
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						["""pixel_values"""]



		def __init__(      self				:			Optional[Any]  ,	UpperCamelCase__				:			str = True  ,	UpperCamelCase__				:			List[str] = None  ,	UpperCamelCase__				:			Optional[Any] = PILImageResampling.BICUBIC  ,	UpperCamelCase__				:			List[str] = True  ,	UpperCamelCase__				:			Optional[Any] = None  ,	UpperCamelCase__				:			int = True  ,	UpperCamelCase__				:			int = 1 / 255  ,	UpperCamelCase__				:			Tuple = True  ,	UpperCamelCase__				:			Union[str, Any] = IMAGENET_DEFAULT_MEAN  ,	UpperCamelCase__				:			Optional[int] = IMAGENET_DEFAULT_STD  ,	**UpperCamelCase__				:			int  ,	)						->			List[str]:






				"""simple docstring"""







				super().__init__(**__UpperCAmelCase      )
				snake_case  :       str     			=						size if size is not None else {'shortest_edge': 224}
				snake_case  :       int     			=						get_size_dict(__UpperCAmelCase  ,	default_to_square=__UpperCAmelCase      )
				snake_case  :       Dict     			=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
				snake_case  :       Union[str, Any]     			=						get_size_dict(__UpperCAmelCase  ,	param_name='''crop_size'''      )

				snake_case  :       int     			=						do_resize
				snake_case  :       str     			=						size
				snake_case  :       int     			=						resample
				snake_case  :       Tuple     			=						do_center_crop
				snake_case  :       List[str]     			=						crop_size
				snake_case  :       str     			=						do_rescale
				snake_case  :       Dict     			=						rescale_factor
				snake_case  :       Optional[int]     			=						do_normalize
				snake_case  :       int     			=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				snake_case  :       Union[str, Any]     			=						image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			int = PILImageResampling.BICUBIC  ,	UpperCamelCase__				:			Optional[int] = None  ,	**UpperCamelCase__				:			Dict  ,	)						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Any     			=						get_size_dict(__UpperCAmelCase  ,	default_to_square=__UpperCAmelCase      )
				# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
				if "shortest_edge" in size:
						snake_case  :       List[Any]     			=						int((256 / 224) * size['''shortest_edge''']      )
						snake_case  :       Union[str, Any]     			=						get_resize_output_image_size(__UpperCAmelCase  ,	size=__UpperCAmelCase  ,	default_to_square=__UpperCAmelCase      )
						snake_case  :       List[str]     			=						{'height': output_size[0], 'width': output_size[1]}
				if "height" not in size_dict or "width" not in size_dict:
						raise ValueError(
						    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'      )
				return resize(
				    __UpperCAmelCase  ,	size=(size_dict['''height'''], size_dict['''width'''])  ,	resample=__UpperCAmelCase  ,	data_format=__UpperCAmelCase  ,	**__UpperCAmelCase      )



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Optional[int] = None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			int:






				"""simple docstring"""







				snake_case  :       List[str]     			=						get_size_dict(__UpperCAmelCase      )
				if "height" not in size or "width" not in size:
						raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'      )
				return center_crop(__UpperCAmelCase  ,	size=(size['''height'''], size['''width'''])  ,	data_format=__UpperCAmelCase  ,	**__UpperCAmelCase      )



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[Any] = None  ,	**UpperCamelCase__				:			Union[str, Any]  ,	)						->			Tuple:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[Any] = None  ,	**UpperCamelCase__				:			List[Any]  ,	)						->			Tuple:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Dict = None  ,	UpperCamelCase__				:			List[str] = None  ,	UpperCamelCase__				:			Dict = None  ,	UpperCamelCase__				:			Optional[Any] = None  ,	UpperCamelCase__				:			Union[str, Any] = None  ,	UpperCamelCase__				:			int = None  ,	UpperCamelCase__				:			Tuple = None  ,	UpperCamelCase__				:			List[str] = None  ,	UpperCamelCase__				:			Dict = None  ,	UpperCamelCase__				:			List[Any] = None  ,	UpperCamelCase__				:			Any = None  ,	UpperCamelCase__				:			Optional[Any] = ChannelDimension.FIRST  ,	**UpperCamelCase__				:			Optional[int]  ,	)						->			List[str]:






				"""simple docstring"""







				snake_case  :       int     			=						do_resize if do_resize is not None else self.do_resize
				snake_case  :       List[Any]     			=						resample if resample is not None else self.resample
				snake_case  :       int     			=						do_center_crop if do_center_crop is not None else self.do_center_crop
				snake_case  :       str     			=						do_rescale if do_rescale is not None else self.do_rescale
				snake_case  :       int     			=						rescale_factor if rescale_factor is not None else self.rescale_factor
				snake_case  :       Optional[int]     			=						do_normalize if do_normalize is not None else self.do_normalize
				snake_case  :       List[str]     			=						image_mean if image_mean is not None else self.image_mean
				snake_case  :       int     			=						image_std if image_std is not None else self.image_std

				snake_case  :       Any     			=						size if size is not None else self.size
				snake_case  :       str     			=						get_size_dict(__UpperCAmelCase  ,	default_to_square=__UpperCAmelCase      )
				snake_case  :       Optional[Any]     			=						crop_size if crop_size is not None else self.crop_size
				snake_case  :       Tuple     			=						get_size_dict(__UpperCAmelCase  ,	param_name='''crop_size'''      )

				snake_case  :       Optional[int]     			=						make_list_of_images(__UpperCAmelCase      )

				if not valid_images(__UpperCAmelCase      ):
						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.'''      )

				# All transformations expect numpy arrays.
				snake_case  :       Union[str, Any]     			=						[to_numpy_array(__UpperCAmelCase      ) for image in images]

				if do_resize:
						snake_case  :       Optional[Any]     			=						[self.resize(__UpperCAmelCase  ,	__UpperCAmelCase  ,	__UpperCAmelCase      ) for image in images]

				if do_center_crop:
						snake_case  :       Dict     			=						[self.center_crop(__UpperCAmelCase  ,	__UpperCAmelCase      ) for image in images]

				if do_rescale:
						snake_case  :       str     			=						[self.rescale(__UpperCAmelCase  ,	__UpperCAmelCase      ) for image in images]

				if do_normalize:
						snake_case  :       Optional[Any]     			=						[self.normalize(__UpperCAmelCase  ,	__UpperCAmelCase  ,	__UpperCAmelCase      ) for image in images]

				snake_case  :       Optional[Any]     			=						[to_channel_dimension_format(__UpperCAmelCase  ,	__UpperCAmelCase      ) for image in images]

				snake_case  :       str     			=						{'pixel_values': images}
				return BatchFeature(data=__UpperCAmelCase  ,	tensor_type=__UpperCAmelCase      )









"""simple docstring"""





import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__A											=						{
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Union[str, Any]:





  """simple docstring"""


  if got_ver is None or want_ver is None:
    raise ValueError(
        F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
        F" reinstalling {pkg}."     )
  if not ops[op](version.parse(_SCREAMING_SNAKE_CASE     )	,  version.parse(_SCREAMING_SNAKE_CASE     )     ):
    raise ImportError(
        F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = None     )   ->None:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				F"\n{hint}" if hint is not None else ''

  # non-versioned check
  if re.match(r'^[\w_\-\d]+$'	,  _SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				requirement, None, None
  else:
    lowerCAmelCase__     :List[str]											=				re.findall(r'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'	,  _SCREAMING_SNAKE_CASE     )
    if not match:
      raise ValueError(
          'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
          F" got {requirement}"     )
    lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				match[0]
    lowerCAmelCase__     :List[Any]											=				want_full.split(','     )  # there could be multiple requirements
    lowerCAmelCase__     :Any											=				{}
    for w in want_range:
      lowerCAmelCase__     :Tuple											=				re.findall(r'^([\s!=<>]{1,2})(.+)'	,  _SCREAMING_SNAKE_CASE     )
      if not match:
        raise ValueError(
            'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
            F" but got {requirement}"     )
      lowerCAmelCase__				,							lowerCAmelCase__     :int											=				match[0]
      lowerCAmelCase__     :str											=				want_ver
      if op not in ops:
        raise ValueError(F"{requirement}: need one of {list(ops.keys()     )}, but got {op}"     )

    # special case
  if pkg == "python":
    lowerCAmelCase__     :Any											=				'.'.join([str(_SCREAMING_SNAKE_CASE     ) for x in sys.version_info[:3]]     )
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    return

  # check if any version is installed
  try:
    lowerCAmelCase__     :List[Any]											=				importlib.metadata.version(_SCREAMING_SNAKE_CASE     )
  except importlib.metadata.PackageNotFoundError:
    raise importlib.metadata.PackageNotFoundError(
        F"The '{requirement}' distribution was not found and is required by this application. {hint}"     )

  # check that the right version is installed if version number or a range was provided
  if want_ver is not None:
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )




def 					__A  (_SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[Any]											=				'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
  return require_version(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
