import unittest

import numpy as np

from transformers import RobertaConfig, 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.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )






class   __lowercase				(  unittest.TestCase  ):





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






         """simple docstring"""







         UpperCAmelCase							=     parent
         UpperCAmelCase							=     batch_size
         UpperCAmelCase							=     seq_length
         UpperCAmelCase							=     is_training
         UpperCAmelCase							=     use_attention_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_choices





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






         """simple docstring"""







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

         UpperCAmelCase							=     None
         if self.use_attention_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							=     RobertaConfig(
             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=UpperCamelCase__					,       initializer_range=self.initializer_range					,       )

         return config, input_ids, token_type_ids, attention_mask





      def        _lowercase				(		self						:					List[str]       )    ->   Tuple:






         """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''': attention_mask}
         return config, inputs_dict





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     self.prepare_config_and_inputs()
         UpperCAmelCase							=     config_and_inputs

         UpperCAmelCase							=     True
         UpperCAmelCase							=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
         UpperCAmelCase							=     ids_tensor([self.batch_size, self.seq_length]					,       vocab_size=2       )

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






@require_flax
class   __lowercase				(  UpperCamelCase_ ,					unittest.TestCase  ):
      UpperCamelCase								=       True

      UpperCamelCase								=       (
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     FlaxRobertaModelTester(self       )





      @slow
      def        _lowercase				(		self						:					int       )    ->   Dict:






         """simple docstring"""







         for model_class_name in self.all_model_classes:
            UpperCAmelCase							=     model_class_name.from_pretrained("""roberta-base"""					,       from_pt=UpperCamelCase__       )
            UpperCAmelCase							=     model(np.ones((1, 1)       )       )
            self.assertIsNotNone(UpperCamelCase__       )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


__a     =			{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a     =			["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
 from .tokenization_herbert import HerbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_herbert_fast import HerbertTokenizerFast

else:
 import sys

 __a     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



