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 __snake_case    (						unittest.TestCase		):




   def __init__(   self      :    str	,     _lowercase      :    str	,     _lowercase      :    int=13	,     _lowercase      :    Union[str, Any]=7	,     _lowercase      :    List[str]=True	,     _lowercase      :    Any=True	,     _lowercase      :    str=True	,     _lowercase      :    Any=True	,     _lowercase      :    Union[str, Any]=99	,     _lowercase      :    int=32	,     _lowercase      :    int=5	,     _lowercase      :    int=4	,     _lowercase      :    Any=37	,     _lowercase      :    Optional[int]="gelu"	,     _lowercase      :    List[Any]=0.1	,     _lowercase      :    List[str]=0.1	,     _lowercase      :    Optional[Any]=5_12	,     _lowercase      :    int=16	,     _lowercase      :    Dict=2	,     _lowercase      :    int=0.02	,     _lowercase      :    Dict=4	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				seq_length
     SCREAMING_SNAKE_CASE__      							=				is_training
     SCREAMING_SNAKE_CASE__      							=				use_attention_mask
     SCREAMING_SNAKE_CASE__      							=				use_token_type_ids
     SCREAMING_SNAKE_CASE__      							=				use_labels
     SCREAMING_SNAKE_CASE__      							=				vocab_size
     SCREAMING_SNAKE_CASE__      							=				hidden_size
     SCREAMING_SNAKE_CASE__      							=				num_hidden_layers
     SCREAMING_SNAKE_CASE__      							=				num_attention_heads
     SCREAMING_SNAKE_CASE__      							=				intermediate_size
     SCREAMING_SNAKE_CASE__      							=				hidden_act
     SCREAMING_SNAKE_CASE__      							=				hidden_dropout_prob
     SCREAMING_SNAKE_CASE__      							=				attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__      							=				max_position_embeddings
     SCREAMING_SNAKE_CASE__      							=				type_vocab_size
     SCREAMING_SNAKE_CASE__      							=				type_sequence_label_size
     SCREAMING_SNAKE_CASE__      							=				initializer_range
     SCREAMING_SNAKE_CASE__      							=				num_choices




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				None
     if self.use_attention_mask:
       SCREAMING_SNAKE_CASE__      							=				random_attention_mask([self.batch_size, self.seq_length]				)

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

     SCREAMING_SNAKE_CASE__      							=				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=_lowercase	,     initializer_range=self.initializer_range	,     )

     return config, input_ids, token_type_ids, attention_mask




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				config_and_inputs
     SCREAMING_SNAKE_CASE__      							=				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
     return config, inputs_dict




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				config_and_inputs

     SCREAMING_SNAKE_CASE__      							=				True
     SCREAMING_SNAKE_CASE__      							=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
     SCREAMING_SNAKE_CASE__      							=				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 __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							True

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




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				FlaxRobertaModelTester(self				)




   @slow
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






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

import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


__lowerCamelCase      :			str							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    List[str]	,     *_lowercase      :    Union[str, Any]	,     **_lowercase      :    Union[str, Any]				):



     """simple docstring"""






     warnings.warn(
         """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use DonutImageProcessor instead."""	,     _lowercase	,     )
     super().__init__(*_lowercase	,     **_lowercase				)
