




"""simple docstring"""



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
SCREAMING_SNAKE_CASE    =	"src/diffusers"
SCREAMING_SNAKE_CASE    =	"."


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







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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__	=    object_name.split("."						)
 A__	=    0

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

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

 # Now let's find the class / func in the code!
 A__	=    ""
 A__	=    0
 for name in parts[i + 1 :]:
  while (
      line_index < len(lowercase_						) 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(lowercase_						):
  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).
 A__	=    line_index
 while line_index < len(lowercase_						) and _should_continue(lines[line_index]      ,			lowercase_						):
  line_index += 1
 # Clean up empty lines at the end (if any).
 while len(lines[line_index - 1]						) <= 1:
  line_index -= 1

 A__	=    lines[start_index:line_index]
 return "".join(lowercase_						)


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







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Tuple:
 A__	=    code.split("\n"						)
 A__	=    0
 while idx < len(lowercase_						) and len(lines[idx]						) == 0:
  idx += 1
 if idx < len(lowercase_						):
  return re.search(R"^(\s*)\S"      ,			lines[idx]						).groups()[0]
 return ""







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		List[Any]:
 A__	=    len(get_indent(lowercase_						)						) > 0
 if has_indent:
  A__	=    f"""class Bla:\n{code}"""
 A__	=    black.Mode(target_versions={black.TargetVersion.PYaa}      ,			line_length=1_19      ,			preview=lowercase_						)
 A__	=    black.format_str(lowercase_      ,			mode=lowercase_						)
 A__,				A__	=    style_docstrings_in_code(lowercase_						)
 return result[len("class Bla:\n"						) :] if has_indent else result







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=False						)						->		int:
 with open(lowercase_      ,			"r"      ,			encoding="utf-8"      ,			newline="\n"						) as f:
  A__	=    f.readlines()
 A__	=    []
 A__	=    0
 # Not a for loop cause `lines` is going to change (if `overwrite=True`).
 while line_index < len(lowercase_						):
  A__	=    _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.
  A__,				A__,				A__	=    search.groups()
  A__	=    find_code_in_diffusers(lowercase_						)
  A__	=    get_indent(lowercase_						)

  A__	=    line_index + 1 if indent == theoretical_indent else line_index + 2
  A__	=    theoretical_indent
  A__	=    start_index

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

  A__	=    lines[start_index:line_index]
  A__	=    "".join(lowercase_						)

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

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

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

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_ = False						)						->		Any:
 A__	=    glob.glob(os.path.join(lowercase_      ,			"**/*.py"						)      ,			recursive=lowercase_						)
 A__	=    []
 for filename in all_files:
  A__	=    is_copy_consistent(lowercase_      ,			lowercase_						)
  diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
 if not overwrite and len(lowercase_						) > 0:
  A__	=    "\n".join(lowercase_						)
  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__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      SCREAMING_SNAKE_CASE    =	parser.parse_args()

      check_copies(args.fix_and_overwrite)











"""simple docstring"""



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''ViTImageProcessor'''
    lowercase__						 =				('''CLIPTokenizer''', '''CLIPTokenizerFast''')




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Dict=None     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."     ,			snake_case_     ,			)
      A__	=    kwargs.pop("feature_extractor"				)

     A__	=    image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(snake_case_     ,			snake_case_				)




    def __call__(   self						:	int     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Dict=None     ,			snake_case_						:	int=None     ,			**snake_case_						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     if text is None and visual_prompt is None and images is None:
      raise ValueError("You have to specify either text, visual prompt or images."				)

     if text is not None and visual_prompt is not None:
      raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None and images is not None:
      A__	=    {
          "pixel_values": image_features.pixel_values,
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     elif text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     elif visual_prompt is not None:
      A__	=    {
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							str:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			*snake_case_						:	Any     ,			**snake_case_						:	List[Any]				)				->							List[str]:




     '''simple docstring'''

     return self.tokenizer.decode(*snake_case_     ,			**snake_case_				)




    @property
    def 							__magic_name__							(   self						:	int				)				->							Optional[int]:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,			snake_case_     ,			)
     return self.image_processor_class




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,			snake_case_     ,			)
     return self.image_processor





