


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_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase=13	,   _UpperCAmelCase=7	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=99	,   _UpperCAmelCase=64	,   _UpperCAmelCase=32	,   _UpperCAmelCase=5	,   _UpperCAmelCase=4	,   _UpperCAmelCase=37	,   _UpperCAmelCase="gelu"	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=5_12	,   _UpperCAmelCase=16	,   _UpperCAmelCase=2	,   _UpperCAmelCase=0.02	,   _UpperCAmelCase=3	,   _UpperCAmelCase=4	,   _UpperCAmelCase=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 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			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=_UpperCAmelCase	,   initializer_range=self.initializer_range	,   )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   MobileBertModel(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase      )

       self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)      )
       self.parent.assertEqual(result.pooler_output.shape	,   (self.batch_size, self.hidden_size)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   MobileBertForMaskedLM(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   labels=_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   MobileBertForNextSentencePrediction(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(
           _UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   labels=_UpperCAmelCase	,   )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, 2)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   MobileBertForPreTraining(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(
           _UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   labels=_UpperCAmelCase	,   next_sentence_label=_UpperCAmelCase	,   )
       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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   MobileBertForQuestionAnswering(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(
           _UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   start_positions=_UpperCAmelCase	,   end_positions=_UpperCAmelCase	,   )
       self.parent.assertEqual(result.start_logits.shape	,   (self.batch_size, self.seq_length)      )
       self.parent.assertEqual(result.end_logits.shape	,   (self.batch_size, self.seq_length)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   self.num_labels
       snake_case_ =   MobileBertForSequenceClassification(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   labels=_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   self.num_labels
       snake_case_ =   MobileBertForTokenClassification(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   labels=_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.num_labels)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   self.num_choices
       snake_case_ =   MobileBertForMultipleChoice(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       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(
           _UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   labels=_UpperCAmelCase	,   )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_choices)      )





 def 		UpperCamelCase__  (			self      ):
       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 lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						(
     (
         MobileBertModel,
         MobileBertForMaskedLM,
         MobileBertForMultipleChoice,
         MobileBertForNextSentencePrediction,
         MobileBertForPreTraining,
         MobileBertForQuestionAnswering,
         MobileBertForSequenceClassification,
         MobileBertForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __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 {}
 )
 __snake_case				=						True



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=False      ):
       snake_case_ =   super()._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase	,   return_labels=_UpperCAmelCase      )

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   MobileBertModelTester(self      )
       snake_case_ =   ConfigTester(self	,   config_class=_UpperCAmelCase	,   hidden_size=37      )



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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_model(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_masked_lm(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_pretraining(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_question_answering(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_UpperCAmelCase      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_token_classification(*_UpperCAmelCase      )






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




      """simple docstring"""

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


UpperCAmelCase    =					1e-3







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

 '''simple docstring'''

 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   MobileBertModel.from_pretrained('''google/mobilebert-uncased'''      ).to(_UpperCAmelCase      )
       snake_case_ =   _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]      )
       with torch.no_grad():
             snake_case_ =   model(_UpperCAmelCase      )[0]
       snake_case_ =   torch.Size((1, 9, 5_12)      )
       self.assertEqual(output.shape	,   _UpperCAmelCase      )
       snake_case_ =   torch.tensor(
           [
               [
                   [-2.4736526E07, 8.2691656E04, 1.6521838E05],
                   [-5.7541704E-01, 3.9056022E00, 4.4011507E00],
                   [2.6047359E00, 1.5677652E00, -1.7324188E-01],
               ]
           ]	,   device=_UpperCAmelCase	,   )

       # 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      )



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase    =					2_9979_2458

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE      ) ** 2      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						np.ndarray:




      """simple docstring"""

      return np.array(
          [
              [gamma(SCREAMING_SNAKE_CASE      ), -gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), 0, 0],
              [-gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), gamma(SCREAMING_SNAKE_CASE      ), 0, 0],
              [0, 0, 1, 0],
              [0, 0, 0, 1],
          ]      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = None      )->						np.ndarray:




      """simple docstring"""

      if event is None:
            snake_case_ =   np.array([ct, x, y, z]      )  # Symbolic four vector
      else:
            event[0] *= c  # x0 is ct (speed of light * time)

      return transformation_matrix(SCREAMING_SNAKE_CASE      ) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   UpperCAmelCase    =					transform(2997_9245)
   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}''')