
"""simple docstring"""





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
				_a														=		{
				    """linear""": PIL.Image.Resampling.BILINEAR,
				    """bilinear""": PIL.Image.Resampling.BILINEAR,
				    """bicubic""": PIL.Image.Resampling.BICUBIC,
				    """lanczos""": PIL.Image.Resampling.LANCZOS,
				    """nearest""": PIL.Image.Resampling.NEAREST,
				}
else:
				_a														=		{
				    """linear""": PIL.Image.LINEAR,
				    """bilinear""": PIL.Image.BILINEAR,
				    """bicubic""": PIL.Image.BICUBIC,
				    """lanczos""": PIL.Image.LANCZOS,
				    """nearest""": PIL.Image.NEAREST,
				}



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


					"""simple docstring"""





					_UpperCamelCase										=  (images / 2 + 0.5).clamp(0,					1     )
					_UpperCamelCase										=  images.cpu().permute(0,					2,					3,					1     ).float().numpy()
					_UpperCamelCase										=  numpy_to_pil(__snake_case     )
					return images



def 				lowerCamelCase__		(      __snake_case     )							->		Tuple:


					"""simple docstring"""





					if images.ndim == 3:
										_UpperCamelCase										=  images[None, ...]
					_UpperCamelCase										=  (images * 2_55).round().astype('''uint8'''     )
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										_UpperCamelCase										=  [Image.fromarray(image.squeeze(),					mode='''L'''     ) for image in images]
					else:
										_UpperCamelCase										=  [Image.fromarray(__snake_case     ) for image in images]

					return pil_images








"""simple docstring"""





import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    AlbertForMaskedLM,
				    AlbertForMultipleChoice,
				    AlbertForPreTraining,
				    AlbertForQuestionAnswering,
				    AlbertForSequenceClassification,
				    AlbertForTokenClassification,
				    AlbertModel,
				)
				from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a=13    ,		__a=7    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=99    ,		__a=16    ,		__a=36    ,		__a=6    ,		__a=6    ,		__a=6    ,		__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    ,		)	->	List[str]:






							'''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										=  embedding_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_hidden_groups
							_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)	->	List[str]:






							'''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)	->	List[str]:






							'''simple docstring'''
							return AlbertConfig(
							    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    ,		initializer_range=self.initializer_range    ,		num_hidden_groups=self.num_hidden_groups    ,		)



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






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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  AlbertForPreTraining(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(
							    __a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a    ,		sentence_order_label=__a    ,		)
							self.parent.assertEqual(result.prediction_logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size))
							self.parent.assertEqual(result.sop_logits.shape    ,		(self.batch_size, config.num_labels))



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






							'''simple docstring'''
							_UpperCamelCase										=  AlbertForMaskedLM(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    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  AlbertForQuestionAnswering(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(
							    __a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		start_positions=__a    ,		end_positions=__a    ,		)
							self.parent.assertEqual(result.start_logits.shape    ,		(self.batch_size, self.seq_length))
							self.parent.assertEqual(result.end_logits.shape    ,		(self.batch_size, self.seq_length))



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






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  AlbertForSequenceClassification(__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.num_labels))



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






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  AlbertForTokenClassification(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.num_labels))



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






							'''simple docstring'''
							_UpperCamelCase										=  self.num_choices
							_UpperCamelCase										=  AlbertForMultipleChoice(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  input_ids.unsqueeze(1).expand(-1    ,		self.num_choices    ,		-1).contiguous()
							_UpperCamelCase										=  token_type_ids.unsqueeze(1).expand(-1    ,		self.num_choices    ,		-1).contiguous()
							_UpperCamelCase										=  input_mask.unsqueeze(1).expand(-1    ,		self.num_choices    ,		-1).contiguous()
							_UpperCamelCase										=  model(
							    __a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a    ,		)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_choices))




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






							'''simple docstring'''
							_UpperCamelCase										=  self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  
							)										=  config_and_inputs
							_UpperCamelCase										=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_torch
class    _UpperCAmelCase(   lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				(
		    (
		        AlbertModel,
		        AlbertForPreTraining,
		        AlbertForMaskedLM,
		        AlbertForMultipleChoice,
		        AlbertForSequenceClassification,
		        AlbertForTokenClassification,
		        AlbertForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase__      =				(
		    {
		        'feature-extraction': AlbertModel,
		        'fill-mask': AlbertForMaskedLM,
		        'question-answering': AlbertForQuestionAnswering,
		        'text-classification': AlbertForSequenceClassification,
		        'token-classification': AlbertForTokenClassification,
		        'zero-shot': AlbertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase__      =				True



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a=False)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  super()._prepare_for_class(__a    ,		__a    ,		return_labels=__a)

							if return_labels:
												if model_class in get_values(__a):
																	_UpperCamelCase										=  torch.zeros(
																	    (self.model_tester.batch_size, self.model_tester.seq_length)    ,		dtype=torch.long    ,		device=__a)
																	_UpperCamelCase										=  torch.zeros(
																	    self.model_tester.batch_size    ,		dtype=torch.long    ,		device=__a)
							return inputs_dict



		def 						UpperCAmelCase							( self)	->	Dict:






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



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






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



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






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



		def 						UpperCAmelCase							( self)	->	int:






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



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






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



		def 						UpperCAmelCase							( self)	->	Any:






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



		def 						UpperCAmelCase							( self)	->	Any:






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



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






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



		def 						UpperCAmelCase							( self)	->	Dict:






							'''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)




		@slow
		def 						UpperCAmelCase							( self)	->	int:






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






@require_torch
class    _UpperCAmelCase(   unittest.TestCase     ):



		@slow
		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  AlbertModel.from_pretrained('''albert-base-v2''')
							_UpperCamelCase										=  torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]])
							_UpperCamelCase										=  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
							with torch.no_grad():
												_UpperCamelCase										=  model(__a    ,		attention_mask=__a)[0]
							_UpperCamelCase										=  torch.Size((1, 11, 7_68))
							self.assertEqual(output.shape    ,		__a)
							_UpperCamelCase										=  torch.tensor(
							    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]])

							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,		__a    ,		atol=1e-4))






