
"""simple docstring"""







import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							str							,						snake_case_   :							Tuple							,						snake_case_   :							List[str]							,						snake_case_   :							Optional[Any]    ):
												__snake_case     			=       dataset
												__snake_case     			=       process
												__snake_case     			=       params





					def __len__(				self   :							List[Any]    ):
												return len(self.dataset    )





					def __getitem__(				self   :							Dict							,						snake_case_   :							Optional[int]    ):
												__snake_case     			=       self.dataset[i]
												__snake_case     			=       self.process(snake_case_							,						**self.params    )
												return processed







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Optional[int]							,						snake_case_   :							Any							,						snake_case_   :							str							,						snake_case_   :							Tuple							,						snake_case_   :							Dict=None    ):
												__snake_case     			=       loader
												__snake_case     			=       infer
												__snake_case     			=       params
												if loader_batch_size == 1:
																			# Let's spare some time by deactivating altogether
																			__snake_case     			=       None
												__snake_case     			=       loader_batch_size

												# Internal bookkeeping
												__snake_case     			=       None
												__snake_case     			=       None





					def __len__(				self   :							Union[str, Any]    ):
												return len(self.loader    )





					def __iter__(				self   :							List[Any]    ):
												__snake_case     			=       iter(self.loader    )
												return self





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												if isinstance(self._loader_batch_data							,						torch.Tensor    ):
																			# Batch data is simple tensor, just fetch the slice
																			__snake_case     			=       self._loader_batch_data[self._loader_batch_index]
												else:
																			# Batch data is assumed to be BaseModelOutput (or dict)
																			__snake_case     			=       {}
																			for k, element in self._loader_batch_data.items():
																										if isinstance(snake_case_							,						snake_case_    ):
																																	# Convert ModelOutput to tuple first
																																	__snake_case     			=       element.to_tuple()
																																	if isinstance(element[0]							,						torch.Tensor    ):
																																								__snake_case     			=       tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																																	elif isinstance(element[0]							,						np.ndarray    ):
																																								__snake_case     			=       tuple(np.expand_dims(el[self._loader_batch_index]							,						0    ) for el in element    )
																																	continue
																										if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case_							,						snake_case_    ):
																																	# Those are stored as lists of tensors so need specific unbatching.
																																	if isinstance(element[0]							,						torch.Tensor    ):
																																								__snake_case     			=       tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																																	elif isinstance(element[0]							,						np.ndarray    ):
																																								__snake_case     			=       tuple(np.expand_dims(el[self._loader_batch_index]							,						0    ) for el in element    )
																																	continue
																										if element is None:
																																	# This can happen for optional data that get passed around
																																	__snake_case     			=       None
																										elif isinstance(element[self._loader_batch_index]							,						torch.Tensor    ):
																																	# Take correct batch data, but make it looked like batch_size=1
																																	# For compatibility with other methods within transformers

																																	__snake_case     			=       element[self._loader_batch_index].unsqueeze(0    )
																										elif isinstance(element[self._loader_batch_index]							,						np.ndarray    ):
																																	# Take correct batch data, but make it looked like batch_size=1
																																	# For compatibility with other methods within transformers
																																	__snake_case     			=       np.expand_dims(element[self._loader_batch_index]							,						0    )
																										else:
																																	# This is typically a list, so no need to `unsqueeze`.
																																	__snake_case     			=       element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																			__snake_case     			=       self._loader_batch_data.__class__(snake_case_    )
												self._loader_batch_index += 1
												return result





					def 							lowerCAmelCase      (				self   :							str    ):
												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																			# We are currently unrolling a batch so we just need to return
																			# the current item within a batch
																			return self.loader_batch_item()

												# We're out of items within a batch
												__snake_case     			=       next(self.iterator    )
												__snake_case     			=       self.infer(snake_case_							,						**self.params    )
												# We now have a batch of "inferred things".
												if self.loader_batch_size is not None:
																			# Try to infer the size of the batch
																			if isinstance(snake_case_							,						torch.Tensor    ):
																										__snake_case     			=       processed
																			else:
																										__snake_case     			=       list(processed.keys()    )[0]
																										__snake_case     			=       processed[key]
																			if isinstance(snake_case_							,						snake_case_    ):
																										__snake_case     			=       len(snake_case_    )
																			else:
																										__snake_case     			=       first_tensor.shape[0]
																			if 0 < observed_batch_size < self.loader_batch_size:
																										# could be last batch so we can't unroll as many
																										# elements.
																										__snake_case     			=       observed_batch_size
																			# Setting internal index to unwrap the batch
																			__snake_case     			=       processed
																			__snake_case     			=       0
																			return self.loader_batch_item()
												else:
																			# We're not unrolling batches
																			return processed







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Any							,						snake_case_   :							List[Any]							,						snake_case_   :							List[str]							,						snake_case_   :							Any							,						snake_case_   :							Union[str, Any]=None    ):
												super().__init__(snake_case_							,						snake_case_							,						snake_case_    )





					def __iter__(				self   :							Dict    ):
												__snake_case     			=       iter(self.loader    )
												__snake_case     			=       None
												return self





					def 							lowerCAmelCase      (				self   :							Dict    ):
												if self.subiterator is None:
																			__snake_case     			=       self.infer(next(self.iterator    )							,						**self.params    )
												try:
																			# Try to return next item
																			__snake_case     			=       next(self.subiterator    )
												except StopIteration:
																			# When a preprocess iterator ends, we can start lookig at the next item
																			# ChunkIterator will keep feeding until ALL elements of iterator
																			# all have created their subiterator and have been iterating against.
																			#
																			# Another way to look at it, is we're basically flattening lists of lists
																			# into a single list, but with generators
																			__snake_case     			=       self.infer(next(self.iterator    )							,						**self.params    )
																			__snake_case     			=       next(self.subiterator    )
												return processed







class 							__magic_name__     (		lowercase__   ):





					def __iter__(				self   :							int    ):
												__snake_case     			=       iter(self.loader    )
												return self





					def 							lowerCAmelCase      (				self   :							Dict    ):
												# Extremely similar to PipelineIterator in its unpacking mechanism
												# BUT, we have an extra required item which is the presence of `is_last`
												# That is because everything is flattened by `PipelineChunkIterator` we
												# need to keep track of how to regroup here in the original `process`
												# boundaries so that `process` and `postprocess` see the same data.

												# This iterator accumulates items (possibly while unbatching) until it
												# its a `is_last` and then just passes it on to the caller.
												__snake_case     			=       False
												__snake_case     			=       []
												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																			while self._loader_batch_index < self.loader_batch_size:
																										__snake_case     			=       self.loader_batch_item()
																										__snake_case     			=       item.pop("is_last"    )
																										accumulator.append(snake_case_    )
																										if is_last:
																																	return accumulator

												while not is_last:
																			__snake_case     			=       self.infer(next(self.iterator    )							,						**self.params    )
																			if self.loader_batch_size is not None:
																										if isinstance(snake_case_							,						torch.Tensor    ):
																																	__snake_case     			=       processed
																										else:
																																	__snake_case     			=       list(processed.keys()    )[0]
																																	__snake_case     			=       processed[key]
																										if isinstance(snake_case_							,						snake_case_    ):
																																	__snake_case     			=       len(snake_case_    )
																										else:
																																	__snake_case     			=       first_tensor.shape[0]
																										if 0 < observed_batch_size < self.loader_batch_size:
																																	# could be last batch so we can't unroll as many
																																	# elements.
																																	__snake_case     			=       observed_batch_size
																										__snake_case     			=       processed
																										__snake_case     			=       0
																										while self._loader_batch_index < self.loader_batch_size:
																																	__snake_case     			=       self.loader_batch_item()
																																	__snake_case     			=       item.pop("is_last"    )
																																	accumulator.append(snake_case_    )
																																	if is_last:
																																								return accumulator
																			else:
																										__snake_case     			=       processed
																										__snake_case     			=       item.pop("is_last"    )
																										accumulator.append(snake_case_    )
												return accumulator







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							str							,						snake_case_   :							Dataset							,						snake_case_   :							str    ):
												__snake_case     			=       dataset
												__snake_case     			=       key





					def __len__(				self   :							int    ):
												return len(self.dataset    )





					def __getitem__(				self   :							List[Any]							,						snake_case_   :							Optional[Any]    ):
												return self.dataset[i][self.key]







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Optional[int]							,						snake_case_   :							Dataset							,						snake_case_   :							str							,						snake_case_   :							str    ):
												__snake_case     			=       dataset
												__snake_case     			=       keya
												__snake_case     			=       keya





					def __len__(				self   :							Any    ):
												return len(self.dataset    )





					def __getitem__(				self   :							Optional[int]							,						snake_case_   :							List[Any]    ):
												return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}


"""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 							__magic_name__     :





					def __init__(				self   :							Optional[int]							,						snake_case_   :							List[str]							,						snake_case_   :							str=13							,						snake_case_   :							Optional[int]=7							,						snake_case_   :							Optional[Any]=True							,						snake_case_   :							Union[str, Any]=True							,						snake_case_   :							Tuple=True							,						snake_case_   :							int=True							,						snake_case_   :							int=99							,						snake_case_   :							Optional[int]=64							,						snake_case_   :							Dict=32							,						snake_case_   :							Dict=5							,						snake_case_   :							List[str]=4							,						snake_case_   :							List[Any]=37							,						snake_case_   :							Optional[Any]="gelu"							,						snake_case_   :							Optional[Any]=0.1							,						snake_case_   :							List[Any]=0.1							,						snake_case_   :							Union[str, Any]=512							,						snake_case_   :							int=16							,						snake_case_   :							List[str]=2							,						snake_case_   :							Union[str, Any]=0.02							,						snake_case_   :							Union[str, Any]=3							,						snake_case_   :							str=4							,						snake_case_   :							int=None							,						):
												__snake_case     			=       parent
												__snake_case     			=       batch_size
												__snake_case     			=       seq_length
												__snake_case     			=       is_training
												__snake_case     			=       use_input_mask
												__snake_case     			=       use_token_type_ids
												__snake_case     			=       use_labels
												__snake_case     			=       vocab_size
												__snake_case     			=       hidden_size
												__snake_case     			=       embedding_size
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       num_attention_heads
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_act
												__snake_case     			=       hidden_dropout_prob
												__snake_case     			=       attention_probs_dropout_prob
												__snake_case     			=       max_position_embeddings
												__snake_case     			=       type_vocab_size
												__snake_case     			=       type_sequence_label_size
												__snake_case     			=       initializer_range
												__snake_case     			=       num_labels
												__snake_case     			=       num_choices
												__snake_case     			=       scope





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size    )

												__snake_case     			=       None
												if self.use_input_mask:
																			__snake_case     			=       random_attention_mask([self.batch_size, self.seq_length]    )

												__snake_case     			=       None
												if self.use_token_type_ids:
																			__snake_case     			=       ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size    )

												__snake_case     			=       None
												__snake_case     			=       None
												__snake_case     			=       None
												if self.use_labels:
																			__snake_case     			=       ids_tensor([self.batch_size]							,						self.type_sequence_label_size    )
																			__snake_case     			=       ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels    )
																			__snake_case     			=       ids_tensor([self.batch_size]							,						self.num_choices    )

												__snake_case     			=       self.get_config()

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





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												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 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Tuple							,						snake_case_   :							Any							,						snake_case_   :							str							,						snake_case_   :							Dict							,						snake_case_   :							List[str]    ):
												__snake_case     			=       MobileBertModel(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       model(snake_case_							,						attention_mask=snake_case_							,						token_type_ids=snake_case_    )
												__snake_case     			=       model(snake_case_							,						token_type_ids=snake_case_    )
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							List[Any]							,						snake_case_   :							Any							,						snake_case_   :							Optional[int]							,						snake_case_   :							int							,						snake_case_   :							int							,						snake_case_   :							str							,						snake_case_   :							str    ):
												__snake_case     			=       MobileBertForMaskedLM(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							Optional[int]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							List[str]							,						snake_case_   :							int							,						snake_case_   :							int							,						snake_case_   :							int							,						snake_case_   :							Any    ):
												__snake_case     			=       MobileBertForNextSentencePrediction(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]							,						snake_case_   :							str							,						snake_case_   :							Optional[Any]							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]							,						snake_case_   :							Dict    ):
												__snake_case     			=       MobileBertForPreTraining(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							List[str]							,						snake_case_   :							int							,						snake_case_   :							Any							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Dict							,						snake_case_   :							int							,						snake_case_   :							int    ):
												__snake_case     			=       MobileBertForQuestionAnswering(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							Tuple							,						snake_case_   :							int							,						snake_case_   :							str							,						snake_case_   :							Dict							,						snake_case_   :							Optional[int]							,						snake_case_   :							str							,						snake_case_   :							List[str]    ):
												__snake_case     			=       self.num_labels
												__snake_case     			=       MobileBertForSequenceClassification(snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							Tuple							,						snake_case_   :							str							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[int]							,						snake_case_   :							Tuple							,						snake_case_   :							List[Any]    ):
												__snake_case     			=       self.num_labels
												__snake_case     			=       MobileBertForTokenClassification(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Any							,						snake_case_   :							Any							,						snake_case_   :							Dict							,						snake_case_   :							Union[str, Any]    ):
												__snake_case     			=       self.num_choices
												__snake_case     			=       MobileBertForMultipleChoice(config=snake_case_    )
												model.to(snake_case_    )
												model.eval()
												__snake_case     			=       input_ids.unsqueeze(1    ).expand(-1							,						self.num_choices							,						-1    ).contiguous()
												__snake_case     			=       token_type_ids.unsqueeze(1    ).expand(-1							,						self.num_choices							,						-1    ).contiguous()
												__snake_case     			=       input_mask.unsqueeze(1    ).expand(-1							,						self.num_choices							,						-1    ).contiguous()
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       self.prepare_config_and_inputs()
												(
												    (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   
												)     			=       config_and_inputs
												__snake_case     			=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
												return config, inputs_dict







@require_torch
class 							__magic_name__     (		lowercase__      ,		lowercase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					Dict     							=   (
					    (
					        MobileBertModel,
					        MobileBertForMaskedLM,
					        MobileBertForMultipleChoice,
					        MobileBertForNextSentencePrediction,
					        MobileBertForPreTraining,
					        MobileBertForQuestionAnswering,
					        MobileBertForSequenceClassification,
					        MobileBertForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   (
					    {
					        'feature-extraction': MobileBertModel,
					        'fill-mask': MobileBertForMaskedLM,
					        'question-answering': MobileBertForQuestionAnswering,
					        'text-classification': MobileBertForSequenceClassification,
					        'token-classification': MobileBertForTokenClassification,
					        'zero-shot': MobileBertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					_SCREAMING_SNAKE_CASE       :					int     							=   True





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							List[str]							,						snake_case_   :							str							,						snake_case_   :							str=False    ):
												__snake_case     			=       super()._prepare_for_class(snake_case_							,						snake_case_							,						return_labels=snake_case_    )

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





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       MobileBertModelTester(self    )
												__snake_case     			=       ConfigTester(self							,						config_class=snake_case_							,						hidden_size=37    )





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												self.config_tester.run_common_tests()





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_model(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_    )




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


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


_SCREAMING_SNAKE_CASE	       =      1E-3







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





					@slow
					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       MobileBertModel.from_pretrained("google/mobilebert-uncased"    ).to(snake_case_    )
												__snake_case     			=       _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]    )
												with torch.no_grad():
																			__snake_case     			=       model(snake_case_    )[0]
												__snake_case     			=       torch.Size((1, 9, 512)    )
												self.assertEqual(output.shape							,						snake_case_    )
												__snake_case     			=       torch.tensor(
												    [
												        [
												            [-2.4_73_65_26e07, 8.2_69_16_56e04, 1.6_52_18_38e05],
												            [-5.7_54_17_04e-01, 3.9_05_60_22e00, 4.4_01_15_07e00],
												            [2.6_04_73_59e00, 1.5_67_76_52e00, -1.7_32_41_88e-01],
												        ]
												    ]							,						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
												__snake_case     			=       torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE    )
												__snake_case     			=       torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE    )

												self.assertTrue(lower_bound and upper_bound    )
