamelCase										=  to_channel_dimension_format(__a    ,		__a)
						return image




	def 						UpperCAmelCase							( self    ,		__a    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = ChannelDimension.FIRST    ,		**__a    ,		)	->	PIL.Image.Image:






						'''simple docstring'''
						_UpperCamelCase										=  do_resize if do_resize is not None else self.do_resize
						_UpperCamelCase										=  resample if resample is not None else self.resample
						_UpperCamelCase										=  do_center_crop if do_center_crop is not None else self.do_center_crop
						_UpperCamelCase										=  do_rescale if do_rescale is not None else self.do_rescale
						_UpperCamelCase										=  rescale_factor if rescale_factor is not None else self.rescale_factor
						_UpperCamelCase										=  do_normalize if do_normalize is not None else self.do_normalize
						_UpperCamelCase										=  image_mean if image_mean is not None else self.image_mean
						_UpperCamelCase										=  image_std if image_std is not None else self.image_std

						_UpperCamelCase										=  size if size is not None else self.size
						_UpperCamelCase										=  get_size_dict(__a    ,		default_to_square=__a)
						_UpperCamelCase										=  crop_size if crop_size is not None else self.crop_size
						_UpperCamelCase										=  get_size_dict(__a    ,		param_name='''crop_size''')

						if not valid_images(__a):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.''')

						_UpperCamelCase										=  make_batched(__a)

						_UpperCamelCase										=  [
						    [
						        self._preprocess_image(
						            image=__a    ,		do_resize=__a    ,		size=__a    ,		resample=__a    ,		do_center_crop=__a    ,		crop_size=__a    ,		do_rescale=__a    ,		rescale_factor=__a    ,		do_normalize=__a    ,		image_mean=__a    ,		image_std=__a    ,		data_format=__a    ,		)
						        for img in video
						    ]
						    for video in videos
						]

						_UpperCamelCase										=  {'''pixel_values''': videos}
						return BatchFeature(data=__a    ,		tensor_type=__a)








"""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    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a=13    ,		__a=7    ,		__a=True    ,		__a=True    ,		__a=False    ,		__a=True    ,		__a=99    ,		__a=32    ,		__a=5    ,		__a=4    ,		__a=37    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=16    ,		__a=2    ,		__a=0.02    ,		__a=3    ,		__a=4    ,		__a=None    ,		)	->	Tuple:






							'''simple docstring'''
							_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)	->	Tuple:






							'''simple docstring'''
							_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)	->	str:






							'''simple docstring'''
							return BioGptConfig(
							    vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		type_vocab_size=self.type_vocab_size    ,		is_decoder=__a    ,		initializer_range=self.initializer_range    ,		)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a)
							_UpperCamelCase										=  model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptForCausalLM(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  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    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		*__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModel(config=__a)
							model.to(__a)
							model.eval()

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

							# first forward pass
							_UpperCamelCase			,  _UpperCamelCase										=  model(__a    ,		attention_mask=__a).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,)    ,		__a).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=__a)]    ,		dim=1    ,		)

							# get two different outputs
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a)['''last_hidden_state''']
							_UpperCamelCase										=  model(__a    ,		past_key_values=__a    ,		attention_mask=__a)['''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(__a    ,		__a    ,		atol=1e-3))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		*__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModel(config=__a).to(__a).eval()

							_UpperCamelCase										=  torch.ones(input_ids.shape    ,		dtype=torch.long    ,		device=__a)

							# first forward pass
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		use_cache=__a)

							_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(__a    ,		attention_mask=__a)['''last_hidden_state''']
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		past_key_values=__a)[
							    '''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(__a    ,		__a    ,		atol=1e-3))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		*__a    ,		__a=False)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptForCausalLM(__a)
							model.to(__a)
							if gradient_checkpointing:
												model.gradient_checkpointing_enable()

							_UpperCamelCase										=  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    ,		__a    ,		*__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModel(__a)
							_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    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		*__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  BioGptForTokenClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  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[Any]:






							'''simple docstring'''
							_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    _UpperCAmelCase(   lowerCamelCase				,					lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				(
		    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
		    if is_torch_available()
		    else ()
		)
		lowercase__      =				(BioGptForCausalLM,) if is_torch_available() else ()
		lowercase__      =				(
		    {
		        'feature-extraction': BioGptModel,
		        'text-classification': BioGptForSequenceClassification,
		        'text-generation': BioGptForCausalLM,
		        'token-classification': BioGptForTokenClassification,
		        'zero-shot': BioGptForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase__      =				False



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=__a    ,		hidden_size=37)



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_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(*__a)



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__a)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*__a    ,		gradient_checkpointing=__a)



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__a)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*__a)



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*__a)



		@slow
		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							model.to(__a)
							_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(__a    ,		return_tensors='''pt'''    ,		padding=__a)
							_UpperCamelCase										=  inputs['''input_ids'''].to(__a)

							_UpperCamelCase										=  model.generate(
							    input_ids=__a    ,		attention_mask=inputs['''attention_mask'''].to(__a)    ,		)

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

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

							_UpperCamelCase										=  tokenizer.batch_decode(__a    ,		skip_special_tokens=__a)
							_UpperCamelCase										=  tokenizer.decode(output_non_padded[0]    ,		skip_special_tokens=__a)
							_UpperCamelCase										=  tokenizer.decode(output_padded[0]    ,		skip_special_tokens=__a)

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



		@slow
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase										=  BioGptModel.from_pretrained(__a)
												self.assertIsNotNone(__a)



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase										=  3
							_UpperCamelCase										=  input_dict['''input_ids''']
							_UpperCamelCase										=  input_ids.ne(1).to(__a)
							_UpperCamelCase										=  ids_tensor([self.model_tester.batch_size]    ,		self.model_tester.type_sequence_label_size)
							_UpperCamelCase										=  BioGptForSequenceClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  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]:






							'''simple docstring'''
							_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(__a)
							_UpperCamelCase										=  ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]    ,		self.model_tester.type_sequence_label_size).to(torch.float)
							_UpperCamelCase										=  BioGptForSequenceClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  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    _UpperCAmelCase(   unittest.TestCase     ):



		@slow
		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							_UpperCamelCase										=  torch.tensor([[2, 48_05, 9, 6_56, 21]])
							_UpperCamelCase										=  model(__a)[0]

							_UpperCamelCase										=  4_23_84

							_UpperCamelCase										=  torch.Size((1, 5, vocab_size))
							self.assertEqual(output.shape    ,		__a)

							_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]    ,		__a    ,		atol=1e-4))




		@slow
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  BioGptTokenizer.from_pretrained('''microsoft/biogpt''')
							_UpperCamelCase										=  BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
							model.to(__a)

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

							_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(__a    ,		__a)






