
"""simple docstring"""





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     _UpperCAmelCase(    unittest.TestCase	):



		def __init__(					self				,		__a				,		__a=13				,		__a=7				,		__a=True				,		__a=True				,		__a=True				,		__a=True				,		__a=99				,		__a=32				,		__a=5				,		__a=4				,		__a=37				,		__a="gelu"				,		__a=0.1				,		__a=0.1				,		__a=5_12				,		__a=16				,		__a=2				,		__a=0.02				,		__a=4				,		)       ->       Optional[Any]:






							'''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 						UpperCAmelCase							(					self)       ->       List[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=snake_case_				,		initializer_range=self.initializer_range				,		)

							return config, input_ids, token_type_ids, attention_mask



		def 						UpperCAmelCase							(					self)       ->       List[str]:






							'''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 						UpperCAmelCase							(					self)       ->       Tuple:






							'''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     _UpperCAmelCase(    __lowercase      ,     unittest.TestCase	):
		lowercase__						=      True

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



		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase							 =  FlaxRobertaModelTester(self)




		@slow
		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							for model_class_name in self.all_model_classes:
												_UpperCamelCase							 =  model_class_name.from_pretrained('''roberta-base'''				,		from_pt=snake_case_)
												_UpperCamelCase							 =  model(np.ones((1, 1)))
												self.assertIsNotNone(snake_case_)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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