


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class A  (   lowerCAmelCase__     ):






      def __init__(					self						:     Optional[Any]			,   lowercase_						:     Optional[Any]			,   lowercase_						:     Optional[int]=None			,   lowercase_						:     Tuple=True			,   lowercase_						:     str=None			,   **lowercase_						:     Union[str, Any] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		int		=parent
             _lowerCamelCase       :		Optional[Any]		=config_class
             _lowerCamelCase       :		Optional[Any]		=has_text_modality
             _lowerCamelCase       :		Optional[int]		=kwargs
             _lowerCamelCase       :		Optional[int]		=common_properties






      def        lowerCamelCase       (					self						:     Dict )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=self.config_class(**self.inputs_dict )
             _lowerCamelCase       :		Optional[int]		=(
                 ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
                 if self.common_properties is None
                 else self.common_properties
             )

             # Add common fields for text models
             if self.has_text_modality:
                    common_properties.extend(['vocab_size'] )

             # Test that config has the common properties as getters
             for prop in common_properties:
                    self.parent.assertTrue(hasattr(__a			,   __a )			,   msg=F'''`{prop}` does not exist''' )

             # Test that config has the common properties as setter
             for idx, name in enumerate(__a ):
                    try:
                           setattr(__a			,   __a			,   __a )
                           self.parent.assertEqual(
                               getattr(__a			,   __a )			,   __a			,   msg=F'''`{name} value {idx} expected, but was {getattr(__a			,   __a )}''' )
                    except NotImplementedError:
                           # Some models might not be able to implement setters for common_properties
                           # In that case, a NotImplementedError is raised
                           pass

        # Test if config class can be called with Config(prop_name=..)
             for idx, name in enumerate(__a ):
                    try:
                           _lowerCamelCase       :		str		=self.config_class(**{name: idx} )
                           self.parent.assertEqual(
                               getattr(__a			,   __a )			,   __a			,   msg=F'''`{name} value {idx} expected, but was {getattr(__a			,   __a )}''' )
                    except NotImplementedError:
                           # Some models might not be able to implement setters for common_properties
                           # In that case, a NotImplementedError is raised
                           pass






      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.config_class(**self.inputs_dict )
             _lowerCamelCase       :		Any		=json.loads(config.to_json_string() )
             for key, value in self.inputs_dict.items():
                    self.parent.assertEqual(obj[key]			,   __a )






      def        lowerCamelCase       (					self						:     Tuple )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.config_class(**self.inputs_dict )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase       :		str		=os.path.join(__a			,   'config.json' )
                    config_first.to_json_file(__a )
                    _lowerCamelCase       :		str		=self.config_class.from_json_file(__a )

             self.parent.assertEqual(config_second.to_dict()			,   config_first.to_dict() )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=self.config_class(**self.inputs_dict )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    config_first.save_pretrained(__a )
                    _lowerCamelCase       :		Optional[Any]		=self.config_class.from_pretrained(__a )

             self.parent.assertEqual(config_second.to_dict()			,   config_first.to_dict() )






      def        lowerCamelCase       (					self						:     int )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.config_class(**self.inputs_dict )

             _lowerCamelCase       :		Tuple		='test'
             with tempfile.TemporaryDirectory() as tmpdirname:
                    _lowerCamelCase       :		List[Any]		=os.path.join(__a			,   __a )
                    config_first.save_pretrained(__a )
                    _lowerCamelCase       :		Union[str, Any]		=self.config_class.from_pretrained(__a			,   subfolder=__a )

             self.parent.assertEqual(config_second.to_dict()			,   config_first.to_dict() )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.config_class(**self.inputs_dict			,   num_labels=5 )
             self.parent.assertEqual(len(config.idalabel )			,   5 )
             self.parent.assertEqual(len(config.labelaid )			,   5 )

             _lowerCamelCase       :		Union[str, Any]		=3
             self.parent.assertEqual(len(config.idalabel )			,   3 )
             self.parent.assertEqual(len(config.labelaid )			,   3 )






      def        lowerCamelCase       (					self						:     Any )					->						Optional[int]:
             """simple docstring"""




             if self.config_class.is_composition:
                    return
             _lowerCamelCase       :		List[str]		=self.config_class()
             self.parent.assertIsNotNone(__a )






      def        lowerCamelCase       (					self						:     Any )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=copy.deepcopy(__a )
             _lowerCamelCase       :		List[str]		=self.config_class(**__a )
             _lowerCamelCase       :		int		=[]
             for key, value in config_common_kwargs.items():
                    if key == "torch_dtype":
                           if not is_torch_available():
                                  continue
                           else:
                                  import torch

                                  if config.torch_dtype != torch.floataa:
                                         wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa) )
                    elif getattr(__a			,   __a ) != value:
                           wrong_values.append((key, getattr(__a			,   __a ), value) )

             if len(__a ) > 0:
                    _lowerCamelCase       :		List[Any]		='\n'.join([F'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values] )
                    raise ValueError(F'''The following keys were not properly set in the config:\n{errors}''' )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Union[str, Any]:
             """simple docstring"""




             self.create_and_test_config_common_properties()
             self.create_and_test_config_to_json_string()
             self.create_and_test_config_to_json_file()
             self.create_and_test_config_from_and_save_pretrained()
             self.create_and_test_config_from_and_save_pretrained_subfolder()
             self.create_and_test_config_with_num_labels()
             self.check_config_can_be_init_without_params()
             self.check_config_arguments_init()






"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase						:    Optional[Any]				      ="""CLIPImageProcessor"""
							__UpperCAmelCase						:    Union[str, Any]				      =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")

							def __init__(     self    ,   __a=None    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."    ,   __a    ,   )
													__lowerCAmelCase				    =			kwargs.pop("feature_extractor"	)

										__lowerCAmelCase				    =			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("You need to specify an `image_processor`."	)
										if tokenizer is None:
													raise ValueError("You need to specify a `tokenizer`."	)

										super().__init__(__a    ,   __a	)

							def __call__(     self    ,   __a=None    ,   __a=None    ,   __a=None    ,   **__a	):

										if text is None and images is None:
													raise ValueError("You have to specify either text or images. Both cannot be none."	)

										if text is not None:
													__lowerCAmelCase				    =			self.tokenizer(__a    ,   return_tensors=__a    ,   **__a	)

										if images is not None:
													__lowerCAmelCase				    =			self.image_processor(__a    ,   return_tensors=__a    ,   **__a	)

										if text is not None and images is not None:
													__lowerCAmelCase				    =			image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										else:
													return BatchEncoding(data=dict(**__a	)    ,   tensor_type=__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.batch_decode(*__a    ,   **__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.decode(*__a    ,   **__a	)



							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer.model_input_names
										__lowerCAmelCase				    =			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)




