
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						,       __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=512						,       __A=16						,       __A=2						,       __A=0.02						,       __A=4						,       )						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=parent
              SCREAMING_SNAKE_CASE_							:				List[str]		=batch_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=seq_length
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=is_training
              SCREAMING_SNAKE_CASE_							:				str		=use_attention_mask
              SCREAMING_SNAKE_CASE_							:				Dict		=use_token_type_ids
              SCREAMING_SNAKE_CASE_							:				int		=use_labels
              SCREAMING_SNAKE_CASE_							:				Any		=vocab_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=hidden_size
              SCREAMING_SNAKE_CASE_							:				str		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=num_attention_heads
              SCREAMING_SNAKE_CASE_							:				Any		=intermediate_size
              SCREAMING_SNAKE_CASE_							:				Dict		=hidden_act
              SCREAMING_SNAKE_CASE_							:				Any		=hidden_dropout_prob
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=max_position_embeddings
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=type_vocab_size
              SCREAMING_SNAKE_CASE_							:				int		=type_sequence_label_size
              SCREAMING_SNAKE_CASE_							:				str		=initializer_range
              SCREAMING_SNAKE_CASE_							:				Tuple		=num_choices


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				str		=ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=None
              if self.use_attention_mask:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=random_attention_mask([self.batch_size, self.seq_length]				)

              SCREAMING_SNAKE_CASE_							:				Dict		=None
              if self.use_token_type_ids:
                     SCREAMING_SNAKE_CASE_							:				Any		=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 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.prepare_config_and_inputs()
              SCREAMING_SNAKE_CASE_							:				Any		=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 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.prepare_config_and_inputs()
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=config_and_inputs

              SCREAMING_SNAKE_CASE_							:				Any		=True
              SCREAMING_SNAKE_CASE_							:				str		=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=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_		(      a__  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						True

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


       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				str		=FlaxRobertaModelTester(self				)





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






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







'''simple docstring'''




from ...configuration_utils import PretrainedConfig


lowercase__       : Any         =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''tapas'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase=10.0		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase="ratio"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase		,      **_UpperCAmelCase)

          # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
          __A		:					Dict						=			vocab_size
          __A		:					Tuple						=			hidden_size
          __A		:					Any						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					Tuple						=			intermediate_size
          __A		:					List[Any]						=			hidden_dropout_prob
          __A		:					int						=			attention_probs_dropout_prob
          __A		:					List[str]						=			max_position_embeddings
          __A		:					Optional[int]						=			type_vocab_sizes
          __A		:					str						=			initializer_range
          __A		:					List[str]						=			layer_norm_eps

          # Fine-tuning task hyperparameters
          __A		:					List[str]						=			positive_label_weight
          __A		:					List[Any]						=			num_aggregation_labels
          __A		:					Optional[Any]						=			aggregation_loss_weight
          __A		:					Tuple						=			use_answer_as_supervision
          __A		:					List[str]						=			answer_loss_importance
          __A		:					Any						=			use_normalized_answer_loss
          __A		:					Any						=			huber_loss_delta
          __A		:					Union[str, Any]						=			temperature
          __A		:					Tuple						=			aggregation_temperature
          __A		:					Optional[Any]						=			use_gumbel_for_cells
          __A		:					List[str]						=			use_gumbel_for_aggregation
          __A		:					Tuple						=			average_approximation_function
          __A		:					List[str]						=			cell_selection_preference
          __A		:					Dict						=			answer_loss_cutoff
          __A		:					Union[str, Any]						=			max_num_rows
          __A		:					Optional[Any]						=			max_num_columns
          __A		:					int						=			average_logits_per_cell
          __A		:					Optional[Any]						=			select_one_column
          __A		:					int						=			allow_empty_column_selection
          __A		:					List[Any]						=			init_cell_selection_weights_to_zero
          __A		:					int						=			reset_position_index_per_cell
          __A		:					Union[str, Any]						=			disable_per_token_loss

          # Aggregation hyperparameters
          __A		:					Optional[Any]						=			aggregation_labels
          __A		:					List[str]						=			no_aggregation_label_index

          if isinstance(self.aggregation_labels		,      _UpperCAmelCase):
               __A		:					Optional[Any]						=			{int(_UpperCAmelCase): v for k, v in aggregation_labels.items()}