
import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
snake_case_      :					Optional[Any]				      =     "src/transformers"

# Matches is_xxx_available()
snake_case_      :					str				      =     re.compile(R'is\_([a-z_]*)_available()')
# Matches from xxx import bla
snake_case_      :					Union[str, Any]				      =     re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
snake_case_      :					Dict				      =     re.compile(R'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


snake_case_      :					Any				      =     "\n{0} = None\n"

snake_case_      :					Tuple				      =     "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


snake_case_      :					Optional[Any]				      =     "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



def      __UpperCAmelCase    (  snake_case_							:		str				):



	'''simple docstring'''



	if _re_test_backend.search(snake_case_				) is None:
		return None
	UpperCAmelCase:					List[str] 						=     [b[0] for b in _re_backend.findall(snake_case_				)]
	backends.sort()
	return "_and_".join(snake_case_				)



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	with open(os.path.join(snake_case_				,					"__init__.py"				)				,					"r"				,					encoding="utf-8"				,					newline="\n"				) as f:
		UpperCAmelCase:					Union[str, Any] 						=     f.readlines()

	# Get to the point we do the actual imports for type checking
	UpperCAmelCase:					int 						=     0
	while not lines[line_index].startswith("if TYPE_CHECKING"				):
		line_index += 1

	UpperCAmelCase:					Optional[Any] 						=     {}
	# Go through the end of the file
	while line_index < len(snake_case_				):
		# If the line is an if is_backend_available, we grab all objects associated.
		UpperCAmelCase:					Any 						=     find_backend(lines[line_index]				)
		if backend is not None:
			while not lines[line_index].startswith("    else:"				):
				line_index += 1
			line_index += 1

			UpperCAmelCase:					List[str] 						=     []
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]				) <= 1 or lines[line_index].startswith(" " * 8				):
				UpperCAmelCase:					str 						=     lines[line_index]
				UpperCAmelCase:					Union[str, Any] 						=     _re_single_line_import.search(snake_case_				)
				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:					Optional[int] 						=     objects
		else:
			line_index += 1

	return backend_specific_objects



def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		List[str]				):



	'''simple docstring'''



	if name.isupper():
		return DUMMY_CONSTANT.format(snake_case_				)
	elif name.islower():
		return DUMMY_FUNCTION.format(snake_case_				,					snake_case_				)
	else:
		return DUMMY_CLASS.format(snake_case_				,					snake_case_				)



def      __UpperCAmelCase    (  snake_case_							:		Dict=None				):



	'''simple docstring'''



	if backend_specific_objects is None:
		UpperCAmelCase:					Any 						=     read_init()
	# For special correspondence backend to module name as used in the function requires_modulename
	UpperCAmelCase:					Optional[Any] 						=     {}

	for backend, objects in backend_specific_objects.items():
		UpperCAmelCase:					int 						=     '''[''' + ''', '''.join(F'\"{b}\"' for b in backend.split("_and_"				)				) + ''']'''
		UpperCAmelCase:					Any 						=     '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
		dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
		dummy_file += "\n".join([create_dummy_object(snake_case_				,					snake_case_				) for o in objects]				)
		UpperCAmelCase:					List[str] 						=     dummy_file

	return dummy_files



def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]=False				):



	'''simple docstring'''



	UpperCAmelCase:					Dict 						=     create_dummy_files()
	# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
	UpperCAmelCase:					Tuple 						=     {'''torch''': '''pt'''}

	# Locate actual dummy modules and read their content.
	UpperCAmelCase:					Optional[Any] 						=     os.path.join(snake_case_				,					"utils"				)
	UpperCAmelCase:					int 						=     {
	    backend: os.path.join(snake_case_				,					F'dummy_{short_names.get(snake_case_				,					snake_case_				)}_objects.py'				)
	    for backend in dummy_files.keys()
	}

	UpperCAmelCase:					Optional[Any] 						=     {}
	for backend, file_path in dummy_file_paths.items():
		if os.path.isfile(snake_case_				):
			with open(snake_case_				,					"r"				,					encoding="utf-8"				,					newline="\n"				) as f:
				UpperCAmelCase:					List[Any] 						=     f.read()
		else:
			UpperCAmelCase:					List[Any] 						=     ''''''

	for backend in dummy_files.keys():
		if dummy_files[backend] != actual_dummies[backend]:
			if overwrite:
				print(
				    F'Updating transformers.utils.dummy_{short_names.get(snake_case_				,					snake_case_				)}_objects.py as the main '
				    "__init__ has new objects."				)
				with open(dummy_file_paths[backend]				,					"w"				,					encoding="utf-8"				,					newline="\n"				) as f:
					f.write(dummy_files[backend]				)
			else:
				raise ValueError(
				    "The main __init__ has objects that are not present in "
				    F'transformers.utils.dummy_{short_names.get(snake_case_				,					snake_case_				)}_objects.py. Run `make fix-copies` '
				    "to fix this."				)


if __name__ == "__main__":
	snake_case_      :					Optional[int]				      =     argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	snake_case_      :					str				      =     parser.parse_args()

	check_dummies(args.fix_and_overwrite)




from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__UpperCamelCase							:							Any    							=					logging.get_logger(__name__)  # pylint: disable=invalid-name

class    __magic_name__						(  __lowerCAmelCase):
     A:		Dict								=   ["safety_checker", "feature_extractor"]


     def __init__(   self				:  List[str]				,						lowerCamelCase__				:  AutoencoderKL				,						lowerCamelCase__				:  CLIPTextModel				,						lowerCamelCase__				:  CLIPTokenizer				,						lowerCamelCase__				:  UNetaDConditionModel				,						lowerCamelCase__				:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,						lowerCamelCase__				:  StableDiffusionSafetyChecker				,						lowerCamelCase__				:  CLIPImageProcessor				,						lowerCamelCase__				:  bool = True				,						)	->   Optional[Any]:





      '''simple docstring'''






      super().__init__()
      if hasattr(scheduler.config				,						'''steps_offset'''      ) and scheduler.config.steps_offset != 1:
       UpperCamelCase__       :     int 							= (
           F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
           F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
           '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
           ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
           ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
           ''' file'''
       )
       deprecate('''steps_offset!=1'''				,						'''1.0.0'''				,						lowerCamelCase__				,						standard_warn=lowerCamelCase__      )
       UpperCamelCase__       :     int 							= dict(scheduler.config      )
       UpperCamelCase__       :     List[str] 							= 1
       UpperCamelCase__       :     List[str] 							= FrozenDict(lowerCamelCase__      )

      self.register_modules(
          vae=lowerCamelCase__				,						text_encoder=lowerCamelCase__				,						tokenizer=lowerCamelCase__				,						unet=lowerCamelCase__				,						scheduler=lowerCamelCase__				,						safety_checker=lowerCamelCase__				,						feature_extractor=lowerCamelCase__				,						)
      self.register_to_config(requires_safety_checker=lowerCamelCase__      )


     @property
     def   UpperCAmelCase__						(   self				:  str      )	->   Dict[str, Any]:





      '''simple docstring'''






      return {k: getattr(self				,						lowerCamelCase__      ) for k in self.config.keys() if not k.startswith('''_'''      )}


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  Optional[Union[str, int]] = "auto"      )	->   str:





      '''simple docstring'''






      if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       UpperCamelCase__       :     Dict 							= self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  int      )	->   Tuple:





      '''simple docstring'''






      self.enable_attention_slicing(lowerCamelCase__      )


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  float = 0.8				,						lowerCamelCase__				:  Optional[int] = 50				,						lowerCamelCase__				:  Optional[float] = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  Optional[float] = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						)	->   Optional[Any]:





      '''simple docstring'''






      return StableDiffusionInpaintPipelineLegacy(**self.components      )(
          prompt=lowerCamelCase__				,						image=lowerCamelCase__				,						mask_image=lowerCamelCase__				,						strength=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						)


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  float = 0.8				,						lowerCamelCase__				:  Optional[int] = 50				,						lowerCamelCase__				:  Optional[float] = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  Optional[float] = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						**lowerCamelCase__				:  str				,						)	->   str:





      '''simple docstring'''






      return StableDiffusionImgaImgPipeline(**self.components      )(
          prompt=lowerCamelCase__				,						image=lowerCamelCase__				,						strength=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						callback_steps=lowerCamelCase__				,						)


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  int = 512				,						lowerCamelCase__				:  int = 512				,						lowerCamelCase__				:  int = 50				,						lowerCamelCase__				:  float = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  float = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[torch.FloatTensor] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						)	->   Optional[Any]:





      '''simple docstring'''






      return StableDiffusionPipeline(**self.components      )(
          prompt=lowerCamelCase__				,						height=lowerCamelCase__				,						width=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						latents=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						callback_steps=lowerCamelCase__				,						)


