





'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class  UpperCAmelCase     (			snake_case_   ):
      _lowercase:			Union[str, Any]						     =   ['''image_processor''', '''tokenizer''']
      _lowercase:			int						     =   '''AutoImageProcessor'''
      _lowercase:			Optional[int]						     =   '''AutoTokenizer'''




      def __init__(			self			: int			,			__snake_case			: Tuple=None			,			__snake_case			: Optional[int]=None			,			**__snake_case			: Tuple  )				->      List[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."""			,			__snake_case			,			)
                  _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__(__snake_case			,			__snake_case  )
            _lowerCAmelCase							     =							self.image_processor
            _lowerCAmelCase							     =							False




      def __call__(			self			: Dict			,			*__snake_case			: Optional[int]			,			**__snake_case			: Union[str, Any]  )				->      Tuple:
            # For backward compatibility
            if self._in_target_context_manager:
                  return self.current_processor(*__snake_case			,			**__snake_case  )

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

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




      def 				lowercase__    (			self			: List[Any]			,			*__snake_case			: Dict			,			**__snake_case			: List[str]  )				->      int:
            return self.tokenizer.batch_decode(*__snake_case			,			**__snake_case  )




      def 				lowercase__    (			self			: int			,			*__snake_case			: Tuple			,			**__snake_case			: Optional[Any]  )				->      Any:
            return self.tokenizer.decode(*__snake_case			,			**__snake_case  )




      @contextmanager
      def 				lowercase__    (			self			: int  )				->      Optional[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			: Optional[int]			,			__snake_case			: Union[str, Any]			,			__snake_case			: List[Any]=False			,			__snake_case			: Dict=None  )				->      Tuple:
            if added_vocab is None:
                  _lowerCAmelCase							     =							self.tokenizer.get_added_vocab()

            _lowerCAmelCase							     =							{}

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

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

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




      @property
      def 				lowercase__    (			self			: Optional[int]  )				->      Union[str, Any]:
            warnings.warn(
                """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			__snake_case			,			)
            return self.image_processor_class




      @property
      def 				lowercase__    (			self			: List[Any]  )				->      Any:
            warnings.warn(
                """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			__snake_case			,			)
            return self.image_processor







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase  							=						logging.get_logger(__name__)


if is_vision_available():
      import PIL



class   snake_case_       (		__A       ):
   __A	:		str         =				["pixel_values"]






   def __init__(      self				:     int	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[int, float] = 1 / 2_55	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = True	,						**lowercase_				:     Union[str, Any]	,						)					->						None:
       super().__init__(**lowercase_  )
       lowercase__ :   Tuple    					=				size if size is not None else {"shortest_edge": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       lowercase__ :   List[str]    					=				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_	,						param_name="crop_size"  )

       lowercase__ :   Dict    					=				do_resize
       lowercase__ :   List[Any]    					=				size
       lowercase__ :   int    					=				resample
       lowercase__ :   Union[str, Any]    					=				do_center_crop
       lowercase__ :   Optional[int]    					=				crop_size
       lowercase__ :   List[str]    					=				do_rescale
       lowercase__ :   int    					=				rescale_factor
       lowercase__ :   List[Any]    					=				do_normalize
       lowercase__ :   Union[str, Any]    					=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowercase__ :   str    					=				image_std if image_std is not None else OPENAI_CLIP_STD
       lowercase__ :   Dict    					=				do_convert_rgb






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Union[str, Any]	,						)					->						np.ndarray:
       lowercase__ :   str    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
       lowercase__ :   Dict    					=				get_resize_output_image_size(lowercase_	,						size=size["shortest_edge"]	,						default_to_square=lowercase_  )
       return resize(lowercase_	,						size=lowercase_	,						resample=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     int	,						)					->						np.ndarray:
       lowercase__ :   Optional[Any]    					=				get_size_dict(lowercase_  )
       if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''  )
       return center_crop(lowercase_	,						size=(size["height"], size["width"])	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[int, float]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Optional[Any]	,						)					->						Any:
       return rescale(lowercase_	,						scale=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     str	,						)					->						np.ndarray:
       return normalize(lowercase_	,						mean=lowercase_	,						std=lowercase_	,						data_format=lowercase_	,						**lowercase_  )







   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     ImageInput	,						lowercase_				:     bool = None	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = None	,						lowercase_				:     bool = None	,						lowercase_				:     int = None	,						lowercase_				:     bool = None	,						lowercase_				:     float = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						lowercase_				:     Optional[ChannelDimension] = ChannelDimension.FIRST	,						**lowercase_				:     Union[str, Any]	,						)					->						PIL.Image.Image:
       lowercase__ :   int    					=				do_resize if do_resize is not None else self.do_resize
       lowercase__ :   Dict    					=				size if size is not None else self.size
       lowercase__ :   List[Any]    					=				get_size_dict(lowercase_	,						param_name="size"	,						default_to_square=lowercase_  )
       lowercase__ :   Dict    					=				resample if resample is not None else self.resample
       lowercase__ :   int    					=				do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase__ :   Dict    					=				crop_size if crop_size is not None else self.crop_size
       lowercase__ :   List[str]    					=				get_size_dict(lowercase_	,						param_name="crop_size"	,						default_to_square=lowercase_  )
       lowercase__ :   int    					=				do_rescale if do_rescale is not None else self.do_rescale
       lowercase__ :   Dict    					=				rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase__ :   Union[str, Any]    					=				do_normalize if do_normalize is not None else self.do_normalize
       lowercase__ :   int    					=				image_mean if image_mean is not None else self.image_mean
       lowercase__ :   List[str]    					=				image_std if image_std is not None else self.image_std
       lowercase__ :   Union[str, Any]    					=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowercase__ :   Union[str, Any]    					=				make_list_of_images(lowercase_  )

       if not valid_images(lowercase_  ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."  )

       if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."  )

       if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."  )

       if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."  )

       if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
           lowercase__ :   Dict    					=				[convert_to_rgb(lowercase_  ) for image in images]

       # All transformations expect numpy arrays.
       lowercase__ :   Optional[Any]    					=				[to_numpy_array(lowercase_  ) for image in images]

       if do_resize:
           lowercase__ :   List[Any]    					=				[self.resize(image=lowercase_	,						size=lowercase_	,						resample=lowercase_  ) for image in images]

       if do_center_crop:
           lowercase__ :   int    					=				[self.center_crop(image=lowercase_	,						size=lowercase_  ) for image in images]

       if do_rescale:
           lowercase__ :   str    					=				[self.rescale(image=lowercase_	,						scale=lowercase_  ) for image in images]

       if do_normalize:
           lowercase__ :   Optional[int]    					=				[self.normalize(image=lowercase_	,						mean=lowercase_	,						std=lowercase_  ) for image in images]

       lowercase__ :   Optional[Any]    					=				[to_channel_dimension_format(lowercase_	,						lowercase_  ) for image in images]

       lowercase__ :   List[str]    					=				{"pixel_values": images}
       return BatchFeature(data=lowercase_	,						tensor_type=lowercase_  )



