





import argparse
import os
import re


UpperCAmelCase_           =	"src/diffusers"

# Pattern that looks at the indentation in a line.
UpperCAmelCase_           =	re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase_           =	re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCAmelCase_           =	re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCAmelCase_           =	re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCAmelCase_           =	re.compile(r"\[([^\]]+)\]")
def      A__       (    SCREAMING_SNAKE_CASE_   :      Tuple						)  ->  Tuple:






   """simple docstring"""







   _UpperCAmelCase           =   _re_indent.search(SCREAMING_SNAKE_CASE_						)
   return "" if search is None else search.groups()[0]
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict				,	SCREAMING_SNAKE_CASE_   :      Optional[int]=""				,	SCREAMING_SNAKE_CASE_   :      int=None				,	SCREAMING_SNAKE_CASE_   :      Tuple=None						)  ->  Any:






   """simple docstring"""







   _UpperCAmelCase           =   0
   _UpperCAmelCase           =   code.split('''\n'''						)
   if start_prompt is not None:
      while not lines[index].startswith(SCREAMING_SNAKE_CASE_						):
         index += 1
      _UpperCAmelCase           =   ['''\n'''.join(lines[:index]						)]
   else:
      _UpperCAmelCase           =   []

   # We split into blocks until we get to the `end_prompt` (or the end of the block).
   _UpperCAmelCase           =   [lines[index]]
   index += 1
   while index < len(SCREAMING_SNAKE_CASE_						) and (end_prompt is None or not lines[index].startswith(SCREAMING_SNAKE_CASE_						)):
      if len(lines[index]						) > 0 and get_indent(lines[index]						) == indent_level:
         if len(SCREAMING_SNAKE_CASE_						) > 0 and get_indent(current_block[-1]						).startswith(indent_level + ''' '''						):
            current_block.append(lines[index]						)
            blocks.append('''\n'''.join(SCREAMING_SNAKE_CASE_						)						)
            if index < len(SCREAMING_SNAKE_CASE_						) - 1:
               _UpperCAmelCase           =   [lines[index + 1]]
               index += 1
            else:
               _UpperCAmelCase           =   []
         else:
            blocks.append('''\n'''.join(SCREAMING_SNAKE_CASE_						)						)
            _UpperCAmelCase           =   [lines[index]]
      else:
         current_block.append(lines[index]						)
      index += 1

   # Adds current block if it's nonempty.
   if len(SCREAMING_SNAKE_CASE_						) > 0:
      blocks.append('''\n'''.join(SCREAMING_SNAKE_CASE_						)						)

   # Add final block after end_prompt if provided.
   if end_prompt is not None and index < len(SCREAMING_SNAKE_CASE_						):
      blocks.append('''\n'''.join(lines[index:]						)						)

   return blocks
def      A__       (    SCREAMING_SNAKE_CASE_   :      Tuple						)  ->  Tuple:






   """simple docstring"""







   def _inner(SCREAMING_SNAKE_CASE_   :      Optional[Any]						):
      return key(SCREAMING_SNAKE_CASE_						).lower().replace('''_'''				,	''''''						)

   return _inner
def      A__       (    SCREAMING_SNAKE_CASE_   :      Union[str, Any]				,	SCREAMING_SNAKE_CASE_   :      Any=None						)  ->  int:






   """simple docstring"""







   def noop(SCREAMING_SNAKE_CASE_   :      Optional[Any]						):
      return x

   if key is None:
      _UpperCAmelCase           =   noop
   # Constants are all uppercase, they go first.
   _UpperCAmelCase           =   [obj for obj in objects if key(SCREAMING_SNAKE_CASE_						).isupper()]
   # Classes are not all uppercase but start with a capital, they go second.
   _UpperCAmelCase           =   [obj for obj in objects if key(SCREAMING_SNAKE_CASE_						)[0].isupper() and not key(SCREAMING_SNAKE_CASE_						).isupper()]
   # Functions begin with a lowercase, they go last.
   _UpperCAmelCase           =   [obj for obj in objects if not key(SCREAMING_SNAKE_CASE_						)[0].isupper()]

   _UpperCAmelCase           =   ignore_underscore(SCREAMING_SNAKE_CASE_						)
   return sorted(SCREAMING_SNAKE_CASE_				,	key=SCREAMING_SNAKE_CASE_						) + sorted(SCREAMING_SNAKE_CASE_				,	key=SCREAMING_SNAKE_CASE_						) + sorted(SCREAMING_SNAKE_CASE_				,	key=SCREAMING_SNAKE_CASE_						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[Any]						)  ->  Dict:






   """simple docstring"""







   def _replace(SCREAMING_SNAKE_CASE_   :      Union[str, Any]						):
      _UpperCAmelCase           =   match.groups()[0]
      if "," not in imports:
         return F'''[{imports}]'''
      _UpperCAmelCase           =   [part.strip().replace('''"'''				,	''''''						) for part in imports.split(''','''						)]
      # We will have a final empty element if the line finished with a comma.
      if len(keys[-1]						) == 0:
         _UpperCAmelCase           =   keys[:-1]
      return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(SCREAMING_SNAKE_CASE_						)]						) + "]"

   _UpperCAmelCase           =   import_statement.split('''\n'''						)
   if len(SCREAMING_SNAKE_CASE_						) > 3:
      # Here we have to sort internal imports that are on several lines (one per name):
      # key: [
      #     "object1",
      #     "object2",
      #     ...
      # ]

      # We may have to ignore one or two lines on each side.
      _UpperCAmelCase           =   2 if lines[1].strip() == '''[''' else 1
      _UpperCAmelCase           =   [(i, _re_strip_line.search(SCREAMING_SNAKE_CASE_						).groups()[0]) for i, line in enumerate(lines[idx:-idx]						)]
      _UpperCAmelCase           =   sort_objects(SCREAMING_SNAKE_CASE_				,	key=lambda SCREAMING_SNAKE_CASE_						: x[1]						)
      _UpperCAmelCase           =   [lines[x[0] + idx] for x in sorted_indices]
      return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]						)
   elif len(SCREAMING_SNAKE_CASE_						) == 3:
      # Here we have to sort internal imports that are on one separate line:
      # key: [
      #     "object1", "object2", ...
      # ]
      if _re_bracket_content.search(lines[1]						) is not None:
         _UpperCAmelCase           =   _re_bracket_content.sub(_replace				,	lines[1]						)
      else:
         _UpperCAmelCase           =   [part.strip().replace('''"'''				,	''''''						) for part in lines[1].split(''','''						)]
         # We will have a final empty element if the line finished with a comma.
         if len(keys[-1]						) == 0:
            _UpperCAmelCase           =   keys[:-1]
         _UpperCAmelCase           =   get_indent(lines[1]						) + ''', '''.join([F'''"{k}"''' for k in sort_objects(SCREAMING_SNAKE_CASE_						)]						)
      return "\n".join(SCREAMING_SNAKE_CASE_						)
   else:
      # Finally we have to deal with imports fitting on one line
      _UpperCAmelCase           =   _re_bracket_content.sub(_replace				,	SCREAMING_SNAKE_CASE_						)
      return import_statement
def      A__       (    SCREAMING_SNAKE_CASE_   :      Union[str, Any]				,	SCREAMING_SNAKE_CASE_   :      List[str]=True						)  ->  str:






   """simple docstring"""







   with open(SCREAMING_SNAKE_CASE_				,	'''r'''						) as f:
      _UpperCAmelCase           =   f.read()

   if "_import_structure" not in code:
      return

   # Blocks of indent level 0
   _UpperCAmelCase           =   split_code_in_indented_blocks(
       SCREAMING_SNAKE_CASE_				,	start_prompt='''_import_structure = {'''				,	end_prompt='''if TYPE_CHECKING:'''						)

   # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
   for block_idx in range(1				,	len(SCREAMING_SNAKE_CASE_						) - 1						):
      # Check if the block contains some `_import_structure`s thingy to sort.
      _UpperCAmelCase           =   main_blocks[block_idx]
      _UpperCAmelCase           =   block.split('''\n'''						)

      # Get to the start of the imports.
      _UpperCAmelCase           =   0
      while line_idx < len(SCREAMING_SNAKE_CASE_						) and "_import_structure" not in block_lines[line_idx]:
         # Skip dummy import blocks
         if "import dummy" in block_lines[line_idx]:
            _UpperCAmelCase           =   len(SCREAMING_SNAKE_CASE_						)
         else:
            line_idx += 1
      if line_idx >= len(SCREAMING_SNAKE_CASE_						):
         continue

      # Ignore beginning and last line: they don't contain anything.
      _UpperCAmelCase           =   '''\n'''.join(block_lines[line_idx:-1]						)
      _UpperCAmelCase           =   get_indent(block_lines[1]						)
      # Slit the internal block into blocks of indent level 1.
      _UpperCAmelCase           =   split_code_in_indented_blocks(SCREAMING_SNAKE_CASE_				,	indent_level=SCREAMING_SNAKE_CASE_						)
      # We have two categories of import key: list or _import_structure[key].append/extend
      _UpperCAmelCase           =   _re_direct_key if '''_import_structure''' in block_lines[0] else _re_indirect_key
      # Grab the keys, but there is a trap: some lines are empty or just comments.
      _UpperCAmelCase           =   [(pattern.search(SCREAMING_SNAKE_CASE_						).groups()[0] if pattern.search(SCREAMING_SNAKE_CASE_						) is not None else None) for b in internal_blocks]
      # We only sort the lines with a key.
      _UpperCAmelCase           =   [(i, key) for i, key in enumerate(SCREAMING_SNAKE_CASE_						) if key is not None]
      _UpperCAmelCase           =   [x[0] for x in sorted(SCREAMING_SNAKE_CASE_				,	key=lambda SCREAMING_SNAKE_CASE_						: x[1]						)]

      # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
      _UpperCAmelCase           =   0
      _UpperCAmelCase           =   []
      for i in range(len(SCREAMING_SNAKE_CASE_						)						):
         if keys[i] is None:
            reordered_blocks.append(internal_blocks[i]						)
         else:
            _UpperCAmelCase           =   sort_objects_in_import(internal_blocks[sorted_indices[count]]						)
            reordered_blocks.append(SCREAMING_SNAKE_CASE_						)
            count += 1

        # And we put our main block back together with its first and last line.
      _UpperCAmelCase           =   '''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]						)

   if code != "\n".join(SCREAMING_SNAKE_CASE_						):
      if check_only:
         return True
      else:
         print(F'''Overwriting {file}.'''						)
         with open(SCREAMING_SNAKE_CASE_				,	'''w'''						) as f:
            f.write('''\n'''.join(SCREAMING_SNAKE_CASE_						)						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[str]=True						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   []
   for root, _, files in os.walk(SCREAMING_SNAKE_CASE_						):
      if "__init__.py" in files:
         _UpperCAmelCase           =   sort_imports(os.path.join(SCREAMING_SNAKE_CASE_				,	'''__init__.py'''						)				,	check_only=SCREAMING_SNAKE_CASE_						)
         if result:
            _UpperCAmelCase           =   [os.path.join(SCREAMING_SNAKE_CASE_				,	'''__init__.py'''						)]
   if len(SCREAMING_SNAKE_CASE_						) > 0:
      raise ValueError(F'''Would overwrite {len(SCREAMING_SNAKE_CASE_						)} files, run `make style`.'''						)


if __name__ == "__main__":
   UpperCAmelCase_           =	argparse.ArgumentParser()
   parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
   UpperCAmelCase_           =	parser.parse_args()

   sort_imports_in_all_inits(check_only=args.check_only)






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


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  int							  =   ["""pixel_values"""]





     def __init__(     self				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = True				,							_UpperCamelCase = True				,							_UpperCamelCase = 1 / 255				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase					)
        _UpperCAmelCase           =   size if size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        _UpperCAmelCase           =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase				,							param_name='''crop_size'''					)

        _UpperCAmelCase           =   do_resize
        _UpperCAmelCase           =   do_rescale
        _UpperCAmelCase           =   do_normalize
        _UpperCAmelCase           =   do_center_crop
        _UpperCAmelCase           =   crop_size
        _UpperCAmelCase           =   size
        _UpperCAmelCase           =   resample
        _UpperCAmelCase           =   rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _UpperCAmelCase           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = PILImageResampling.BILINEAR				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        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(_UpperCamelCase				,							size=(size['''height'''], size['''width'''])				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase					):
        return rescale(_UpperCamelCase				,							scale=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        return normalize(_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = ChannelDimension.FIRST				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase           =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase           =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase           =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase           =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							param_name='''crop_size'''				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   resample if resample is not None else self.resample
        _UpperCAmelCase           =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase           =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase           =   size if size is not None else self.size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if not is_batched(_UpperCamelCase					):
           _UpperCAmelCase           =   [images]

        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.'''					)

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

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

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

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

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

        _UpperCAmelCase           =   [to_channel_dimension_format(_UpperCamelCase				,							_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   {'''pixel_values''': images}
        return BatchFeature(data=_UpperCamelCase				,							tensor_type=_UpperCamelCase					)