



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 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  Tuple, _snake_case  :  int, _snake_case  :  Union[str, Any]=1_3, _snake_case  :  int=7, _snake_case  :  Tuple=True, _snake_case  :  Union[str, Any]=True, _snake_case  :  Dict=True, _snake_case  :  Optional[Any]=True, _snake_case  :  Tuple=9_9, _snake_case  :  Any=6_4, _snake_case  :  str=3_2, _snake_case  :  Optional[int]=5, _snake_case  :  Any=4, _snake_case  :  str=3_7, _snake_case  :  List[Any]="gelu", _snake_case  :  Any=0.1, _snake_case  :  List[str]=0.1, _snake_case  :  Union[str, Any]=5_1_2, _snake_case  :  int=1_6, _snake_case  :  Tuple=2, _snake_case  :  Union[str, Any]=0.0_2, _snake_case  :  str=3, _snake_case  :  str=4, _snake_case  :  Optional[int]=None, )   ->List[str]:
            snake_case__							:	List[Any]   	=	parent
            snake_case__							:	str   	=	batch_size
            snake_case__							:	List[str]   	=	seq_length
            snake_case__							:	Dict   	=	is_training
            snake_case__							:	int   	=	use_input_mask
            snake_case__							:	List[Any]   	=	use_token_type_ids
            snake_case__							:	int   	=	use_labels
            snake_case__							:	str   	=	vocab_size
            snake_case__							:	str   	=	hidden_size
            snake_case__							:	Any   	=	embedding_size
            snake_case__							:	Any   	=	num_hidden_layers
            snake_case__							:	int   	=	num_attention_heads
            snake_case__							:	Dict   	=	intermediate_size
            snake_case__							:	Union[str, Any]   	=	hidden_act
            snake_case__							:	List[Any]   	=	hidden_dropout_prob
            snake_case__							:	Union[str, Any]   	=	attention_probs_dropout_prob
            snake_case__							:	Any   	=	max_position_embeddings
            snake_case__							:	List[str]   	=	type_vocab_size
            snake_case__							:	List[Any]   	=	type_sequence_label_size
            snake_case__							:	Optional[Any]   	=	initializer_range
            snake_case__							:	str   	=	num_labels
            snake_case__							:	int   	=	num_choices
            snake_case__							:	Any   	=	scope






     def    lowercase_   (       self  :  int					)   ->Optional[Any]:
            snake_case__							:	Union[str, Any]   	=	ids_tensor([self.batch_size, self.seq_length], self.vocab_size					)

            snake_case__							:	int   	=	None
            if self.use_input_mask:
                   snake_case__							:	Optional[Any]   	=	random_attention_mask([self.batch_size, self.seq_length]					)

            snake_case__							:	Optional[Any]   	=	None
            if self.use_token_type_ids:
                   snake_case__							:	Union[str, Any]   	=	ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size					)

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

            snake_case__							:	Any   	=	self.get_config()

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






     def    lowercase_   (       self  :  Optional[Any]					)   ->List[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    lowercase_   (       self  :  Any, _snake_case  :  Union[str, Any], _snake_case  :  Optional[int], _snake_case  :  str, _snake_case  :  List[str], _snake_case  :  List[Any], _snake_case  :  List[Any], _snake_case  :  List[str]					)   ->Tuple:
            snake_case__							:	Union[str, Any]   	=	MobileBertModel(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Optional[int]   	=	model(_snake_case, attention_mask=_snake_case, token_type_ids=_snake_case					)
            snake_case__							:	Union[str, Any]   	=	model(_snake_case, token_type_ids=_snake_case					)
            snake_case__							:	List[str]   	=	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  :  Optional[Any], _snake_case  :  Optional[Any], _snake_case  :  List[Any], _snake_case  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  Dict					)   ->int:
            snake_case__							:	Union[str, Any]   	=	MobileBertForMaskedLM(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Optional[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.vocab_size)					)






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  List[str], _snake_case  :  Tuple, _snake_case  :  List[str], _snake_case  :  List[str], _snake_case  :  Optional[int], _snake_case  :  Union[str, Any], _snake_case  :  Optional[int]					)   ->List[Any]:
            snake_case__							:	List[Any]   	=	MobileBertForNextSentencePrediction(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	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, 2)					)






     def    lowercase_   (       self  :  str, _snake_case  :  str, _snake_case  :  Union[str, Any], _snake_case  :  Any, _snake_case  :  Any, _snake_case  :  int, _snake_case  :  Dict, _snake_case  :  Any					)   ->Union[str, Any]:
            snake_case__							:	str   	=	MobileBertForPreTraining(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	int   	=	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  :  List[str], _snake_case  :  List[str], _snake_case  :  Optional[Any], _snake_case  :  List[Any], _snake_case  :  int, _snake_case  :  List[Any], _snake_case  :  Optional[Any], _snake_case  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Any   	=	MobileBertForQuestionAnswering(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	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  :  List[Any], _snake_case  :  Optional[int], _snake_case  :  Tuple, _snake_case  :  str, _snake_case  :  Union[str, Any], _snake_case  :  Union[str, Any], _snake_case  :  Optional[Any], _snake_case  :  List[str]					)   ->Any:
            snake_case__							:	Optional[int]   	=	self.num_labels
            snake_case__							:	List[str]   	=	MobileBertForSequenceClassification(_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	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_labels)					)






     def    lowercase_   (       self  :  int, _snake_case  :  List[Any], _snake_case  :  Union[str, Any], _snake_case  :  Dict, _snake_case  :  int, _snake_case  :  Union[str, Any], _snake_case  :  List[Any], _snake_case  :  List[Any]					)   ->Optional[Any]:
            snake_case__							:	Dict   	=	self.num_labels
            snake_case__							:	Optional[int]   	=	MobileBertForTokenClassification(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Optional[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  :  Optional[int], _snake_case  :  Optional[Any], _snake_case  :  str, _snake_case  :  Union[str, Any], _snake_case  :  Union[str, Any], _snake_case  :  int, _snake_case  :  Dict, _snake_case  :  Tuple					)   ->Optional[Any]:
            snake_case__							:	List[str]   	=	self.num_choices
            snake_case__							:	Optional[int]   	=	MobileBertForMultipleChoice(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Optional[int]   	=	input_ids.unsqueeze(1					).expand(-1, self.num_choices, -1					).contiguous()
            snake_case__							:	List[Any]   	=	token_type_ids.unsqueeze(1					).expand(-1, self.num_choices, -1					).contiguous()
            snake_case__							:	List[str]   	=	input_mask.unsqueeze(1					).expand(-1, self.num_choices, -1					).contiguous()
            snake_case__							:	Tuple   	=	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  :  Union[str, Any]					)   ->str:
            snake_case__							:	Tuple   	=	self.prepare_config_and_inputs()
            (
                snake_case__ 
            )							:	Union[str, Any]   	=	config_and_inputs
            snake_case__							:	Tuple   	=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict




@require_torch
class 					snake_case__						(       lowerCAmelCase_				,     lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	(
         (
             MobileBertModel,
             MobileBertForMaskedLM,
             MobileBertForMultipleChoice,
             MobileBertForNextSentencePrediction,
             MobileBertForPreTraining,
             MobileBertForQuestionAnswering,
             MobileBertForSequenceClassification,
             MobileBertForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE						=	(
         {
             """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						=	True






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Optional[Any], _snake_case  :  List[str], _snake_case  :  Tuple=False					)   ->List[Any]:
            snake_case__							:	Optional[int]   	=	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__							:	Tuple   	=	torch.zeros(
                              (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=_snake_case					)
                          snake_case__							:	Dict   	=	torch.zeros(
                              self.model_tester.batch_size, dtype=torch.long, device=_snake_case					)
            return inputs_dict






     def    lowercase_   (       self  :  List[Any]					)   ->Tuple:
            snake_case__							:	int   	=	MobileBertModelTester(self					)
            snake_case__							:	Union[str, Any]   	=	ConfigTester(self, config_class=_snake_case, hidden_size=3_7					)






     def    lowercase_   (       self  :  Dict					)   ->Optional[int]:
            self.config_tester.run_common_tests()






     def    lowercase_   (       self  :  int					)   ->str:
            snake_case__							:	List[str]   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_model(*_snake_case					)






     def    lowercase_   (       self  :  List[Any]					)   ->List[Any]:
            snake_case__							:	int   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_masked_lm(*_snake_case					)






     def    lowercase_   (       self  :  Dict					)   ->List[str]:
            snake_case__							:	Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_snake_case					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Union[str, Any]:
            snake_case__							:	List[Any]   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_snake_case					)






     def    lowercase_   (       self  :  str					)   ->str:
            snake_case__							:	Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_pretraining(*_snake_case					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Tuple   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_question_answering(*_snake_case					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->str:
            snake_case__							:	List[str]   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_snake_case					)






     def    lowercase_   (       self  :  Optional[int]					)   ->List[str]:
            snake_case__							:	Dict   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_token_classification(*_snake_case					)


def        lowercase_							(A : Any      ):
       return torch.tensor(
           lowercase__		,					dtype=torch.long		,					device=lowercase__		,					)


a_   :Any        =      1e-3





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






     """simple docstring"""


     @slow
     def    lowercase_   (       self  :  List[str]					)   ->Union[str, Any]:
            snake_case__							:	Optional[int]   	=	MobileBertModel.from_pretrained('google/mobilebert-uncased'					).to(_snake_case					)
            snake_case__							:	Tuple   	=	_long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]					)
            with torch.no_grad():
                   snake_case__							:	Any   	=	model(_snake_case					)[0]
            snake_case__							:	Tuple   	=	torch.Size((1, 9, 5_1_2)					)
            self.assertEqual(output.shape, _snake_case					)
            snake_case__							:	List[Any]   	=	torch.tensor(
                [
                    [
                        [-2.4736526e07, 8.2691656e04, 1.6521838e05],
                        [-5.7541704e-01, 3.9056022e00, 4.4011507e00],
                        [2.6047359e00, 1.5677652e00, -1.7324188e-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__							:	Any   	=	torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE					)
            snake_case__							:	str   	=	torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE					)

            self.assertTrue(lower_bound and upper_bound					)












"""simple docstring"""






def      _snake_case			(				lowercase__							,    lowercase__		):
				# "extended trapezoidal rule"
				# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
				_lowerCamelCase     :			List[Any]    =    (boundary[1] - boundary[0]) / steps
				_lowerCamelCase     :			Tuple    =    boundary[0]
				_lowerCamelCase     :			Dict    =    boundary[1]
				_lowerCamelCase     :			List[Any]    =    make_points(lowercase__							,    lowercase__							,    lowercase__		)
				_lowerCamelCase     :			List[Any]    =    0.0
				y += (h / 2.0) * f(lowercase__		)
				for i in x_i:
								# print(i)
								y += h * f(lowercase__		)
				y += (h / 2.0) * f(lowercase__		)
				return y







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			str    =    a + h
				while x < (b - h):
								yield x
								_lowerCamelCase     :			int    =    x + h







def      _snake_case			(				lowercase__		):  # enter your function here
				_lowerCamelCase     :			Optional[Any]    =    (x - 0) * (x - 0)
				return y







def      _snake_case			(				):
				_lowerCamelCase     :			int    =    0.0  # Lower bound of integration
				_lowerCamelCase     :			Optional[int]    =    1.0  # Upper bound of integration
				_lowerCamelCase     :			List[str]    =    1_0.0  # define number of steps or resolution
				_lowerCamelCase     :			List[Any]    =    [a, b]  # define boundary of integration
				_lowerCamelCase     :			Optional[Any]    =    method_a(lowercase__							,    lowercase__		)
				print(f'''y = {y}'''		)


if __name__ == "__main__":
			main()