



'''simple docstring'''





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       lowerCamelCase_					(       unittest.TestCase  ):
  """simple docstring"""




  def __init__(				self   :			Dict    ,			_a   :			List[Any]    ,			_a   :			str=13    ,			_a   :			int=7    ,			_a   :			Dict=True    ,			_a   :			Dict=True    ,			_a   :			Union[str, Any]=True    ,			_a   :			str=True    ,			_a   :			List[str]=99    ,			_a   :			Optional[int]=24    ,			_a   :			Any=32    ,			_a   :			List[str]=5    ,			_a   :			Dict=4    ,			_a   :			int=37    ,			_a   :			str="gelu"    ,			_a   :			Tuple=0.1    ,			_a   :			Optional[int]=0.1    ,			_a   :			int=512    ,			_a   :			Dict=16    ,			_a   :			int=2    ,			_a   :			str=0.02    ,			_a   :			List[Any]=4    ,			)      ->      str:
    __lowerCamelCase : Any					 =				parent
    __lowerCamelCase : Any					 =				batch_size
    __lowerCamelCase : Optional[int]					 =				seq_length
    __lowerCamelCase : Dict					 =				is_training
    __lowerCamelCase : int					 =				use_attention_mask
    __lowerCamelCase : Optional[int]					 =				use_token_type_ids
    __lowerCamelCase : int					 =				use_labels
    __lowerCamelCase : Any					 =				vocab_size
    __lowerCamelCase : Tuple					 =				hidden_size
    __lowerCamelCase : List[Any]					 =				embedding_size
    __lowerCamelCase : Any					 =				num_hidden_layers
    __lowerCamelCase : List[str]					 =				num_attention_heads
    __lowerCamelCase : Dict					 =				intermediate_size
    __lowerCamelCase : Dict					 =				hidden_act
    __lowerCamelCase : Tuple					 =				hidden_dropout_prob
    __lowerCamelCase : Union[str, Any]					 =				attention_probs_dropout_prob
    __lowerCamelCase : Optional[Any]					 =				max_position_embeddings
    __lowerCamelCase : List[str]					 =				type_vocab_size
    __lowerCamelCase : List[str]					 =				type_sequence_label_size
    __lowerCamelCase : List[str]					 =				initializer_range
    __lowerCamelCase : str					 =				num_choices




  def 		_lowercase  (				self   :			str     )      ->      List[Any]:
    __lowerCamelCase : Optional[int]					 =				ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size     )

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

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

    __lowerCamelCase : 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 		_lowercase  (				self   :			Any     )      ->      List[Any]:
    __lowerCamelCase : List[str]					 =				self.prepare_config_and_inputs()
    __lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase : Any					 =				config_and_inputs
    __lowerCamelCase : Union[str, Any]					 =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
    return config, inputs_dict






@require_flax
class       lowerCamelCase_					(       lowerCAmelCase__     ,     unittest.TestCase  ):
  """simple docstring"""




  a_							      =True

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




  def 		_lowercase  (				self   :			Optional[int]     )      ->      List[Any]:
    __lowerCamelCase : Tuple					 =				FlaxElectraModelTester(self     )




  @slow
  def 		_lowercase  (				self   :			Dict     )      ->      Optional[Any]:






    for model_class_name in self.all_model_classes:
      if model_class_name == FlaxElectraForMaskedLM:
        __lowerCamelCase : Any					 =				model_class_name.from_pretrained('google/electra-small-generator'     )
      else:
        __lowerCamelCase : Tuple					 =				model_class_name.from_pretrained('google/electra-small-discriminator'     )
      __lowerCamelCase : Union[str, Any]					 =				model(np.ones((1, 1)     )     )
      self.assertIsNotNone(lowerCAmelCase_     )










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_UpperCamelCase							       =		{}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							       =		['BarthezTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							       =		['BarthezTokenizerFast']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_barthez import BarthezTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_barthez_fast import BarthezTokenizerFast

else:
   import sys

   _UpperCamelCase							       =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






