






"""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 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def __init__(		self  :						Dict		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						int=13		,			lowercase_  :						Union[str, Any]=7		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						Union[str, Any]=True		,			lowercase_  :						List[str]=True		,			lowercase_  :						List[str]=99		,			lowercase_  :						Union[str, Any]=32		,			lowercase_  :						str=5		,			lowercase_  :						List[Any]=4		,			lowercase_  :						int=37		,			lowercase_  :						str="gelu"		,			lowercase_  :						List[Any]=0.1		,			lowercase_  :						Any=0.1		,			lowercase_  :						Optional[int]=512		,			lowercase_  :						int=16		,			lowercase_  :						Dict=2		,			lowercase_  :						Tuple=0.02		,			lowercase_  :						Any=4		,			):




          '''simple docstring'''


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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size)

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

          SCREAMING_SNAKE_CASE_   :						str							=	None
          if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE_   :						Tuple							=	ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size)

          SCREAMING_SNAKE_CASE_   :						int							=	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 _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


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



      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	config_and_inputs

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	True
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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 							lowerCAmelCase__       (    UpperCAmelCase__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						True

      __UpperCamelCase							=						(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	FlaxRobertaModelTester(self)



      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_   :						str							=	model_class_name.from_pretrained('''roberta-base'''		,			from_pt=UpperCamelCase_)
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(np.ones((1, 1)))
              self.assertIsNotNone(UpperCamelCase_)










"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_     :				List[str]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				Dict         =				{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"data2vec-text"
      def __init__(		self  :						Any		,			lowercase_  :						Any=30522		,			lowercase_  :						Any=768		,			lowercase_  :						Union[str, Any]=12		,			lowercase_  :						Dict=12		,			lowercase_  :						List[Any]=3072		,			lowercase_  :						str="gelu"		,			lowercase_  :						int=0.1		,			lowercase_  :						Dict=0.1		,			lowercase_  :						str=512		,			lowercase_  :						Optional[int]=2		,			lowercase_  :						int=0.02		,			lowercase_  :						int=1e-12		,			lowercase_  :						Any=1		,			lowercase_  :						Any=0		,			lowercase_  :						List[Any]=2		,			lowercase_  :						Tuple="absolute"		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						int=None		,			**lowercase_  :						Tuple		,			):




          '''simple docstring'''


          super().__init__(pad_token_id=lowercase_		,			bos_token_id=lowercase_		,			eos_token_id=lowercase_		,			**lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						Any							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Tuple							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	type_vocab_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	layer_norm_eps
          SCREAMING_SNAKE_CASE_   :						str							=	position_embedding_type
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	use_cache
          SCREAMING_SNAKE_CASE_   :						str							=	classifier_dropout




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          if self.task == "multiple-choice":
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              SCREAMING_SNAKE_CASE_   :						Any							=	{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ])


