






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):

       '''simple docstring'''



       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      ,    )			->   str:






            '''simple docstring'''





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



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






            '''simple docstring'''





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

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

            __snake_case      :int 							=    None
            if self.use_token_type_ids:
                 __snake_case      :List[str] 							=    ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size     )

            __snake_case      :List[str] 							=    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=a__      ,    initializer_range=self.initializer_range      ,    )

            return config, input_ids, token_type_ids, attention_mask



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





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



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





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

            __snake_case      :List[str] 							=    True
            __snake_case      :Tuple 							=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
            __snake_case      :Tuple 							=    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__ (				A_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[str]								=			True

       lowerCamelCase				:					Optional[int]								=			(
           (
               FlaxRobertaModel,
               FlaxRobertaForCausalLM,
               FlaxRobertaForMaskedLM,
               FlaxRobertaForSequenceClassification,
               FlaxRobertaForTokenClassification,
               FlaxRobertaForMultipleChoice,
               FlaxRobertaForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :Tuple 							=    FlaxRobertaModelTester(self     )



       @slow
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            for model_class_name in self.all_model_classes:
                 __snake_case      :Any 							=    model_class_name.from_pretrained("""roberta-base"""      ,    from_pt=a__     )
                 __snake_case      :int 							=    model(np.ones((1, 1)     )     )
                 self.assertIsNotNone(a__     )






"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


UpperCAmelCase				=						logging.get_logger(__name__)




class 	lowercase__				(					A_				):





   def __init__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  None:
      warnings.warn(
          """The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use ImageGPTImageProcessor instead."""      ,   SCREAMING_SNAKE_CASE      ,   )
      super().__init__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

