



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  ,  a_  ,  a_=13  ,  a_=7  ,  a_=True  ,  a_=True  ,  a_=True  ,  a_=True  ,  a_=99  ,  a_=64  ,  a_=32  ,  a_=5  ,  a_=4  ,  a_=37  ,  a_="gelu"  ,  a_=0.1  ,  a_=0.1  ,  a_=512  ,  a_=16  ,  a_=2  ,  a_=0.02  ,  a_=3  ,  a_=4  ,  a_=None  ,  ):
											a__				      =		parent
											a__				      =		batch_size
											a__				      =		seq_length
											a__				      =		is_training
											a__				      =		use_input_mask
											a__				      =		use_token_type_ids
											a__				      =		use_labels
											a__				      =		vocab_size
											a__				      =		hidden_size
											a__				      =		embedding_size
											a__				      =		num_hidden_layers
											a__				      =		num_attention_heads
											a__				      =		intermediate_size
											a__				      =		hidden_act
											a__				      =		hidden_dropout_prob
											a__				      =		attention_probs_dropout_prob
											a__				      =		max_position_embeddings
											a__				      =		type_vocab_size
											a__				      =		type_sequence_label_size
											a__				      =		initializer_range
											a__				      =		num_labels
											a__				      =		num_choices
											a__				      =		scope


							def _a      (							self		):
											a__				      =		ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size		)

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

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

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

											a__				      =		self.get_config()

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


							def _a      (							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=a_  ,  initializer_range=self.initializer_range  ,  )


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertModel(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(a_  ,  attention_mask=a_  ,  token_type_ids=a_		)
											a__				      =		model(a_  ,  token_type_ids=a_		)
											a__				      =		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 _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertForMaskedLM(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		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 _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertForNextSentencePrediction(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(
											    a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_  ,  )
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, 2)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertForPreTraining(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(
											    a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_  ,  next_sentence_label=a_  ,  )
											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 _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MobileBertForQuestionAnswering(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		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 _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		self.num_labels
											a__				      =		MobileBertForSequenceClassification(a_		)
											model.to(a_		)
											model.eval()
											a__				      =		model(a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_		)
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		self.num_labels
											a__				      =		MobileBertForTokenClassification(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		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 _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		self.num_choices
											a__				      =		MobileBertForMultipleChoice(config=a_		)
											model.to(a_		)
											model.eval()
											a__				      =		input_ids.unsqueeze(1		).expand(-1  ,  self.num_choices  ,  -1		).contiguous()
											a__				      =		token_type_ids.unsqueeze(1		).expand(-1  ,  self.num_choices  ,  -1		).contiguous()
											a__				      =		input_mask.unsqueeze(1		).expand(-1  ,  self.num_choices  ,  -1		).contiguous()
											a__				      =		model(
											    a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  labels=a_  ,  )
											self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_choices)		)







							def _a      (							self		):
											a__				      =		self.prepare_config_and_inputs()
											(
											    (
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			(
											    a__
											)      ,			
											)				      =		config_and_inputs
											a__				      =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
											return config, inputs_dict




@require_torch
class  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      Tuple         =					(
							    (
							        MobileBertModel,
							        MobileBertForMaskedLM,
							        MobileBertForMultipleChoice,
							        MobileBertForNextSentencePrediction,
							        MobileBertForPreTraining,
							        MobileBertForQuestionAnswering,
							        MobileBertForSequenceClassification,
							        MobileBertForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase__				:      str         =					(
							    {
							        """feature-extraction""": MobileBertModel,
							        """fill-mask""": MobileBertForMaskedLM,
							        """question-answering""": MobileBertForQuestionAnswering,
							        """text-classification""": MobileBertForSequenceClassification,
							        """token-classification""": MobileBertForTokenClassification,
							        """zero-shot""": MobileBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							UpperCamelCase__				:      Dict         =					True


							def _a      (							self  ,  a_  ,  a_  ,  a_=False		):
											a__				      =		super()._prepare_for_class(a_  ,  a_  ,  return_labels=a_		)

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


							def _a      (							self		):
											a__				      =		MobileBertModelTester(self		)
											a__				      =		ConfigTester(self  ,  config_class=a_  ,  hidden_size=37		)


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


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_model(*a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_masked_lm(*a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_multiple_choice(*a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_pretraining(*a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_question_answering(*a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_sequence_classification(*a_		)







							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mobilebert_for_token_classification(*a_		)




def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				return torch.tensor(
				    __a ,		dtype=torch.long ,		device=__a ,		)


UpperCAmelCase							 =					1E-3




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







							'''simple docstring'''






							@slow
							def _a      (							self		):
											a__				      =		MobileBertModel.from_pretrained("""google/mobilebert-uncased"""		).to(a_		)
											a__				      =		_long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]		)
											with torch.no_grad():
															a__				      =		model(a_		)[0]
											a__				      =		torch.Size((1, 9, 512)		)
											self.assertEqual(output.shape  ,  a_		)
											a__				      =		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=a_  ,  )

											# 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
											a__				      =		torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE		)
											a__				      =		torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE		)

											self.assertTrue(lower_bound and upper_bound		)






from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase							 =					299_792_458

# Symbols
UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					symbols("""ct x y z""")

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				if velocity > c:
								raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""		)
				elif velocity < 1:
								# Usually the speed should be much higher than 1 (c order of magnitude)
								raise ValueError("""Speed must be greater than or equal to 1!"""		)

				return velocity / c

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return 1 / sqrt(1 - beta(__a		) ** 2		)

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return np.array(
				    [
				        [gamma(__a		), -gamma(__a		) * beta(__a		), 0, 0],
				        [-gamma(__a		) * beta(__a		), gamma(__a		), 0, 0],
				        [0, 0, 1, 0],
				        [0, 0, 0, 1],
				    ]		)

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

				"""simple docstring"""
				# Ensure event is not empty
				if event is None:
								a__				      =		np.array([ct, x, y, z]		)  # Symbolic four vector
				else:
								event[0] *= c  # x0 is ct (speed of light * time)

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					transform(29_979_245)
	print("""Example of four vector: """)
	print(f"""ct' = {four_vector[0]}""")
	print(f"""x' = {four_vector[1]}""")
	print(f"""y' = {four_vector[2]}""")
	print(f"""z' = {four_vector[3]}""")

	# Substitute symbols with numerical values
	UpperCAmelCase							 =					{ct: c, x: 1, y: 1, z: 1}
	UpperCAmelCase							 =					[four_vector[i].subs(sub_dict) for i in range(4)]

	print(f"""\n{numerical_vector}""")

