
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''image_processor''', '''tokenizer''']
					SCREAMING_SNAKE_CASE			    =    '''AutoImageProcessor'''
					SCREAMING_SNAKE_CASE			    =    '''AutoTokenizer'''

					def __init__(	self					:     str   ,					__UpperCamelCase					:     Any=None   ,					__UpperCamelCase					:     str=None   ,					**__UpperCamelCase					:     Optional[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."""   ,					__UpperCamelCase   ,					)
																			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__(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.image_processor
												lowerCamelCase_							  =					False

					def __call__(	self					:     List[Any]   ,					*__UpperCamelCase					:     int   ,					**__UpperCamelCase					:     List[str]   ):
												# For backward compatibility
												if self._in_target_context_manager:
																			return self.current_processor(*__UpperCamelCase   ,					**__UpperCamelCase   )

												lowerCamelCase_							  =					kwargs.pop("""images"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					kwargs.pop("""text"""   ,					__UpperCamelCase   )
												if len(__UpperCamelCase   ) > 0:
																			lowerCamelCase_							  =					args[0]
																			lowerCamelCase_							  =					args[1:]

												if images is None and text is None:
																			raise ValueError("""You need to specify either an `images` or `text` input to process."""   )

												if images is not None:
																			lowerCamelCase_							  =					self.image_processor(__UpperCamelCase   ,					*__UpperCamelCase   ,					**__UpperCamelCase   )
												if text is not None:
																			lowerCamelCase_							  =					self.tokenizer(__UpperCamelCase   ,					**__UpperCamelCase   )

												if text is None:
																			return inputs
												elif images is None:
																			return encodings
												else:
																			lowerCamelCase_							  =					encodings["""input_ids"""]
																			return inputs

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

					def    lowercase__      (	self					:     int   ,					*__UpperCamelCase					:     Tuple   ,					**__UpperCamelCase					:     Any   ):
												return self.tokenizer.decode(*__UpperCamelCase   ,					**__UpperCamelCase   )

					@contextmanager
					def    lowercase__      (	self					:     Any   ):
												warnings.warn(
												    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
												    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
												    """your images inputs, or in a separate call."""   )
												lowerCamelCase_							  =					True
												lowerCamelCase_							  =					self.tokenizer
												yield
												lowerCamelCase_							  =					self.image_processor
												lowerCamelCase_							  =					False

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     Optional[Any]=None   ):
												if added_vocab is None:
																			lowerCamelCase_							  =					self.tokenizer.get_added_vocab()

												lowerCamelCase_							  =					{}

												while tokens:
																			lowerCamelCase_							  =					re.search(R"""<s_(.*?)>"""   ,					__UpperCamelCase   ,					re.IGNORECASE   )
																			if start_token is None:
																										break
																			lowerCamelCase_							  =					start_token.group(1   )
																			lowerCamelCase_							  =					re.search(RF'''</s_{key}>'''   ,					__UpperCamelCase   ,					re.IGNORECASE   )
																			lowerCamelCase_							  =					start_token.group()
																			if end_token is None:
																										lowerCamelCase_							  =					tokens.replace(__UpperCamelCase   ,					""""""   )
																			else:
																										lowerCamelCase_							  =					end_token.group()
																										lowerCamelCase_							  =					re.escape(__UpperCamelCase   )
																										lowerCamelCase_							  =					re.escape(__UpperCamelCase   )
																										lowerCamelCase_							  =					re.search(F'''{start_token_escaped}(.*?){end_token_escaped}'''   ,					__UpperCamelCase   ,					re.IGNORECASE   )
																										if content is not None:
																																	lowerCamelCase_							  =					content.group(1   ).strip()
																																	if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																								lowerCamelCase_							  =					self.tokenajson(__UpperCamelCase   ,					is_inner_value=__UpperCamelCase   ,					added_vocab=__UpperCamelCase   )
																																								if value:
																																															if len(__UpperCamelCase   ) == 1:
																																																						lowerCamelCase_							  =					value[0]
																																															lowerCamelCase_							  =					value
																																	else:  # leaf nodes
																																								lowerCamelCase_							  =					[]
																																								for leaf in content.split(R"""<sep/>"""   ):
																																															lowerCamelCase_							  =					leaf.strip()
																																															if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																						lowerCamelCase_							  =					leaf[1:-2]  # for categorical special tokens
																																															output[key].append(__UpperCamelCase   )
																																								if len(output[key]   ) == 1:
																																															lowerCamelCase_							  =					output[key][0]

																										lowerCamelCase_							  =					tokens[tokens.find(__UpperCamelCase   ) + len(__UpperCamelCase   ) :].strip()
																										if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																	return [output] + self.tokenajson(tokens[6:]   ,					is_inner_value=__UpperCamelCase   ,					added_vocab=__UpperCamelCase   )

												if len(__UpperCamelCase   ):
																			return [output] if is_inner_value else output
												else:
																			return [] if is_inner_value else {"text_sequence": tokens}

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



					@property
					def    lowercase__      (	self					:     Optional[int]   ):
												warnings.warn(
												    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,					__UpperCamelCase   ,					)
												return self.image_processor







from typing import TYPE_CHECKING

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


lowercase        =  {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

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

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''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        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





