

'''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
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     (    A__     ,				A__      ):






 """simple docstring"""



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


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = object_name.split('''.'''      )
 __lowercase							  = 0

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

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

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

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


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     (    A__      ):






 """simple docstring"""



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


def 		_A     (    A__      ):






 """simple docstring"""



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


def 		_A     (    A__     ,				A__=False      ):






 """simple docstring"""



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

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

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

  __lowercase							  = lines[start_index:line_index]
  __lowercase							  = ''''''.join(A__      )

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

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

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

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


def 		_A     (    A__ = False      ):






 """simple docstring"""



 __lowercase							  = glob.glob(os.path.join(A__     ,				'''**/*.py'''      )     ,				recursive=A__      )
 __lowercase							  = []
 for filename in all_files:
  __lowercase							  = is_copy_consistent(A__     ,				A__      )
  diffs += [F"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs]
 if not overwrite and len(A__      ) > 0:
  __lowercase							  = '''\n'''.join(A__      )
  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)






'''simple docstring'''

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

__lowerCAmelCase									=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[Any]:
		for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
				if class_name in extractors:
						_a     :      List[Any]        =							model_type_to_module_name(lowerCAmelCase_  )

						_a     :      Optional[Any]        =							importlib.import_module(f""".{module_name}"""						,			'transformers.models'  )
						try:
								return getattr(lowerCAmelCase_						,			lowerCAmelCase_  )
						except AttributeError:
								continue

		for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
				if getattr(lowerCAmelCase_						,			'__name__'						,			lowerCAmelCase_  ) == class_name:
						return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
		_a     :      Dict        =							importlib.import_module('transformers'  )
		if hasattr(lowerCAmelCase_						,			lowerCAmelCase_  ):
				return getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

		return None



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = False						,			lowerCAmelCase_ = False						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = False						,			**lowerCAmelCase_						,			)			->      Tuple:
		_a     :      List[str]        =							get_file_from_repo(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			cache_dir=lowerCAmelCase_						,			force_download=lowerCAmelCase_						,			resume_download=lowerCAmelCase_						,			proxies=lowerCAmelCase_						,			use_auth_token=lowerCAmelCase_						,			revision=lowerCAmelCase_						,			local_files_only=lowerCAmelCase_						,			)
		if resolved_config_file is None:
				logger.info(
				    'Could not locate the image processor configuration file, will try to use the model config instead.'  )
				return {}

		with open(lowerCAmelCase_						,			encoding='utf-8'  ) as reader:
				return json.load(lowerCAmelCase_  )

class 	__magic_name__						:







							def __init__(							self					:       List[str]		):
									raise EnvironmentError(
									    'AutoImageProcessor is designed to be instantiated '
									    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'		)







							@classmethod
							@replace_list_option_in_docstrings(_UpperCAmelCase		)
							def 							__lowercase			(							cls					:       Dict	,_UpperCAmelCase					:       Union[str, Any]	,**_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Any        =							kwargs.pop('config'	,_UpperCAmelCase		)
									_a     :      Dict        =							kwargs.pop('trust_remote_code'	,_UpperCAmelCase		)
									_a     :      Any        =							True

									_a       ,					_a     :      Tuple        =							ImageProcessingMixin.get_image_processor_dict(_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      List[Any]        =							config_dict.get('image_processor_type'	,_UpperCAmelCase		)
									_a     :      int        =							None
									if "AutoImageProcessor" in config_dict.get('auto_map'	,{}		):
											_a     :      Any        =							config_dict['auto_map']['AutoImageProcessor']

									# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
									# and if so, infer the image processor class from there.
									if image_processor_class is None and image_processor_auto_map is None:
											_a     :      List[Any]        =							config_dict.pop('feature_extractor_type'	,_UpperCAmelCase		)
											if feature_extractor_class is not None:
													logger.warning(
													    'Could not find image processor class in the image processor config or the model config. Loading'
													    ' based on pattern matching with the model\'s feature extractor configuration.'		)
													_a     :      Optional[int]        =							feature_extractor_class.replace('FeatureExtractor'	,'ImageProcessor'		)
											if "AutoFeatureExtractor" in config_dict.get('auto_map'	,{}		):
													_a     :      List[Any]        =							config_dict['auto_map']['AutoFeatureExtractor']
													_a     :      List[str]        =							feature_extractor_auto_map.replace('FeatureExtractor'	,'ImageProcessor'		)
													logger.warning(
													    'Could not find image processor auto map in the image processor config or the model config.'
													    ' Loading based on pattern matching with the model\'s feature extractor configuration.'		)

        # If we don't find the image processor class in the image processor config, let's try the model config.
									if image_processor_class is None and image_processor_auto_map is None:
											if not isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													_a     :      Dict        =							AutoConfig.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
											# It could be in `config.image_processor_type``
											_a     :      Optional[int]        =							getattr(_UpperCAmelCase	,'image_processor_type'	,_UpperCAmelCase		)
											if hasattr(_UpperCAmelCase	,'auto_map'		) and "AutoImageProcessor" in config.auto_map:
													_a     :      Union[str, Any]        =							config.auto_map['AutoImageProcessor']

									if image_processor_class is not None:
											_a     :      Optional[int]        =							image_processor_class_from_name(_UpperCAmelCase		)

									_a     :      List[str]        =							image_processor_auto_map is not None
									_a     :      Optional[int]        =							image_processor_class is not None or type(_UpperCAmelCase		) in IMAGE_PROCESSOR_MAPPING
									_a     :      Optional[int]        =							resolve_trust_remote_code(
									    _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)

									if has_remote_code and trust_remote_code:
											_a     :      Dict        =							get_class_from_dynamic_module(
											    _UpperCAmelCase	,_UpperCAmelCase	,**_UpperCAmelCase		)
											_a     :      int        =							kwargs.pop('code_revision'	,_UpperCAmelCase		)
											if os.path.isdir(_UpperCAmelCase		):
													image_processor_class.register_for_auto_class()
											return image_processor_class.from_dict(_UpperCAmelCase	,**_UpperCAmelCase		)
									elif image_processor_class is not None:
											return image_processor_class.from_dict(_UpperCAmelCase	,**_UpperCAmelCase		)
									# Last try: we use the IMAGE_PROCESSOR_MAPPING.
									elif type(_UpperCAmelCase		) in IMAGE_PROCESSOR_MAPPING:
											_a     :      Dict        =							IMAGE_PROCESSOR_MAPPING[type(_UpperCAmelCase		)]
											return image_processor_class.from_dict(_UpperCAmelCase	,**_UpperCAmelCase		)

									raise ValueError(
									    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
									    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
									    F"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()		)}"""		)







							@staticmethod
							def 							__lowercase			(							_UpperCAmelCase					:       Optional[Any]	,_UpperCAmelCase					:       Dict		):
									IMAGE_PROCESSOR_MAPPING.register(_UpperCAmelCase	,_UpperCAmelCase		)




