




"""simple docstring"""




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		["image_processor", "tokenizer"]
				__UpperCamelCase    						=		"AutoImageProcessor"
				__UpperCamelCase    						=		"AutoTokenizer"





				def __init__( self	,				_a=None	,				_a=None	,				**_a				):





							"""simple docstring"""
							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				)
							lowerCamelCase       =      self.image_processor
							lowerCamelCase       =      False





				def __call__( self	,				*_a	,				**_a				):





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

							lowerCamelCase       =      kwargs.pop("""images"""	,				_a				)
							lowerCamelCase       =      kwargs.pop("""text"""	,				_a				)
							if len(_a				) > 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(_a	,				*_a	,				**_a				)
							if text is not None:
										lowerCamelCase       =      self.tokenizer(_a	,				**_a				)

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





				def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





							"""simple docstring"""
							return self.tokenizer.batch_decode(*_a	,				**_a				)





				def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





							"""simple docstring"""
							return self.tokenizer.decode(*_a	,				**_a				)





				@contextmanager
				def      _lowerCAmelCase       ( self				):





							"""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."""				)
							lowerCamelCase       =      True
							lowerCamelCase       =      self.tokenizer
							yield
							lowerCamelCase       =      self.image_processor
							lowerCamelCase       =      False





				def      _lowerCAmelCase       ( self	,				_a	,				_a=False	,				_a=None				):





							"""simple docstring"""
							if added_vocab is None:
										lowerCamelCase       =      self.tokenizer.get_added_vocab()

							lowerCamelCase       =      {}

							while tokens:
										lowerCamelCase       =      re.search(r"""<s_(.*?)>"""	,				_a	,				re.IGNORECASE				)
										if start_token is None:
													break
										lowerCamelCase       =      start_token.group(1				)
										lowerCamelCase       =      re.search(rf'</s_{key}>'	,				_a	,				re.IGNORECASE				)
										lowerCamelCase       =      start_token.group()
										if end_token is None:
													lowerCamelCase       =      tokens.replace(_a	,				""""""				)
										else:
													lowerCamelCase       =      end_token.group()
													lowerCamelCase       =      re.escape(_a				)
													lowerCamelCase       =      re.escape(_a				)
													lowerCamelCase       =      re.search(f'{start_token_escaped}(.*?){end_token_escaped}'	,				_a	,				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(_a	,				is_inner_value=_a	,				added_vocab=_a				)
																			if value:
																						if len(_a				) == 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(_a				)
																			if len(output[key]				) == 1:
																						lowerCamelCase       =      output[key][0]

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

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





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,				_a	,				)
							return self.image_processor_class





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,				_a	,				)
							return self.image_processor









"""simple docstring"""




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:    List[str]  							=							{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Tuple  							=							[
					    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AutoformerForPrediction""",
					    """AutoformerModel""",
					    """AutoformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_autoformer import (
					    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    AutoformerConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_autoformer import (
										    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AutoformerForPrediction,
										    AutoformerModel,
										    AutoformerPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase							:    str  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



