



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	List[Any]    =['image_processor', 'tokenizer']
							__lowerCamelCase	:	Dict    ='AutoImageProcessor'
							__lowerCamelCase	:	Union[str, Any]    ='AutoTokenizer'





							def __init__(   self		:		Optional[int]      ,				__lowercase		:		Dict=None      ,				__lowercase		:		Dict=None      ,				**__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		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      ,				)
															__a       =		kwargs.pop("""feature_extractor"""	)

											__a       =		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	)
											__a       =		self.image_processor
											__a       =		False





							def __call__(   self		:		int      ,				*__lowercase		:		List[Any]      ,				**__lowercase		:		List[str]	):

											'''simple docstring'''
											# For backward compatibility
											if self._in_target_context_manager:
															return self.current_processor(*__lowercase      ,				**__lowercase	)

											__a       =		kwargs.pop("""images"""      ,				__lowercase	)
											__a       =		kwargs.pop("""text"""      ,				__lowercase	)
											if len(__lowercase	) > 0:
															__a       =		args[0]
															__a       =		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:
															__a       =		self.image_processor(__lowercase      ,				*__lowercase      ,				**__lowercase	)
											if text is not None:
															__a       =		self.tokenizer(__lowercase      ,				**__lowercase	)

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





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				*__lowercase		:		int      ,				**__lowercase		:		Any	):

											'''simple docstring'''
											return self.tokenizer.batch_decode(*__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Any      ,				*__lowercase		:		int      ,				**__lowercase		:		Optional[int]	):

											'''simple docstring'''
											return self.tokenizer.decode(*__lowercase      ,				**__lowercase	)





							@contextmanager
							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											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."""	)
											__a       =		True
											__a       =		self.tokenizer
											yield
											__a       =		self.image_processor
											__a       =		False





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		List[Any]      ,				__lowercase		:		List[str]=False      ,				__lowercase		:		int=None	):

											'''simple docstring'''
											if added_vocab is None:
															__a       =		self.tokenizer.get_added_vocab()

											__a       =		{}

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

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

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





							@property
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											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 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											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_tokenizers_available, is_torch_available


lowerCamelCase__			= {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NezhaForNextSentencePrediction""",
						    """NezhaForMaskedLM""",
						    """NezhaForPreTraining""",
						    """NezhaForMultipleChoice""",
						    """NezhaForQuestionAnswering""",
						    """NezhaForSequenceClassification""",
						    """NezhaForTokenClassification""",
						    """NezhaModel""",
						    """NezhaPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
