
import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


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


# This is to make sure the diffusers module imported is the one in the repo.
_lowerCamelCase					   =importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
_lowerCamelCase					   =spec.loader.load_module()





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 return line.startswith(lowerCamelCase							) or len(lowerCamelCase							) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$""",	lowerCamelCase							) is not None





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	List[Any]							=      object_name.split("""."""							)
 lowerCamelCase		:	Optional[Any]							=      0

 # First let's find the module where our object lives.
 lowerCamelCase		:	Tuple							=      parts[i]
 while i < len(lowerCamelCase							) and not os.path.isfile(os.path.join(lowerCamelCase,	F'''{module}.py'''							)							):
  i += 1
  if i < len(lowerCamelCase							):
   lowerCamelCase		:	str							=      os.path.join(lowerCamelCase,	parts[i]							)
 if i >= len(lowerCamelCase							):
  raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.'''							)

 with open(os.path.join(lowerCamelCase,	F'''{module}.py'''							),	"""r""",	encoding="""utf-8""",	newline="""\n"""							) as f:
  lowerCamelCase		:	str							=      f.readlines()

 # Now let's find the class / func in the code!
 lowerCamelCase		:	Dict							=      """"""
 lowerCamelCase		:	Optional[Any]							=      0
 for name in parts[i + 1 :]:
  while (
      line_index < len(lowerCamelCase							) and re.search(RF'''^{indent}(class|def)\s+{name}(\(|\:)''',	lines[line_index]							) is None
  ):
   line_index += 1
  indent += "    "
  line_index += 1

 if line_index >= len(lowerCamelCase							):
  raise ValueError(F''' {object_name} does not match any function or class in {module}.'''							)

 # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
 lowerCamelCase		:	Dict							=      line_index
 while line_index < len(lowerCamelCase							) and _should_continue(lines[line_index],	lowerCamelCase							):
  line_index += 1
 # Clean up empty lines at the end (if any).
 while len(lines[line_index - 1]							) <= 1:
  line_index -= 1

 lowerCamelCase		:	Union[str, Any]							=      lines[start_index:line_index]
 return "".join(lowerCamelCase							)


_lowerCamelCase					   =re.compile(R"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
_lowerCamelCase					   =re.compile(R"""^\s*(\S+)->(\S+)(\s+.*|$)""")
_lowerCamelCase					   =re.compile(R"""<FILL\s+[^>]*>""")





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	int							=      code.split("""\n"""							)
 lowerCamelCase		:	Any							=      0
 while idx < len(lowerCamelCase							) and len(lines[idx]							) == 0:
  idx += 1
 if idx < len(lowerCamelCase							):
  return re.search(R"""^(\s*)\S""",	lines[idx]							).groups()[0]
 return ""





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	Any							=      len(get_indent(lowerCamelCase							)							) > 0
 if has_indent:
  lowerCamelCase		:	Any							=      F'''class Bla:\n{code}'''
 lowerCamelCase		:	Any							=      black.Mode(target_versions={black.TargetVersion.PYaa},	line_length=119,	preview=lowerCamelCase							)
 lowerCamelCase		:	Dict							=      black.format_str(lowerCamelCase,	mode=lowerCamelCase							)
 lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      style_docstrings_in_code(lowerCamelCase							)
 return result[len("""class Bla:\n"""							) :] if has_indent else result





def     _a					(						lowerCamelCase,	lowerCamelCase=False							):
 with open(lowerCamelCase,	"""r""",	encoding="""utf-8""",	newline="""\n"""							) as f:
  lowerCamelCase		:	List[Any]							=      f.readlines()
 lowerCamelCase		:	str							=      []
 lowerCamelCase		:	int							=      0
 # Not a for loop cause `lines` is going to change (if `overwrite=True`).
 while line_index < len(lowerCamelCase							):
  lowerCamelCase		:	str							=      _re_copy_warning.search(lines[line_index]							)
  if search is None:
   line_index += 1
   continue

  # There is some copied code here, let's retrieve the original.
  lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      search.groups()
  lowerCamelCase		:	Optional[int]							=      find_code_in_diffusers(lowerCamelCase							)
  lowerCamelCase		:	Optional[int]							=      get_indent(lowerCamelCase							)

  lowerCamelCase		:	Optional[Any]							=      line_index + 1 if indent == theoretical_indent else line_index + 2
  lowerCamelCase		:	Optional[Any]							=      theoretical_indent
  lowerCamelCase		:	Dict							=      start_index

  # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
  lowerCamelCase		:	Tuple							=      True
  while line_index < len(lowerCamelCase							) and should_continue:
   line_index += 1
   if line_index >= len(lowerCamelCase							):
    break
   lowerCamelCase		:	Union[str, Any]							=      lines[line_index]
   lowerCamelCase		:	str							=      _should_continue(lowerCamelCase,	lowerCamelCase							) and re.search(F'''^{indent}# End copy''',	lowerCamelCase							) is None
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]							) <= 1:
   line_index -= 1

  lowerCamelCase		:	Union[str, Any]							=      lines[start_index:line_index]
  lowerCamelCase		:	Tuple							=      """""".join(lowerCamelCase							)

  # Remove any nested `Copied from` comments to avoid circular copies
  lowerCamelCase		:	Any							=      [line for line in theoretical_code.split("""\n"""							) if _re_copy_warning.search(lowerCamelCase							) is None]
  lowerCamelCase		:	Optional[Any]							=      """\n""".join(lowerCamelCase							)

  # Before comparing, use the `replace_pattern` on the original code.
  if len(lowerCamelCase							) > 0:
   lowerCamelCase		:	Optional[int]							=      replace_pattern.replace("""with""",	""""""							).split(""","""							)
   lowerCamelCase		:	List[str]							=      [_re_replace_pattern.search(lowerCamelCase							) for p in patterns]
   for pattern in patterns:
    if pattern is None:
     continue
    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Any							=      pattern.groups()
    lowerCamelCase		:	Optional[Any]							=      re.sub(lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)
    if option.strip() == "all-casing":
     lowerCamelCase		:	Optional[Any]							=      re.sub(obja.lower(),	obja.lower(),	lowerCamelCase							)
     lowerCamelCase		:	Dict							=      re.sub(obja.upper(),	obja.upper(),	lowerCamelCase							)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
   lowerCamelCase		:	Any							=      blackify(lines[start_index - 1] + theoretical_code							)
   lowerCamelCase		:	Optional[int]							=      theoretical_code[len(lines[start_index - 1]							) :]

  # Test for a diff and act accordingly.
  if observed_code != theoretical_code:
   diffs.append([object_name, start_index]							)
   if overwrite:
    lowerCamelCase		:	Optional[Any]							=      lines[:start_index] + [theoretical_code] + lines[line_index:]
    lowerCamelCase		:	Tuple							=      start_index + 1

 if overwrite and len(lowerCamelCase							) > 0:
  # Warn the user a file has been modified.
  print(F'''Detected changes, rewriting {filename}.'''							)
  with open(lowerCamelCase,	"""w""",	encoding="""utf-8""",	newline="""\n"""							) as f:
   f.writelines(lowerCamelCase							)
 return diffs





def     _a					(						lowerCamelCase = False							):
 lowerCamelCase		:	Dict							=      glob.glob(os.path.join(lowerCamelCase,	"""**/*.py"""							),	recursive=lowerCamelCase							)
 lowerCamelCase		:	Tuple							=      []
 for filename in all_files:
  lowerCamelCase		:	List[Any]							=      is_copy_consistent(lowerCamelCase,	lowerCamelCase							)
  diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
 if not overwrite and len(lowerCamelCase							) > 0:
  lowerCamelCase		:	Dict							=      """\n""".join(lowerCamelCase							)
  raise Exception(
      """Found the following copy inconsistencies:\n"""
      + diff
      + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""							)


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

      check_copies(args.fix_and_overwrite)






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_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


_lowerCamelCase					   =logging.get_logger(__name__)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			["""pixel_values"""]
      def __init__(      self    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = PILImageResampling.BILINEAR    ,  __magic_name__ = True    ,  __magic_name__ = 1 / 2_5_5    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      size if size is not None else {"""shortest_edge""": 3_8_4}
       lowerCamelCase		:	Tuple							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	Dict							=      do_resize
       lowerCamelCase		:	List[Any]							=      size
       # Default value set here for backwards compatibility where the value in config is None
       lowerCamelCase		:	Any							=      crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       lowerCamelCase		:	Union[str, Any]							=      resample
       lowerCamelCase		:	str							=      do_rescale
       lowerCamelCase		:	Union[str, Any]							=      rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase		:	int							=      image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = PILImageResampling.BICUBIC    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	Union[str, Any]							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )
       if "shortest_edge" not in size:
        raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''      )
       lowerCamelCase		:	str							=      size["""shortest_edge"""]

       if shortest_edge < 3_8_4:
        # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
        lowerCamelCase		:	List[str]							=      int(shortest_edge / crop_pct      )
        lowerCamelCase		:	Optional[Any]							=      get_resize_output_image_size(__magic_name__    ,  size=__magic_name__    ,  default_to_square=__magic_name__      )
        lowerCamelCase		:	Optional[int]							=      resize(image=__magic_name__    ,  size=__magic_name__    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
        # then crop to (shortest_edge, shortest_edge)
        return center_crop(image=__magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  data_format=__magic_name__    ,  **__magic_name__      )
       else:
        # warping (no cropping) when evaluated at 384 or larger
        return resize(
            __magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return rescale(__magic_name__    ,  scale=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return normalize(__magic_name__    ,  mean=__magic_name__    ,  std=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = ChannelDimension.FIRST    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	str							=      do_resize if do_resize is not None else self.do_resize
       lowerCamelCase		:	Optional[Any]							=      crop_pct if crop_pct is not None else self.crop_pct
       lowerCamelCase		:	Optional[int]							=      resample if resample is not None else self.resample
       lowerCamelCase		:	Optional[int]							=      do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase		:	List[str]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else self.image_mean
       lowerCamelCase		:	Tuple							=      image_std if image_std is not None else self.image_std

       lowerCamelCase		:	Dict							=      size if size is not None else self.size
       lowerCamelCase		:	str							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	List[str]							=      make_list_of_images(__magic_name__      )

       if not valid_images(__magic_name__      ):
        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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
        raise ValueError("""crop_pct must be specified if size < 384."""      )

       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.
       lowerCamelCase		:	Optional[Any]							=      [to_numpy_array(__magic_name__      ) for image in images]

       if do_resize:
        lowerCamelCase		:	List[Any]							=      [self.resize(image=__magic_name__    ,  size=__magic_name__    ,  crop_pct=__magic_name__    ,  resample=__magic_name__      ) for image in images]

       if do_rescale:
        lowerCamelCase		:	Union[str, Any]							=      [self.rescale(image=__magic_name__    ,  scale=__magic_name__      ) for image in images]

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

       lowerCamelCase		:	Optional[int]							=      [to_channel_dimension_format(__magic_name__    ,  __magic_name__      ) for image in images]

       lowerCamelCase		:	List[str]							=      {"""pixel_values""": images}
       return BatchFeature(data=__magic_name__    ,  tensor_type=__magic_name__      )




