import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=["image_processor", "tokenizer"]
			UpperCamelCase__    							="CLIPImageProcessor"
			UpperCamelCase__    							=("CLIPTokenizer", "CLIPTokenizerFast")





			def __init__(       self	:	int  ,  lowercase__	:	Dict=None  ,  lowercase__	:	List[Any]=None  ,  **lowercase__	:	Union[str, Any]						):
										_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.'  ,  lowercase__  ,  )
																	_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__(lowercase__  ,  lowercase__						)





			def __call__(       self	:	Optional[int]  ,  lowercase__	:	Any=None  ,  lowercase__	:	Union[str, Any]=None  ,  lowercase__	:	Optional[int]=None  ,  **lowercase__	:	Tuple						):

										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(lowercase__  ,  return_tensors=lowercase__  ,  **lowercase__						)

										if images is not None:
																	_lowerCAmelCase							=							self.image_processor(lowercase__  ,  return_tensors=lowercase__  ,  **lowercase__						)

										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(**lowercase__						)  ,  tensor_type=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  *lowercase__	:	int  ,  **lowercase__	:	Optional[int]						):
										return self.tokenizer.batch_decode(*lowercase__  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]  ,  *lowercase__	:	Any  ,  **lowercase__	:	Dict						):
										return self.tokenizer.decode(*lowercase__  ,  **lowercase__						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.tokenizer.model_input_names
										_lowerCAmelCase							=							self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										warnings.warn(
										    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,  lowercase__  ,  )
										return self.image_processor_class





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										warnings.warn(
										    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,  lowercase__  ,  )
										return self.image_processor







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase:	Optional[Any]       			=		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''RagModel''',
				    '''RagPreTrainedModel''',
				    '''RagSequenceForGeneration''',
				    '''RagTokenForGeneration''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''TFRagModel''',
				    '''TFRagPreTrainedModel''',
				    '''TFRagSequenceForGeneration''',
				    '''TFRagTokenForGeneration''',
				]


if TYPE_CHECKING:
				from .configuration_rag import RagConfig
				from .retrieval_rag import RagRetriever
				from .tokenization_rag import RagTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rag import (
								    TFRagModel,
								    TFRagPreTrainedModel,
								    TFRagSequenceForGeneration,
								    TFRagTokenForGeneration,
								)

else:
				import sys

				_lowercase:	Optional[int]       			=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






