




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''image_processor''', '''tokenizer''']
 UpperCamelCase						   =						'''AutoImageProcessor'''
 UpperCamelCase						   =						'''AutoTokenizer'''

 def __init__(				self					:  Dict						,							A_					:  Optional[Any]=None						,							A_					:  Any=None						,							**A_					:  Dict  )   ->	str:

       """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					:  Tuple						,							*A_					:  List[str]						,							**A_					:  Optional[Any]  )   ->	Optional[int]:

       """simple docstring"""






       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 				a__ (				self					:  Optional[int]						,							*A_					:  Optional[Any]						,							**A_					:  Optional[int]  )   ->	Optional[int]:

       """simple docstring"""






       return self.tokenizer.batch_decode(*A_						,							**A_  )

 def 				a__ (				self					:  List[Any]						,							*A_					:  Any						,							**A_					:  Optional[int]  )   ->	List[str]:

       """simple docstring"""






       return self.tokenizer.decode(*A_						,							**A_  )

 @contextmanager
 def 				a__ (				self					:  Dict  )   ->	Optional[int]:

       """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 				a__ (				self					:  int						,							A_					:  Optional[int]						,							A_					:  Any=False						,							A_					:  int=None  )   ->	Union[str, Any]:

       """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 				a__ (				self					:  Optional[Any]  )   ->	Dict:

       """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 				a__ (				self					:  Any  )   ->	Tuple:

       """simple docstring"""






       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,							A_						,							)
       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


lowerCamelCase				:		Optional[Any]     =   logging.get_logger(__name__)


if is_vision_available():
 import PIL







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  List[Any]						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  bool = True						,							A_					:  Union[int, float] = 1 / 255						,							A_					:  bool = True						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = True						,							**A_					:  Dict						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'shortest_edge': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       lowerCamelCase_		     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_						,							param_name='crop_size'  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_center_crop
       lowerCamelCase_		     =    crop_size
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_factor
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else OPENAI_CLIP_STD
       lowerCamelCase_		     =    do_convert_rgb

 def 				a__ (				self					:  Dict						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
       lowerCamelCase_		     =    get_resize_output_image_size(A_						,							size=size['shortest_edge']						,							default_to_square=A_  )
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Tuple						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Dict						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       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(A_						,							size=(size['height'], size['width'])						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Union[str, Any]						,							)   ->	str:

       """simple docstring"""






       return rescale(A_						,							scale=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Optional[Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, List[float]]						,							A_					:  Union[float, List[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






       return normalize(A_						,							mean=A_						,							std=A_						,							data_format=A_						,							**A_  )





 def 				a__ (				self					:  Any						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = None						,							A_					:  bool = None						,							A_					:  int = None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  Optional[ChannelDimension] = ChannelDimension.FIRST						,							**A_					:  Dict						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_		     =    crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='crop_size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std
       lowerCamelCase_		     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowerCamelCase_		     =    make_list_of_images(A_  )

       if not valid_images(A_  ):
             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:
             lowerCamelCase_		     =    [convert_to_rgb(A_  ) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase_		     =    [to_numpy_array(A_  ) for image in images]

       if do_resize:
             lowerCamelCase_		     =    [self.resize(image=A_						,							size=A_						,							resample=A_  ) for image in images]

       if do_center_crop:
             lowerCamelCase_		     =    [self.center_crop(image=A_						,							size=A_  ) for image in images]

       if do_rescale:
             lowerCamelCase_		     =    [self.rescale(image=A_						,							scale=A_  ) for image in images]

       if do_normalize:
             lowerCamelCase_		     =    [self.normalize(image=A_						,							mean=A_						,							std=A_  ) for image in images]

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    {'pixel_values': images}
       return BatchFeature(data=A_						,							tensor_type=A_  )



