


'''simple docstring'''




import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import BeitImageProcessor



class  lowerCAmelCase				(				unittest.TestCase							):



	def __init__(					self ,					snake_case__ ,					snake_case__=7 ,					snake_case__=3 ,					snake_case__=18 ,					snake_case__=30 ,					snake_case__=400 ,					snake_case__=True ,					snake_case__=None ,					snake_case__=True ,					snake_case__=None ,					snake_case__=True ,					snake_case__=[0.5, 0.5, 0.5] ,					snake_case__=[0.5, 0.5, 0.5] ,					snake_case__=False ,					):
		lowerCAmelCase					:    Optional[Any]      							= size if size is not None else {'height': 20, 'width': 20}
		lowerCAmelCase					:    int      							= crop_size if crop_size is not None else {'height': 18, 'width': 18}
		lowerCAmelCase					:    Tuple      							= parent
		lowerCAmelCase					:    Optional[Any]      							= batch_size
		lowerCAmelCase					:    Optional[int]      							= num_channels
		lowerCAmelCase					:    str      							= image_size
		lowerCAmelCase					:    str      							= min_resolution
		lowerCAmelCase					:    Tuple      							= max_resolution
		lowerCAmelCase					:    str      							= do_resize
		lowerCAmelCase					:    Dict      							= size
		lowerCAmelCase					:    Optional[int]      							= do_center_crop
		lowerCAmelCase					:    int      							= crop_size
		lowerCAmelCase					:    List[Any]      							= do_normalize
		lowerCAmelCase					:    Dict      							= image_mean
		lowerCAmelCase					:    Dict      							= image_std
		lowerCAmelCase					:    Optional[Any]      							= do_reduce_labels



	def 				lowercase     (					self    ):
		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_center_crop": self.do_center_crop,
		    "crop_size": self.crop_size,
		    "do_normalize": self.do_normalize,
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_reduce_labels": self.do_reduce_labels,
		}


def 			__UpperCamelCase    (    )    ->      Tuple:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= load_dataset('hf-internal-testing/fixtures_ade20k'					,   split='test'				)

	lowerCAmelCase					:    Tuple      							= Image.open(dataset[0]['file']				)
	lowerCAmelCase					:    int      							= Image.open(dataset[1]['file']				)

	return image, map

def 			__UpperCamelCase    (    )    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= load_dataset('hf-internal-testing/fixtures_ade20k'					,   split='test'				)

	lowerCAmelCase					:    List[Any]      							= Image.open(ds[0]['file']				)
	lowerCAmelCase					:    Tuple      							= Image.open(ds[1]['file']				)
	lowerCAmelCase					:    Tuple      							= Image.open(ds[2]['file']				)
	lowerCAmelCase					:    Dict      							= Image.open(ds[3]['file']				)

	return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class  lowerCAmelCase				(				__UpperCAmelCase      ,				unittest.TestCase							):
	_lowerCamelCase   :					Union[str, Any]			      = BeitImageProcessor if is_vision_available() else None



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= BeitImageProcessingTester(self    )



	@property
	def 				lowercase     (					self    ):
		return self.image_processor_tester.prepare_image_processor_dict()



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.image_processing_class(**self.image_processor_dict    )
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'do_resize'    )    )
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'size'    )    )
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'do_center_crop'    )    )
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'center_crop'    )    )
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'do_normalize'    )    )
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'image_mean'    )    )
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE ,					'image_std'    )    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.image_processing_class.from_dict(self.image_processor_dict    )
		self.assertEqual(image_processor.size ,					{'height': 20, 'width': 20}    )
		self.assertEqual(image_processor.crop_size ,					{'height': 18, 'width': 18}    )
		self.assertEqual(image_processor.do_reduce_labels ,					__SCREAMING_SNAKE_CASE    )

		lowerCAmelCase					:    Union[str, Any]      							= self.image_processing_class.from_dict(
		    self.image_processor_dict ,					size=42 ,					crop_size=84 ,					reduce_labels=__SCREAMING_SNAKE_CASE    )
		self.assertEqual(image_processor.size ,					{'height': 42, 'width': 42}    )
		self.assertEqual(image_processor.crop_size ,					{'height': 84, 'width': 84}    )
		self.assertEqual(image_processor.do_reduce_labels ,					__SCREAMING_SNAKE_CASE    )



	def 				lowercase     (					self    ):
		pass



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.image_processing_class(**self.image_processor_dict    )
		# create random PIL images
		lowerCAmelCase					:    Any      							= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__SCREAMING_SNAKE_CASE    )
		for image in image_inputs:
			self.assertIsInstance(__SCREAMING_SNAKE_CASE ,					Image.Image    )

		# Test not batched input
		lowerCAmelCase					:    Optional[Any]      							= image_processing(image_inputs[0] ,					return_tensors='pt'    ).pixel_values
		self.assertEqual(
		    encoded_images.shape ,					(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)

		# Test batched
		lowerCAmelCase					:    Any      							= image_processing(__SCREAMING_SNAKE_CASE ,					return_tensors='pt'    ).pixel_values
		self.assertEqual(
		    encoded_images.shape ,					(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= self.image_processing_class(**self.image_processor_dict    )
		# create random numpy tensors
		lowerCAmelCase					:    List[Any]      							= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__SCREAMING_SNAKE_CASE ,					numpify=__SCREAMING_SNAKE_CASE    )
		for image in image_inputs:
			self.assertIsInstance(__SCREAMING_SNAKE_CASE ,					np.ndarray    )

		# Test not batched input
		lowerCAmelCase					:    Union[str, Any]      							= image_processing(image_inputs[0] ,					return_tensors='pt'    ).pixel_values
		self.assertEqual(
		    encoded_images.shape ,					(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)

		# Test batched
		lowerCAmelCase					:    int      							= image_processing(__SCREAMING_SNAKE_CASE ,					return_tensors='pt'    ).pixel_values
		self.assertEqual(
		    encoded_images.shape ,					(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.image_processing_class(**self.image_processor_dict    )
		# create random PyTorch tensors
		lowerCAmelCase					:    Any      							= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__SCREAMING_SNAKE_CASE ,					torchify=__SCREAMING_SNAKE_CASE    )
		for image in image_inputs:
			self.assertIsInstance(__SCREAMING_SNAKE_CASE ,					torch.Tensor    )

		# Test not batched input
		lowerCAmelCase					:    Dict      							= image_processing(image_inputs[0] ,					return_tensors='pt'    ).pixel_values
		self.assertEqual(
		    encoded_images.shape ,					(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)

		# Test batched
		lowerCAmelCase					:    Tuple      							= image_processing(__SCREAMING_SNAKE_CASE ,					return_tensors='pt'    ).pixel_values
		self.assertEqual(
		    encoded_images.shape ,					(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.image_processing_class(**self.image_processor_dict    )
		# create random PyTorch tensors
		lowerCAmelCase					:    List[Any]      							= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__SCREAMING_SNAKE_CASE ,					torchify=__SCREAMING_SNAKE_CASE    )
		lowerCAmelCase					:    List[str]      							= []
		for image in image_inputs:
			self.assertIsInstance(__SCREAMING_SNAKE_CASE ,					torch.Tensor    )
			maps.append(torch.zeros(image.shape[-2:]    ).long()    )

		# Test not batched input
		lowerCAmelCase					:    List[Any]      							= image_processing(image_inputs[0] ,					maps[0] ,					return_tensors='pt'    )
		self.assertEqual(
		    encoding['pixel_values'].shape ,					(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)
		self.assertEqual(
		    encoding['labels'].shape ,					(
		        1,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)
		self.assertEqual(encoding['labels'].dtype ,					torch.long    )
		self.assertTrue(encoding['labels'].min().item() >= 0    )
		self.assertTrue(encoding['labels'].max().item() <= 255    )

		# Test batched
		lowerCAmelCase					:    List[Any]      							= image_processing(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_tensors='pt'    )
		self.assertEqual(
		    encoding['pixel_values'].shape ,					(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)
		self.assertEqual(
		    encoding['labels'].shape ,					(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)
		self.assertEqual(encoding['labels'].dtype ,					torch.long    )
		self.assertTrue(encoding['labels'].min().item() >= 0    )
		self.assertTrue(encoding['labels'].max().item() <= 255    )

		# Test not batched input (PIL images)
		lowerCAmelCase							,   lowerCAmelCase					:    Optional[int]      							= prepare_semantic_single_inputs()

		lowerCAmelCase					:    Union[str, Any]      							= image_processing(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_tensors='pt'    )
		self.assertEqual(
		    encoding['pixel_values'].shape ,					(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)
		self.assertEqual(
		    encoding['labels'].shape ,					(
		        1,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)
		self.assertEqual(encoding['labels'].dtype ,					torch.long    )
		self.assertTrue(encoding['labels'].min().item() >= 0    )
		self.assertTrue(encoding['labels'].max().item() <= 255    )

		# Test batched input (PIL images)
		lowerCAmelCase							,   lowerCAmelCase					:    Tuple      							= prepare_semantic_batch_inputs()

		lowerCAmelCase					:    Tuple      							= image_processing(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_tensors='pt'    )
		self.assertEqual(
		    encoding['pixel_values'].shape ,					(
		        2,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)
		self.assertEqual(
		    encoding['labels'].shape ,					(
		        2,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    ) ,					)
		self.assertEqual(encoding['labels'].dtype ,					torch.long    )
		self.assertTrue(encoding['labels'].min().item() >= 0    )
		self.assertTrue(encoding['labels'].max().item() <= 255    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self.image_processing_class(**self.image_processor_dict    )

		# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
		lowerCAmelCase							,   lowerCAmelCase					:    List[Any]      							= prepare_semantic_single_inputs()
		lowerCAmelCase					:    int      							= image_processing(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_tensors='pt'    )
		self.assertTrue(encoding['labels'].min().item() >= 0    )
		self.assertTrue(encoding['labels'].max().item() <= 150    )

		lowerCAmelCase					:    List[Any]      							= True
		lowerCAmelCase					:    int      							= image_processing(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_tensors='pt'    )
		self.assertTrue(encoding['labels'].min().item() >= 0    )
		self.assertTrue(encoding['labels'].max().item() <= 255    )




'''simple docstring'''




import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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,
				    MobileBertForMaskedLM,
				    MobileBertForMultipleChoice,
				    MobileBertForNextSentencePrediction,
				    MobileBertForPreTraining,
				    MobileBertForQuestionAnswering,
				    MobileBertForSequenceClassification,
				    MobileBertForTokenClassification,
				    MobileBertModel,
				)



class  lowerCAmelCase				:



	def __init__(					self ,					snake_case__ ,					snake_case__=13 ,					snake_case__=7 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=99 ,					snake_case__=64 ,					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__=0.0_2 ,					snake_case__=3 ,					snake_case__=4 ,					snake_case__=None ,					):
		lowerCAmelCase					:    str      							= parent
		lowerCAmelCase					:    Optional[int]      							= batch_size
		lowerCAmelCase					:    Optional[Any]      							= seq_length
		lowerCAmelCase					:    Optional[Any]      							= is_training
		lowerCAmelCase					:    Dict      							= use_input_mask
		lowerCAmelCase					:    Tuple      							= use_token_type_ids
		lowerCAmelCase					:    int      							= use_labels
		lowerCAmelCase					:    int      							= vocab_size
		lowerCAmelCase					:    Any      							= hidden_size
		lowerCAmelCase					:    Optional[Any]      							= embedding_size
		lowerCAmelCase					:    int      							= num_hidden_layers
		lowerCAmelCase					:    List[str]      							= num_attention_heads
		lowerCAmelCase					:    List[Any]      							= intermediate_size
		lowerCAmelCase					:    Dict      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    int      							= attention_probs_dropout_prob
		lowerCAmelCase					:    List[Any]      							= max_position_embeddings
		lowerCAmelCase					:    int      							= type_vocab_size
		lowerCAmelCase					:    List[str]      							= type_sequence_label_size
		lowerCAmelCase					:    Dict      							= initializer_range
		lowerCAmelCase					:    Any      							= num_labels
		lowerCAmelCase					:    str      							= num_choices
		lowerCAmelCase					:    int      							= scope



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size    )

		lowerCAmelCase					:    Union[str, Any]      							= None
		if self.use_input_mask:
			lowerCAmelCase					:    Dict      							= random_attention_mask([self.batch_size, self.seq_length]    )

		lowerCAmelCase					:    Optional[int]      							= None
		if self.use_token_type_ids:
			lowerCAmelCase					:    List[str]      							= ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size    )

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

		lowerCAmelCase					:    Tuple      							= self.get_config()

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



	def 				lowercase     (					self    ):
		return MobileBertConfig(
		    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 ,					embedding_size=self.embedding_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 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[int]      							= MobileBertModel(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    int      							= model(snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= model(snake_case__ ,					token_type_ids=snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= model(snake_case__    )

		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 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    int      							= MobileBertForMaskedLM(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    str      							= 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 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= MobileBertForNextSentencePrediction(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    str      							= model(
		    snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					labels=snake_case__ ,					)
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, 2)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    List[Any]      							= MobileBertForPreTraining(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Tuple      							= model(
		    snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					labels=snake_case__ ,					next_sentence_label=snake_case__ ,					)
		self.parent.assertEqual(result.prediction_logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)    )
		self.parent.assertEqual(result.seq_relationship_logits.shape ,					(self.batch_size, 2)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= MobileBertForQuestionAnswering(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    List[str]      							= model(
		    snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					start_positions=snake_case__ ,					end_positions=snake_case__ ,					)
		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 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Optional[int]      							= self.num_labels
		lowerCAmelCase					:    List[Any]      							= MobileBertForSequenceClassification(snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Union[str, 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.num_labels)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.num_labels
		lowerCAmelCase					:    int      							= MobileBertForTokenClassification(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Union[str, 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.num_labels)    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    List[str]      							= self.num_choices
		lowerCAmelCase					:    Any      							= MobileBertForMultipleChoice(config=snake_case__    )
		model.to(snake_case__    )
		model.eval()
		lowerCAmelCase					:    Dict      							= input_ids.unsqueeze(1    ).expand(-1 ,					self.num_choices ,					-1    ).contiguous()
		lowerCAmelCase					:    int      							= token_type_ids.unsqueeze(1    ).expand(-1 ,					self.num_choices ,					-1    ).contiguous()
		lowerCAmelCase					:    Union[str, Any]      							= input_mask.unsqueeze(1    ).expand(-1 ,					self.num_choices ,					-1    ).contiguous()
		lowerCAmelCase					:    List[str]      							= 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.num_choices)    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.prepare_config_and_inputs()
		(
		    (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   (
		    lowerCAmelCase
		)							,   
		)					:    Optional[Any]      							= config_and_inputs
		lowerCAmelCase					:    List[Any]      							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
		return config, inputs_dict



@require_torch
class  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[str]			      = (
	    (
	        MobileBertModel,
	        MobileBertForMaskedLM,
	        MobileBertForMultipleChoice,
	        MobileBertForNextSentencePrediction,
	        MobileBertForPreTraining,
	        MobileBertForQuestionAnswering,
	        MobileBertForSequenceClassification,
	        MobileBertForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowerCamelCase   :					Tuple			      = (
	    {
	        """feature-extraction""": MobileBertModel,
	        """fill-mask""": MobileBertForMaskedLM,
	        """question-answering""": MobileBertForQuestionAnswering,
	        """text-classification""": MobileBertForSequenceClassification,
	        """token-classification""": MobileBertForTokenClassification,
	        """zero-shot""": MobileBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowerCamelCase   :					str			      = True



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__=False    ):
		lowerCAmelCase					:    int      							= super()._prepare_for_class(snake_case__ ,					snake_case__ ,					return_labels=snake_case__    )

		if return_labels:
			if model_class in get_values(snake_case__    ):
				lowerCAmelCase					:    str      							= torch.zeros(
				    (self.model_tester.batch_size, self.model_tester.seq_length) ,					dtype=torch.long ,					device=snake_case__    )
				lowerCAmelCase					:    Tuple      							= torch.zeros(
				    self.model_tester.batch_size ,					dtype=torch.long ,					device=snake_case__    )
		return inputs_dict



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= MobileBertModelTester(self    )
		lowerCAmelCase					:    Dict      							= ConfigTester(self ,					config_class=snake_case__ ,					hidden_size=37    )



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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_model(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    int      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case__    )


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




	"""simple docstring"""





	return torch.tensor(
	    _A					,   dtype=torch.long					,   device=_A					,   )


_lowerCAmelCase  :		Union[str, Any]		     =	1E-3






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				unittest.TestCase							):



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= MobileBertModel.from_pretrained('google/mobilebert-uncased'    ).to(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]    )
		with torch.no_grad():
			lowerCAmelCase					:    Tuple      							= model(snake_case__    )[0]
		lowerCAmelCase					:    List[Any]      							= torch.Size((1, 9, 512)    )
		self.assertEqual(output.shape ,					snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= torch.tensor(
		    [
		        [
		            [-2.4_7_3_6_5_2_6e0_7, 8.2_6_9_1_6_5_6e0_4, 1.6_5_2_1_8_3_8e0_5],
		            [-5.7_5_4_1_7_0_4e-0_1, 3.9_0_5_6_0_2_2e0_0, 4.4_0_1_1_5_0_7e0_0],
		            [2.6_0_4_7_3_5_9e0_0, 1.5_6_7_7_6_5_2e0_0, -1.7_3_2_4_1_8_8e-0_1],
		        ]
		    ] ,					device=snake_case__ ,					)

		# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
		# ~1 difference, it's therefore not a good idea to measure using addition.
		# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
		# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
		lowerCAmelCase					:    List[str]      							= torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE    )
		lowerCAmelCase					:    Dict      							= torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE    )

		self.assertTrue(lower_bound and upper_bound    )
