






import collections
import importlib.util
import os
import re
from pathlib import Path


A_		:		Optional[int]		     =			'src/transformers'


# Matches is_xxx_available()
A_		:		Tuple		     =			re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
A_		:		List[Any]		     =			re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A_		:		Tuple		     =			re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
A_		:		Optional[Any]		     =			re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
A_		:		str		     =			re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A_		:		Optional[Any]		     =			re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
A_		:		Union[str, Any]		     =			re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
A_		:		Optional[Any]		     =			re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
A_		:		List[Any]		     =			re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
A_		:		Tuple		     =			re.compile(r'^\s*try:')
# Catches a line with else:
A_		:		Tuple		     =			re.compile(r'^\s*else:')

def   snake_case (UpperCAmelCase__  )	->    List[str]:
  if _re_test_backend.search(UpperCAmelCase__  ) is None:
    return None
  UpperCamelCase_: Any   				=    [b[0] for b in _re_backend.findall(UpperCAmelCase__  )]
  backends.sort()
  return "_and_".join(UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__  )	->    Any:
  with open(UpperCAmelCase__     ,   'r'     ,   encoding='utf-8'     ,   newline='\n'  ) as f:
    UpperCamelCase_: Tuple   				=    f.readlines()

  UpperCamelCase_: List[str]   				=    0
  while line_index < len(UpperCAmelCase__  ) and not lines[line_index].startswith('_import_structure = {'  ):
    line_index += 1

  # If this is a traditional init, just return.
  if line_index >= len(UpperCAmelCase__  ):
    return None

  # First grab the objects without a specific backend in _import_structure
  UpperCamelCase_: Any   				=    []
  while not lines[line_index].startswith('if TYPE_CHECKING'  ) and find_backend(lines[line_index]  ) is None:
    UpperCamelCase_: Optional[Any]   				=    lines[line_index]
    # If we have everything on a single line, let's deal with it.
    if _re_one_line_import_struct.search(UpperCAmelCase__  ):
      UpperCamelCase_: List[str]   				=    _re_one_line_import_struct.search(UpperCAmelCase__  ).groups()[0]
      UpperCamelCase_: List[Any]   				=    re.findall('\[([^\]]+)\]'     ,   UpperCAmelCase__  )
      for imp in imports:
        objects.extend([obj[1:-1] for obj in imp.split(', '  )]  )
      line_index += 1
      continue
    UpperCamelCase_: str   				=    _re_import_struct_key_value.search(UpperCAmelCase__  )
    if single_line_import_search is not None:
      UpperCamelCase_: List[Any]   				=    [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '  ) if len(UpperCAmelCase__  ) > 0]
      objects.extend(UpperCAmelCase__  )
    elif line.startswith(' ' * 8 + '"'  ):
      objects.append(line[9:-3]  )
    line_index += 1

  UpperCamelCase_: Optional[int]   				=    {'none': objects}
  # Let's continue with backend-specific objects in _import_structure
  while not lines[line_index].startswith('if TYPE_CHECKING'  ):
    # If the line is an if not is_backend_available, we grab all objects associated.
    UpperCamelCase_: Tuple   				=    find_backend(lines[line_index]  )
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]  ) is None:
      UpperCamelCase_: Dict   				=    None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]  ) is None:
        line_index += 1

      line_index += 1

      UpperCamelCase_: Tuple   				=    []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]  ) <= 1 or lines[line_index].startswith(' ' * 4  ):
        UpperCamelCase_: Any   				=    lines[line_index]
        if _re_import_struct_add_one.search(UpperCAmelCase__  ) is not None:
          objects.append(_re_import_struct_add_one.search(UpperCAmelCase__  ).groups()[0]  )
        elif _re_import_struct_add_many.search(UpperCAmelCase__  ) is not None:
          UpperCamelCase_: Tuple   				=    _re_import_struct_add_many.search(UpperCAmelCase__  ).groups()[0].split(', '  )
          UpperCamelCase_: int   				=    [obj[1:-1] for obj in imports if len(UpperCAmelCase__  ) > 0]
          objects.extend(UpperCAmelCase__  )
        elif _re_between_brackets.search(UpperCAmelCase__  ) is not None:
          UpperCamelCase_: Union[str, Any]   				=    _re_between_brackets.search(UpperCAmelCase__  ).groups()[0].split(', '  )
          UpperCamelCase_: Optional[int]   				=    [obj[1:-1] for obj in imports if len(UpperCAmelCase__  ) > 0]
          objects.extend(UpperCAmelCase__  )
        elif _re_quote_object.search(UpperCAmelCase__  ) is not None:
          objects.append(_re_quote_object.search(UpperCAmelCase__  ).groups()[0]  )
        elif line.startswith(' ' * 8 + '"'  ):
          objects.append(line[9:-3]  )
        elif line.startswith(' ' * 1_2 + '"'  ):
          objects.append(line[1_3:-3]  )
        line_index += 1

      UpperCamelCase_: Tuple   				=    objects
    else:
      line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
  UpperCamelCase_: Optional[Any]   				=    []
  while (
      line_index < len(UpperCAmelCase__  )
      and find_backend(lines[line_index]  ) is None
      and not lines[line_index].startswith('else'  )
  ):
    UpperCamelCase_: List[Any]   				=    lines[line_index]
    UpperCamelCase_: Optional[Any]   				=    _re_import.search(UpperCAmelCase__  )
    if single_line_import_search is not None:
      objects.extend(single_line_import_search.groups()[0].split(', '  )  )
    elif line.startswith(' ' * 8  ):
      objects.append(line[8:-2]  )
    line_index += 1

  UpperCamelCase_: str   				=    {'none': objects}
  # Let's continue with backend-specific objects
  while line_index < len(UpperCAmelCase__  ):
    # If the line is an if is_backend_available, we grab all objects associated.
    UpperCamelCase_: Dict   				=    find_backend(lines[line_index]  )
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]  ) is None:
      UpperCamelCase_: Dict   				=    None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]  ) is None:
        line_index += 1

      line_index += 1

      UpperCamelCase_: Union[str, Any]   				=    []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]  ) <= 1 or lines[line_index].startswith(' ' * 8  ):
        UpperCamelCase_: Any   				=    lines[line_index]
        UpperCamelCase_: int   				=    _re_import.search(UpperCAmelCase__  )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(', '  )  )
        elif line.startswith(' ' * 1_2  ):
          objects.append(line[1_2:-2]  )
        line_index += 1

      UpperCamelCase_: Union[str, Any]   				=    objects
    else:
      line_index += 1

  return import_dict_objects, type_hint_objects

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    str:

  def find_duplicates(UpperCAmelCase__  ):
    return [k for k, v in collections.Counter(UpperCAmelCase__  ).items() if v > 1]

  if list(import_dict_objects.keys()  ) != list(type_hint_objects.keys()  ):
    return ["Both sides of the init do not have the same backends!"]

  UpperCamelCase_: List[Any]   				=    []
  for key in import_dict_objects.keys():
    UpperCamelCase_: Dict   				=    find_duplicates(import_dict_objects[key]  )
    if duplicate_imports:
      errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''  )
    UpperCamelCase_: Any   				=    find_duplicates(type_hint_objects[key]  )
    if duplicate_type_hints:
      errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''  )

    if sorted(set(import_dict_objects[key]  )  ) != sorted(set(type_hint_objects[key]  )  ):
      UpperCamelCase_: int   				=    'base imports' if key == 'none' else F'''{key} backend'''
      errors.append(F'''Differences for {name}:'''  )
      for a in type_hint_objects[key]:
        if a not in import_dict_objects[key]:
          errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.'''  )
      for a in import_dict_objects[key]:
        if a not in type_hint_objects[key]:
          errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.'''  )
  return errors

def   snake_case ()	->    Any:
  UpperCamelCase_: int   				=    []
  for root, _, files in os.walk(UpperCAmelCase__  ):
    if "__init__.py" in files:
      UpperCamelCase_: Any   				=    os.path.join(UpperCAmelCase__     ,   '__init__.py'  )
      UpperCamelCase_: Dict   				=    parse_init(UpperCAmelCase__  )
      if objects is not None:
        UpperCamelCase_: Optional[int]   				=    analyze_results(*UpperCAmelCase__  )
        if len(UpperCAmelCase__  ) > 0:
          UpperCamelCase_: Dict   				=    F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
          failures.append('\n'.join(UpperCAmelCase__  )  )
  if len(UpperCAmelCase__  ) > 0:
    raise ValueError('\n\n'.join(UpperCAmelCase__  )  )

def   snake_case ()	->    str:
  UpperCamelCase_: List[str]   				=    []
  for path, directories, files in os.walk(UpperCAmelCase__  ):
    for folder in directories:
      # Ignore private modules
      if folder.startswith('_'  ):
        directories.remove(UpperCAmelCase__  )
        continue
      # Ignore leftovers from branches (empty folders apart from pycache)
      if len(list((Path(UpperCAmelCase__  ) / folder).glob('*.py'  )  )  ) == 0:
        continue
      UpperCamelCase_: str   				=    str((Path(UpperCAmelCase__  ) / folder).relative_to(UpperCAmelCase__  )  )
      UpperCamelCase_: Dict   				=    short_path.replace(os.path.sep     ,   '.'  )
      submodules.append(UpperCAmelCase__  )
    for fname in files:
      if fname == "__init__.py":
        continue
      UpperCamelCase_: Dict   				=    str((Path(UpperCAmelCase__  ) / fname).relative_to(UpperCAmelCase__  )  )
      UpperCamelCase_: Optional[int]   				=    short_path.replace('.py'     ,   ''  ).replace(os.path.sep     ,   '.'  )
      if len(submodule.split('.'  )  ) == 1:
        submodules.append(UpperCAmelCase__  )
  return submodules


A_		:		str		     =			[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]

def   snake_case ()	->    str:
  # This is to make sure the transformers module imported is the one in the repo.
  UpperCamelCase_: Tuple   				=    importlib.util.spec_from_file_location(
      'transformers'     ,   os.path.join(UpperCAmelCase__     ,   '__init__.py'  )     ,   submodule_search_locations=[PATH_TO_TRANSFORMERS]     ,   )
  UpperCamelCase_: List[str]   				=    spec.loader.load_module()

  UpperCamelCase_: List[Any]   				=    [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
  ]
  if len(UpperCAmelCase__  ) > 0:
    UpperCamelCase_: Optional[int]   				=    '\n'.join(F'''- {module}''' for module in module_not_registered  )
    raise ValueError(
        'The following submodules are not properly registered in the main init of Transformers:\n'
        F'''{list_of_modules}\n'''
        'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'  )


if __name__ == "__main__":
   check_all_inits()
   check_submodules()







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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_		:		int		     =			logging.get_logger(__name__)




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: Any   				=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: Any   				=    get_size_dict(_lowerCamelCase							)
         UpperCamelCase_: Any   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: List[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase  ,      param_name='crop_size'							)

         UpperCamelCase_: Optional[int]   				=    do_resize
         UpperCamelCase_: Tuple   				=    do_rescale
         UpperCamelCase_: Dict   				=    do_normalize
         UpperCamelCase_: Optional[int]   				=    do_center_crop
         UpperCamelCase_: Tuple   				=    crop_size
         UpperCamelCase_: Optional[int]   				=    size
         UpperCamelCase_: Dict   				=    resample
         UpperCamelCase_: Tuple   				=    rescale_factor
         UpperCamelCase_: Tuple   				=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else IMAGENET_DEFAULT_STD





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if "shortest_edge" in size:
           UpperCamelCase_: str   				=    get_resize_output_image_size(_lowerCamelCase  ,      size=size['shortest_edge']  ,      default_to_square=_lowerCamelCase							)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
           UpperCamelCase_: Any   				=    (size['height'], size['width'])
         else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''							)
         return resize(_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase							)
         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(_lowerCamelCase  ,      size=(size['height'], size['width'])  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase							):
         return rescale(_lowerCamelCase  ,      scale=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: str   				=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_: Dict   				=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_: List[str]   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Optional[Any]   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    resample if resample is not None else self.resample
         UpperCamelCase_: str   				=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_: Dict   				=    image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else self.image_std

         UpperCamelCase_: Any   				=    size if size is not None else self.size
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if not is_batched(_lowerCamelCase							):
           UpperCamelCase_: Dict   				=    [images]

         if not valid_images(_lowerCamelCase							):
           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.'							)

         # All transformations expect numpy arrays.
         UpperCamelCase_: List[str]   				=    [to_numpy_array(_lowerCamelCase							) for image in images]

         if do_resize:
           UpperCamelCase_: List[str]   				=    [self.resize(image=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase							) for image in images]

         if do_center_crop:
           UpperCamelCase_: List[str]   				=    [self.center_crop(image=_lowerCamelCase  ,      size=_lowerCamelCase							) for image in images]

         if do_rescale:
           UpperCamelCase_: int   				=    [self.rescale(image=_lowerCamelCase  ,      scale=_lowerCamelCase							) for image in images]

         if do_normalize:
           UpperCamelCase_: Optional[Any]   				=    [self.normalize(image=_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase							) for image in images]

         UpperCamelCase_: List[str]   				=    [to_channel_dimension_format(_lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         UpperCamelCase_: Optional[int]   				=    {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase  ,      tensor_type=_lowerCamelCase							)