

'''simple docstring'''





import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.bert.modeling_flax_bert import (
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
  )







class        a__       (   unittest.TestCase       ):







  """simple docstring"""




  def __init__(			self    : int				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : List[str]=1_3				,				UpperCAmelCase__    : List[Any]=7				,				UpperCAmelCase__    : Any=True				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : int=True				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : str=9_9				,				UpperCAmelCase__    : Optional[Any]=3_2				,				UpperCAmelCase__    : Tuple=5				,				UpperCAmelCase__    : Dict=4				,				UpperCAmelCase__    : Any=3_7				,				UpperCAmelCase__    : Tuple="gelu"				,				UpperCAmelCase__    : Union[str, Any]=0.1				,				UpperCAmelCase__    : Union[str, Any]=0.1				,				UpperCAmelCase__    : Union[str, Any]=5_1_2				,				UpperCAmelCase__    : Union[str, Any]=1_6				,				UpperCAmelCase__    : Optional[Any]=2				,				UpperCAmelCase__    : Dict=0.02				,				UpperCAmelCase__    : str=4				,				)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   parent
        SCREAMING_SNAKE_CASE :      Dict       =   batch_size
        SCREAMING_SNAKE_CASE :      Dict       =   seq_length
        SCREAMING_SNAKE_CASE :      Any       =   is_training
        SCREAMING_SNAKE_CASE :      List[str]       =   use_attention_mask
        SCREAMING_SNAKE_CASE :      int       =   use_token_type_ids
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   use_labels
        SCREAMING_SNAKE_CASE :      Optional[int]       =   vocab_size
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_size
        SCREAMING_SNAKE_CASE :      Tuple       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      Tuple       =   intermediate_size
        SCREAMING_SNAKE_CASE :      Any       =   hidden_act
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   hidden_dropout_prob
        SCREAMING_SNAKE_CASE :      int       =   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE :      Dict       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      List[str]       =   type_vocab_size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   type_sequence_label_size
        SCREAMING_SNAKE_CASE :      Any       =   initializer_range
        SCREAMING_SNAKE_CASE :      List[Any]       =   num_choices


  def  _lowercase      (			self    : Any		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size		)

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

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

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   BertConfig(
            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				,				is_decoder=__lowerCamelCase				,				initializer_range=self.initializer_range				,				)

        return config, input_ids, token_type_ids, attention_mask


  def  _lowercase      (			self    : str		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE :      List[str]       =   config_and_inputs
        SCREAMING_SNAKE_CASE :      Tuple       =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict




  def  _lowercase      (			self    : str		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE :      Tuple       =   config_and_inputs

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   True
        SCREAMING_SNAKE_CASE :      Dict       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
        SCREAMING_SNAKE_CASE :      Dict       =   ids_tensor([self.batch_size, self.seq_length]				,				vocab_size=2		)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )







@require_flax
class        a__       (   lowerCamelCase__	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Dict			       =True

  UpperCAmelCase__      : List[str]			       =(
      (
          FlaxBertModel,
          FlaxBertForPreTraining,
          FlaxBertForMaskedLM,
          FlaxBertForMultipleChoice,
          FlaxBertForQuestionAnswering,
          FlaxBertForNextSentencePrediction,
          FlaxBertForSequenceClassification,
          FlaxBertForTokenClassification,
          FlaxBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )


  def  _lowercase      (			self    : str		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   FlaxBertModelTester(self		)




  @slow
  def  _lowercase      (			self    : str		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   FlaxBertModel.from_pretrained("""bert-base-cased"""		)
        SCREAMING_SNAKE_CASE :      int       =   model(np.ones((1, 1)		)		)
        self.assertIsNotNone(__lowerCamelCase		)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     str				    =    {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Any				    =    ["""LayoutXLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[str]				    =    ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
  from .processing_layoutxlm import LayoutXLMProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm import LayoutXLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
  import sys

  UpperCAmelCase__ :     List[str]				    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




