
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						["image_processor", "tokenizer"]
							lowerCAmelCase_ 						=						"AutoImageProcessor"
							lowerCAmelCase_ 						=						"AutoTokenizer"

							def __init__(self  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  )					->       Any:
														_snake_case       =  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  ,  )
																					_snake_case       =  kwargs.pop("""feature_extractor"""  )

														_snake_case       =  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  )
														_snake_case       =  self.image_processor
														_snake_case       =  False

							def __call__(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       List[Any]:
														# For backward compatibility
														if self._in_target_context_manager:
																					return self.current_processor(*UpperCAmelCase  ,  **UpperCAmelCase  )

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

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

							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       Any:
														return self.tokenizer.batch_decode(*UpperCAmelCase  ,  **UpperCAmelCase  )

							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       Dict:
														return self.tokenizer.decode(*UpperCAmelCase  ,  **UpperCAmelCase  )

							@contextmanager
							def 	lowercase							(self  )					->       str:
														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."""  )
														_snake_case       =  True
														_snake_case       =  self.tokenizer
														yield
														_snake_case       =  self.image_processor
														_snake_case       =  False

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=False  ,  UpperCAmelCase=None  )					->       Dict:
														if added_vocab is None:
																					_snake_case       =  self.tokenizer.get_added_vocab()

														_snake_case       =  {}

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

																												_snake_case       =  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  )					->       List[Any]:
														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  )					->       str:
														warnings.warn(
														    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  UpperCAmelCase  ,  )
														return self.image_processor

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase												=	{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ResNetForImageClassification',
				    'ResNetModel',
				    'ResNetPreTrainedModel',
				    'ResNetBackbone',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFResNetForImageClassification',
				    'TFResNetModel',
				    'TFResNetPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'FlaxResNetForImageClassification',
				    'FlaxResNetModel',
				    'FlaxResNetPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_resnet import (
								    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ResNetBackbone,
								    ResNetForImageClassification,
								    ResNetModel,
								    ResNetPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_resnet import (
								    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFResNetForImageClassification,
								    TFResNetModel,
								    TFResNetPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure)