



'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_MODEL_FOR_PRETRAINING_MAPPING,
       TFMobileBertForMaskedLM,
       TFMobileBertForMultipleChoice,
       TFMobileBertForNextSentencePrediction,
       TFMobileBertForPreTraining,
       TFMobileBertForQuestionAnswering,
       TFMobileBertForSequenceClassification,
       TFMobileBertForTokenClassification,
       TFMobileBertModel,
   )

@require_tf
class 					SCREAMING_SNAKE_CASE					(    __A  ,		__A  ,		unittest.TestCase						):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE          =    (
        (
            TFMobileBertModel,
            TFMobileBertForMaskedLM,
            TFMobileBertForNextSentencePrediction,
            TFMobileBertForPreTraining,
            TFMobileBertForQuestionAnswering,
            TFMobileBertForSequenceClassification,
            TFMobileBertForTokenClassification,
            TFMobileBertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE          =    (
        {
            """feature-extraction""": TFMobileBertModel,
            """fill-mask""": TFMobileBertForMaskedLM,
            """question-answering""": TFMobileBertForQuestionAnswering,
            """text-classification""": TFMobileBertForSequenceClassification,
            """token-classification""": TFMobileBertForTokenClassification,
            """zero-shot""": TFMobileBertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    _SCREAMING_SNAKE_CASE          =    False
    _SCREAMING_SNAKE_CASE          =    False






    def 					A			(    self :   List[str]    ,		UpperCamelCase__ :   Optional[Any]    ,		UpperCamelCase__ :   List[str]    ,		UpperCamelCase__ :   Union[str, Any]=False					):


       """simple docstring"""







       UpperCamelCase      							=    super()._prepare_for_class(UpperCamelCase__    ,		UpperCamelCase__    ,		return_labels=UpperCamelCase__					)

       if return_labels:
          if model_class in get_values(UpperCamelCase__					):
             UpperCamelCase      							=    tf.zeros(self.model_tester.batch_size    ,		dtype=tf.intaa					)

       return inputs_dict

    class 					SCREAMING_SNAKE_CASE					(    __A						):






        """simple docstring"""





        def __init__(    self :   str    ,		UpperCamelCase__ :   Optional[int]    ,		UpperCamelCase__ :   Any=1_3    ,		UpperCamelCase__ :   int=7    ,		UpperCamelCase__ :   Any=True    ,		UpperCamelCase__ :   int=True    ,		UpperCamelCase__ :   Optional[Any]=True    ,		UpperCamelCase__ :   Dict=True    ,		UpperCamelCase__ :   Tuple=9_9    ,		UpperCamelCase__ :   Any=3_2    ,		UpperCamelCase__ :   str=3_2    ,		UpperCamelCase__ :   Tuple=2    ,		UpperCamelCase__ :   Tuple=4    ,		UpperCamelCase__ :   Any=3_7    ,		UpperCamelCase__ :   List[Any]="gelu"    ,		UpperCamelCase__ :   Tuple=0.1    ,		UpperCamelCase__ :   List[str]=0.1    ,		UpperCamelCase__ :   Any=5_1_2    ,		UpperCamelCase__ :   Optional[Any]=1_6    ,		UpperCamelCase__ :   Tuple=2    ,		UpperCamelCase__ :   Any=0.0_2    ,		UpperCamelCase__ :   Optional[Any]=3    ,		UpperCamelCase__ :   Tuple=4    ,		UpperCamelCase__ :   List[str]=None    ,		):


           """simple docstring"""







           UpperCamelCase      							=    parent
           UpperCamelCase      							=    batch_size
           UpperCamelCase      							=    seq_length
           UpperCamelCase      							=    is_training
           UpperCamelCase      							=    use_input_mask
           UpperCamelCase      							=    use_token_type_ids
           UpperCamelCase      							=    use_labels
           UpperCamelCase      							=    vocab_size
           UpperCamelCase      							=    hidden_size
           UpperCamelCase      							=    num_hidden_layers
           UpperCamelCase      							=    num_attention_heads
           UpperCamelCase      							=    intermediate_size
           UpperCamelCase      							=    hidden_act
           UpperCamelCase      							=    hidden_dropout_prob
           UpperCamelCase      							=    attention_probs_dropout_prob
           UpperCamelCase      							=    max_position_embeddings
           UpperCamelCase      							=    type_vocab_size
           UpperCamelCase      							=    type_sequence_label_size
           UpperCamelCase      							=    initializer_range
           UpperCamelCase      							=    num_labels
           UpperCamelCase      							=    num_choices
           UpperCamelCase      							=    scope
           UpperCamelCase      							=    embedding_size






        def 					A			(    self :   Any					):


           """simple docstring"""







           UpperCamelCase      							=    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size					)

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

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

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

           UpperCamelCase      							=    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    ,		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    ,		initializer_range=self.initializer_range    ,		embedding_size=self.embedding_size    ,		)

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






        def 					A			(    self :   Any    ,		UpperCamelCase__ :   Union[str, Any]    ,		UpperCamelCase__ :   int    ,		UpperCamelCase__ :   Tuple    ,		UpperCamelCase__ :   Optional[int]    ,		UpperCamelCase__ :   List[Any]    ,		UpperCamelCase__ :   List[str]    ,		UpperCamelCase__ :   List[str]					):


           """simple docstring"""







           UpperCamelCase      							=    TFMobileBertModel(config=UpperCamelCase__					)
           UpperCamelCase      							=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           UpperCamelCase      							=    model(UpperCamelCase__					)

           UpperCamelCase      							=    [input_ids, input_mask]
           UpperCamelCase      							=    model(UpperCamelCase__					)

           UpperCamelCase      							=    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 					A			(    self :   int    ,		UpperCamelCase__ :   Optional[Any]    ,		UpperCamelCase__ :   Any    ,		UpperCamelCase__ :   List[str]    ,		UpperCamelCase__ :   Tuple    ,		UpperCamelCase__ :   int    ,		UpperCamelCase__ :   str    ,		UpperCamelCase__ :   Dict					):


           """simple docstring"""







           UpperCamelCase      							=    TFMobileBertForMaskedLM(config=UpperCamelCase__					)
           UpperCamelCase      							=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           UpperCamelCase      							=    model(UpperCamelCase__					)
           self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)					)






        def 					A			(    self :   Optional[int]    ,		UpperCamelCase__ :   Dict    ,		UpperCamelCase__ :   int    ,		UpperCamelCase__ :   str    ,		UpperCamelCase__ :   str    ,		UpperCamelCase__ :   int    ,		UpperCamelCase__ :   Optional[int]    ,		UpperCamelCase__ :   Any					):


           """simple docstring"""







           UpperCamelCase      							=    TFMobileBertForNextSentencePrediction(config=UpperCamelCase__					)
           UpperCamelCase      							=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           UpperCamelCase      							=    model(UpperCamelCase__					)
           self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, 2)					)






        def 					A			(    self :   Tuple    ,		UpperCamelCase__ :   int    ,		UpperCamelCase__ :   Dict    ,		UpperCamelCase__ :   List[str]    ,		UpperCamelCase__ :   List[Any]    ,		UpperCamelCase__ :   List[str]    ,		UpperCamelCase__ :   Any    ,		UpperCamelCase__ :   Optional[Any]					):


           """simple docstring"""







           UpperCamelCase      							=    TFMobileBertForPreTraining(config=UpperCamelCase__					)
           UpperCamelCase      							=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           UpperCamelCase      							=    model(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 					A			(    self :   Dict    ,		UpperCamelCase__ :   int    ,		UpperCamelCase__ :   Union[str, Any]    ,		UpperCamelCase__ :   Dict    ,		UpperCamelCase__ :   Union[str, Any]    ,		UpperCamelCase__ :   str    ,		UpperCamelCase__ :   Optional[int]    ,		UpperCamelCase__ :   Any					):


           """simple docstring"""







           UpperCamelCase      							=    self.num_labels
           UpperCamelCase      							=    TFMobileBertForSequenceClassification(config=UpperCamelCase__					)
           UpperCamelCase      							=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           UpperCamelCase      							=    model(UpperCamelCase__					)
           self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)					)






        def 					A			(    self :   Union[str, Any]    ,		UpperCamelCase__ :   int    ,		UpperCamelCase__ :   Dict    ,		UpperCamelCase__ :   Optional[Any]    ,		UpperCamelCase__ :   Any    ,		UpperCamelCase__ :   Optional[int]    ,		UpperCamelCase__ :   List[str]    ,		UpperCamelCase__ :   Optional[Any]					):


           """simple docstring"""







           UpperCamelCase      							=    self.num_choices
           UpperCamelCase      							=    TFMobileBertForMultipleChoice(config=UpperCamelCase__					)
           UpperCamelCase      							=    tf.tile(tf.expand_dims(UpperCamelCase__    ,		1					)    ,		(1, self.num_choices, 1)					)
           UpperCamelCase      							=    tf.tile(tf.expand_dims(UpperCamelCase__    ,		1					)    ,		(1, self.num_choices, 1)					)
           UpperCamelCase      							=    tf.tile(tf.expand_dims(UpperCamelCase__    ,		1					)    ,		(1, self.num_choices, 1)					)
           UpperCamelCase      							=    {
               '''input_ids''': multiple_choice_inputs_ids,
               '''attention_mask''': multiple_choice_input_mask,
               '''token_type_ids''': multiple_choice_token_type_ids,
           }
           UpperCamelCase      							=    model(UpperCamelCase__					)
           self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_choices)					)






        def 					A			(    self :   List[str]    ,		UpperCamelCase__ :   Any    ,		UpperCamelCase__ :   List[Any]    ,		UpperCamelCase__ :   Optional[int]    ,		UpperCamelCase__ :   Tuple    ,		UpperCamelCase__ :   int    ,		UpperCamelCase__ :   List[Any]    ,		UpperCamelCase__ :   Optional[int]					):


           """simple docstring"""







           UpperCamelCase      							=    self.num_labels
           UpperCamelCase      							=    TFMobileBertForTokenClassification(config=UpperCamelCase__					)
           UpperCamelCase      							=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           UpperCamelCase      							=    model(UpperCamelCase__					)
           self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.num_labels)					)






        def 					A			(    self :   Tuple    ,		UpperCamelCase__ :   Dict    ,		UpperCamelCase__ :   Tuple    ,		UpperCamelCase__ :   Any    ,		UpperCamelCase__ :   List[str]    ,		UpperCamelCase__ :   int    ,		UpperCamelCase__ :   Dict    ,		UpperCamelCase__ :   List[str]					):


           """simple docstring"""







           UpperCamelCase      							=    TFMobileBertForQuestionAnswering(config=UpperCamelCase__					)
           UpperCamelCase      							=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           UpperCamelCase      							=    model(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 					A			(    self :   List[Any]					):


           """simple docstring"""







           UpperCamelCase      							=    self.prepare_config_and_inputs()
           (
               UpperCamelCase		
           )      							=    config_and_inputs
           UpperCamelCase      							=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict






    def 					A			(    self :   Union[str, Any]					):


       """simple docstring"""







       UpperCamelCase      							=    TFMobileBertModelTest.TFMobileBertModelTester(self					)
       UpperCamelCase      							=    ConfigTester(self    ,		config_class=UpperCamelCase__    ,		hidden_size=3_7					)






    def 					A			(    self :   List[str]					):


       """simple docstring"""







       self.config_tester.run_common_tests()






    def 					A			(    self :   Tuple					):


       """simple docstring"""







       UpperCamelCase      							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_model(*UpperCamelCase__					)






    def 					A			(    self :   Tuple					):


       """simple docstring"""







       UpperCamelCase      							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCamelCase__					)






    def 					A			(    self :   str					):


       """simple docstring"""







       UpperCamelCase      							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCamelCase__					)






    def 					A			(    self :   int					):


       """simple docstring"""







       UpperCamelCase      							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCamelCase__					)






    def 					A			(    self :   Any					):


       """simple docstring"""







       UpperCamelCase      							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCamelCase__					)






    def 					A			(    self :   Dict					):


       """simple docstring"""







       UpperCamelCase      							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCamelCase__					)






    def 					A			(    self :   Optional[int]					):


       """simple docstring"""







       UpperCamelCase      							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCamelCase__					)






    def 					A			(    self :   Any					):


       """simple docstring"""







       UpperCamelCase      							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCamelCase__					)






    @slow
    def 					A			(    self :   Optional[Any]					):


       """simple docstring"""







       for model_name in ["google/mobilebert-uncased"]:
          UpperCamelCase      							=    TFMobileBertModel.from_pretrained(UpperCamelCase__					)
          self.assertIsNotNone(UpperCamelCase__					)

@require_tf
class 					SCREAMING_SNAKE_CASE					(    unittest.TestCase						):






    """simple docstring"""





    @slow
    def 					A			(    self :   Any					):


       """simple docstring"""







       UpperCamelCase      							=    TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased'					)
       UpperCamelCase      							=    tf.constant([[0, 1, 2, 3, 4, 5]]					)
       UpperCamelCase      							=    model(UpperCamelCase__					)[0]

       UpperCamelCase      							=    [1, 6, 3_0_5_2_2]
       self.assertEqual(output.shape    ,		UpperCamelCase__					)

       UpperCamelCase      							=    tf.constant(
           [
               [
                   [-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
                   [-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
                   [-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
               ]
           ]					)
       tf.debugging.assert_near(output[:, :3, :3]    ,		UpperCamelCase__    ,		atol=1E-4					)









'''simple docstring'''



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE							=						{
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
SCREAMING_SNAKE_CASE							=						'ETAOINSHRDLCUMWFGYPBVKJXQZ'
SCREAMING_SNAKE_CASE							=						'ABCDEFGHIJKLMNOPQRSTUVWXYZ'


def 				lowercase_					(     __A			:							str      )							->   dict[str, int]:



 """simple docstring"""




 lowercase       : List[str]						={letter: 0 for letter in string.ascii_uppercase}
 for letter in message.upper():
  if letter in LETTERS:
   letter_count[letter] += 1

 return letter_count


def 				lowercase_					(     __A			:							tuple      )							->   str:



 """simple docstring"""




 return x[0]


def 				lowercase_					(     __A			:							str      )							->   str:



 """simple docstring"""




 lowercase       : Optional[Any]						=get_letter_count(__A      )
 lowercase       : dict[int, list[str]]						={
     freq: [] for letter, freq in letter_to_freq.items()
 }
 for letter in LETTERS:
  freq_to_letter[letter_to_freq[letter]].append(__A      )

 lowercase       : dict[int, str]						={}

 for freq in freq_to_letter:
  freq_to_letter[freq].sort(key=ETAOIN.find       ,   reverse=__A      )
  lowercase       : Optional[int]						=''''''.join(freq_to_letter[freq]      )

 lowercase       : str						=list(freq_to_letter_str.items()      )
 freq_pairs.sort(key=__A       ,   reverse=__A      )

 lowercase       : list[str]						=[freq_pair[1] for freq_pair in freq_pairs]

 return "".join(__A      )


def 				lowercase_					(     __A			:							str      )							->   int:



 """simple docstring"""




 lowercase       : Optional[Any]						=get_frequency_order(__A      )
 lowercase       : Union[str, Any]						=0
 for common_letter in ETAOIN[:6]:
  if common_letter in freq_order[:6]:
   match_score += 1

 for uncommon_letter in ETAOIN[-6:]:
  if uncommon_letter in freq_order[-6:]:
   match_score += 1

 return match_score


if __name__ == "__main__":
      import doctest

      doctest.testmod()



