


import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.electra.modeling_flax_electra import (
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForMultipleChoice,
           FlaxElectraForPreTraining,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForSequenceClassification,
           FlaxElectraForTokenClassification,
           FlaxElectraModel,
       )


class        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      def __init__(				self:       Union[str, Any]						,      a:       Optional[Any]						,      a:       Dict=13						,      a:       Optional[int]=7						,      a:       Optional[int]=True						,      a:       Optional[Any]=True						,      a:       Union[str, Any]=True						,      a:       Optional[Any]=True						,      a:       Optional[int]=99						,      a:       List[str]=24						,      a:       str=32						,      a:       Optional[int]=5						,      a:       Dict=4						,      a:       Tuple=37						,      a:       Dict="gelu"						,      a:       List[str]=0.1						,      a:       Optional[int]=0.1						,      a:       Optional[Any]=512						,      a:       List[Any]=16						,      a:       List[str]=2						,      a:       Optional[int]=0.0_2						,      a:       Optional[Any]=4						,      ):
       __lowerCamelCase		:							List[Any]							=						parent
       __lowerCamelCase		:							Dict							=						batch_size
       __lowerCamelCase		:							Any							=						seq_length
       __lowerCamelCase		:							str							=						is_training
       __lowerCamelCase		:							Any							=						use_attention_mask
       __lowerCamelCase		:							Any							=						use_token_type_ids
       __lowerCamelCase		:							int							=						use_labels
       __lowerCamelCase		:							Union[str, Any]							=						vocab_size
       __lowerCamelCase		:							Dict							=						hidden_size
       __lowerCamelCase		:							List[str]							=						embedding_size
       __lowerCamelCase		:							Tuple							=						num_hidden_layers
       __lowerCamelCase		:							Tuple							=						num_attention_heads
       __lowerCamelCase		:							Union[str, Any]							=						intermediate_size
       __lowerCamelCase		:							Union[str, Any]							=						hidden_act
       __lowerCamelCase		:							Dict							=						hidden_dropout_prob
       __lowerCamelCase		:							str							=						attention_probs_dropout_prob
       __lowerCamelCase		:							Union[str, Any]							=						max_position_embeddings
       __lowerCamelCase		:							Optional[int]							=						type_vocab_size
       __lowerCamelCase		:							Union[str, Any]							=						type_sequence_label_size
       __lowerCamelCase		:							Tuple							=						initializer_range
       __lowerCamelCase		:							Dict							=						num_choices




      def      _snake_case	(				self:       Dict				):
       __lowerCamelCase		:							int							=						ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size				)

       __lowerCamelCase		:							Union[str, Any]							=						None
       if self.use_attention_mask:
        __lowerCamelCase		:							Any							=						random_attention_mask([self.batch_size, self.seq_length]				)

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

       __lowerCamelCase		:							Optional[Any]							=						ElectraConfig(
           vocab_size=self.vocab_size						,      hidden_size=self.hidden_size						,      embedding_size=self.embedding_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						,      )

       return config, input_ids, token_type_ids, attention_mask




      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							List[Any]							=						self.prepare_config_and_inputs()
       __lowerCamelCase		:							List[Any]							=						config_and_inputs
       __lowerCamelCase		:							List[Any]							=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict



@require_flax
class        A_					(       __UpperCamelCase					,      unittest.TestCase    ):




      '''simple docstring'''


      __snake_case					       =   True

      __snake_case					       =   (
          (
              FlaxElectraModel,
              FlaxElectraForCausalLM,
              FlaxElectraForMaskedLM,
              FlaxElectraForPreTraining,
              FlaxElectraForTokenClassification,
              FlaxElectraForQuestionAnswering,
              FlaxElectraForMultipleChoice,
              FlaxElectraForSequenceClassification,
          )
          if is_flax_available()
          else ()
      )




      def      _snake_case	(				self:       List[str]				):
       __lowerCamelCase		:							List[Any]							=						FlaxElectraModelTester(self				)




      @slow
      def      _snake_case	(				self:       Any				):
       for model_class_name in self.all_model_classes:
        if model_class_name == FlaxElectraForMaskedLM:
         __lowerCamelCase		:							Tuple							=						model_class_name.from_pretrained('google/electra-small-generator'				)
        else:
         __lowerCamelCase		:							Optional[Any]							=						model_class_name.from_pretrained('google/electra-small-discriminator'				)
        __lowerCamelCase		:							List[str]							=						model(np.ones((1, 1)				)				)
        self.assertIsNotNone(snake_case__				)









"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				word.upper()
     UpperCAmelCase__		:  Union[str, Any]				=				0
     UpperCAmelCase__		:  Optional[Any]				=				{}
     UpperCAmelCase__		:  List[Any]				=				[]

     for letter in word:
          if letter not in letter_nums:
               UpperCAmelCase__		:  Tuple				=				str(snake_case							)
               next_num += 1
          word_pattern.append(letter_nums[letter]							)
     return ".".join(snake_case							)


if __name__ == "__main__":
 import pprint
 import time

 _lowerCAmelCase     :     Optional[Any]            =	time.time()
 with open("""dictionary.txt""") as in_file:
  _lowerCAmelCase     :     Optional[int]            =	in_file.read().splitlines()

 _lowerCAmelCase     :     dict            =	{}
 for word in word_list:
  _lowerCAmelCase     :     int            =	get_word_pattern(word)
  if pattern in all_patterns:
   all_patterns[pattern].append(word)
  else:
   _lowerCAmelCase     :     Any            =	[word]

 with open("""word_patterns.txt""", """w""") as out_file:
  out_file.write(pprint.pformat(all_patterns))

 _lowerCAmelCase     :     Union[str, Any]            =	round(time.time() - start_time, 2)
 print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
 # Done!  9,581 word patterns found in 0.58 seconds.


