
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


lowercase_											=	logging.get_logger(__name__)





class   __UpperCamelCase   (    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	['''pixel_values''']



     def __init__(							self    :    List[Any]      ,		_A    :    bool = True      ,		_A    :    Dict[str, int] = None      ,		_A    :    PILImageResampling = PIL.Image.NEAREST      ,		_A    :    bool = False      ,		_A    :    Dict[str, int] = None      ,		_A    :    Union[int, float] = 1 / 255      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    bool = True      ,		_A    :    Optional[Union[float, List[float]]] = None      ,		_A    :    Optional[Union[float, List[float]]] = None      ,		_A    :    bool = True      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       super().__init__(**_A				)
       __SCREAMING_SNAKE_CASE     :					Any			=						size if size is not None else {'''height''': 346, '''width''': 346}
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						get_size_dict(_A				)
       __SCREAMING_SNAKE_CASE     :					str			=						crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
       __SCREAMING_SNAKE_CASE     :					Any			=						get_size_dict(_A      ,		param_name='''crop_size'''				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						do_resize
       __SCREAMING_SNAKE_CASE     :					Tuple			=						size
       __SCREAMING_SNAKE_CASE     :					Any			=						resample
       __SCREAMING_SNAKE_CASE     :					Tuple			=						do_center_crop
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						crop_size
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						do_rescale
       __SCREAMING_SNAKE_CASE     :					List[str]			=						rescale_factor
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						rescale_offset
       __SCREAMING_SNAKE_CASE     :					Tuple			=						do_normalize
       __SCREAMING_SNAKE_CASE     :					Dict			=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image_std if image_std is not None else IMAGENET_STANDARD_STD
       __SCREAMING_SNAKE_CASE     :					Any			=						include_top



     def 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    np.ndarray      ,		_A    :    Dict[str, int]      ,		_A    :    PILImageResampling = PIL.Image.NEAREST      ,		_A    :    Optional[Union[str, ChannelDimension]] = None      ,		**_A    :    List[Any]      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						get_size_dict(_A				)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''				)
       return resize(
           _A      ,		size=(size['''height'''], size['''width'''])      ,		resample=_A      ,		data_format=_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    np.ndarray      ,		_A    :    Dict[str, int]      ,		_A    :    Optional[Union[str, ChannelDimension]] = None      ,		**_A    :    str      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						get_size_dict(_A				)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''				)
       return center_crop(_A      ,		size=(size['''height'''], size['''width'''])      ,		data_format=_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    np.ndarray      ,		_A    :    Union[int, float]      ,		_A    :    bool = True      ,		_A    :    Optional[Union[str, ChannelDimension]] = None      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       if offset:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						(image - 127.5) * scale
         if data_format is not None:
           __SCREAMING_SNAKE_CASE     :					int			=						to_channel_dimension_format(_A      ,		_A				)
         __SCREAMING_SNAKE_CASE     :					int			=						rescaled_image.astype(np.floataa				)
       else:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						rescale(_A      ,		scale=_A      ,		data_format=_A      ,		**_A				)
       return rescaled_image



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    np.ndarray      ,		_A    :    Union[float, List[float]]      ,		_A    :    Union[float, List[float]]      ,		_A    :    Optional[Union[str, ChannelDimension]] = None      ,		**_A    :    int      ,		):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    str      ,		_A    :    ImageInput      ,		_A    :    bool = None      ,		_A    :    Dict[str, int] = None      ,		_A    :    Union[str, Any]=None      ,		_A    :    bool = None      ,		_A    :    Dict[str, int] = None      ,		_A    :    bool = None      ,		_A    :    float = None      ,		_A    :    bool = 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    :    ChannelDimension = ChannelDimension.FIRST      ,		**_A    :    Any      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						do_resize if do_resize is not None else self.do_resize
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						resample if resample is not None else self.resample
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						do_center_crop if do_center_crop is not None else self.do_center_crop
       __SCREAMING_SNAKE_CASE     :					Tuple			=						do_rescale if do_rescale is not None else self.do_rescale
       __SCREAMING_SNAKE_CASE     :					List[str]			=						rescale_factor if rescale_factor is not None else self.rescale_factor
       __SCREAMING_SNAKE_CASE     :					int			=						rescale_offset if rescale_offset is not None else self.rescale_offset
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						do_normalize if do_normalize is not None else self.do_normalize
       __SCREAMING_SNAKE_CASE     :					Any			=						image_mean if image_mean is not None else self.image_mean
       __SCREAMING_SNAKE_CASE     :					int			=						image_std if image_std is not None else self.image_std
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						include_top if include_top is not None else self.include_top

       __SCREAMING_SNAKE_CASE     :					Any			=						size if size is not None else self.size
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						get_size_dict(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						crop_size if crop_size is not None else self.crop_size
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						get_size_dict(_A      ,		param_name='''crop_size'''				)

       __SCREAMING_SNAKE_CASE     :					Any			=						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 or resample is None:
         raise ValueError('''Size and resample 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.
       __SCREAMING_SNAKE_CASE     :					Any			=						[to_numpy_array(_A				) for image in images]

       if do_resize:
         __SCREAMING_SNAKE_CASE     :					Any			=						[self.resize(image=_A      ,		size=_A      ,		resample=_A				) for image in images]

       if do_center_crop:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.center_crop(image=_A      ,		size=_A				) for image in images]

       if do_rescale:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.rescale(image=_A      ,		scale=_A      ,		offset=_A				) for image in images]

       if do_normalize:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.normalize(image=_A      ,		mean=_A      ,		std=_A				) for image in images]

       if include_top:
         __SCREAMING_SNAKE_CASE     :					int			=						[self.normalize(image=_A      ,		mean=[0, 0, 0]      ,		std=_A				) for image in images]

       __SCREAMING_SNAKE_CASE     :					List[str]			=						[to_channel_dimension_format(_A      ,		_A				) for image in images]

       __SCREAMING_SNAKE_CASE     :					str			=						{'''pixel_values''': images}
       return BatchFeature(data=_A      ,		tensor_type=_A				)


import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowercase_											=	"""src/transformers"""

# Matches is_xxx_available()
lowercase_											=	re.compile(R"""is\_([a-z_]*)_available()""")
# Matches from xxx import bla
lowercase_											=	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
lowercase_											=	re.compile(R"""^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)""")


lowercase_											=	"""
{0} = None
"""

lowercase_											=	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


lowercase_											=	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""


def     a__			(    snake_case    ):





  """simple docstring"""
  if _re_test_backend.search(snake_case    ) is None:
    return None
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[b[0] for b in _re_backend.findall(snake_case    )]
  backends.sort()
  return "_and_".join(snake_case    )


def     a__			(    ):





  """simple docstring"""
  with open(os.path.join(snake_case   ,	'''__init__.py'''    )   ,	'''r'''   ,	encoding='''utf-8'''   ,	newline='''\n'''    ) as f:
    __SCREAMING_SNAKE_CASE     :					Tuple			=						f.readlines()

  # Get to the point we do the actual imports for type checking
  __SCREAMING_SNAKE_CASE     :					Any			=						0
  while not lines[line_index].startswith('''if TYPE_CHECKING'''    ):
    line_index += 1

  __SCREAMING_SNAKE_CASE     :					Tuple			=						{}
  # Go through the end of the file
  while line_index < len(snake_case    ):
    # If the line is an if is_backend_available, we grab all objects associated.
    __SCREAMING_SNAKE_CASE     :					int			=						find_backend(lines[line_index]    )
    if backend is not None:
      while not lines[line_index].startswith('''    else:'''    ):
        line_index += 1
      line_index += 1

      __SCREAMING_SNAKE_CASE     :					Any			=						[]
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(''' ''' * 8    ):
        __SCREAMING_SNAKE_CASE     :					Dict			=						lines[line_index]
        __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						_re_single_line_import.search(snake_case    )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(''', '''    )    )
        elif line.startswith(''' ''' * 12    ):
          objects.append(line[12:-2]    )
        line_index += 1

      __SCREAMING_SNAKE_CASE     :					List[str]			=						objects
    else:
      line_index += 1

  return backend_specific_objects


def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  if name.isupper():
    return DUMMY_CONSTANT.format(snake_case    )
  elif name.islower():
    return DUMMY_FUNCTION.format(snake_case   ,	snake_case    )
  else:
    return DUMMY_CLASS.format(snake_case   ,	snake_case    )


def     a__			(    snake_case=None    ):





  """simple docstring"""
  if backend_specific_objects is None:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						{}

  for backend, objects in backend_specific_objects.items():
    __SCREAMING_SNAKE_CASE     :					Tuple			=						'''[''' + ''', '''.join(F'''"{b}"''' for b in backend.split('''_and_'''    )    ) + ''']'''
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						'''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(snake_case   ,	snake_case    ) for o in objects]    )
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						dummy_file

  return dummy_files


def     a__			(    snake_case=False    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  __SCREAMING_SNAKE_CASE     :					str			=						{'''torch''': '''pt'''}

  # Locate actual dummy modules and read their content.
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						os.path.join(snake_case   ,	'''utils'''    )
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						{
      backend: os.path.join(snake_case   ,	F'''dummy_{short_names.get(snake_case   ,	snake_case    )}_objects.py'''    )
      for backend in dummy_files.keys()
  }

  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(snake_case    ):
      with open(snake_case   ,	'''r'''   ,	encoding='''utf-8'''   ,	newline='''\n'''    ) as f:
        __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						f.read()
    else:
      __SCREAMING_SNAKE_CASE     :					Any			=						''''''

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F'''Updating transformers.utils.dummy_{short_names.get(snake_case   ,	snake_case    )}_objects.py as the main '''
            '''__init__ has new objects.'''    )
        with open(dummy_file_paths[backend]   ,	'''w'''   ,	encoding='''utf-8'''   ,	newline='''\n'''    ) as f:
          f.write(dummy_files[backend]    )
      else:
        raise ValueError(
            '''The main __init__ has objects that are not present in '''
            F'''transformers.utils.dummy_{short_names.get(snake_case   ,	snake_case    )}_objects.py. Run `make fix-copies` '''
            '''to fix this.'''    )


if __name__ == "__main__":
   lowercase_											=	argparse.ArgumentParser()
   parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
   lowercase_											=	parser.parse_args()

   check_dummies(args.fix_and_overwrite)
