
'''simple docstring'''

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
UpperCAmelCase__				:	Any					=						"src/transformers"

# Matches is_xxx_available()
UpperCAmelCase__				:	Optional[Any]					=						re.compile(R"is\_([a-z_]*)_available()")
# Matches from xxx import bla
UpperCAmelCase__				:	Tuple					=						re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
UpperCAmelCase__				:	Tuple					=						re.compile(R"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


UpperCAmelCase__				:	Optional[int]					=						"\n{0} = None\n"

UpperCAmelCase__				:	Optional[Any]					=						"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


UpperCAmelCase__				:	Any					=						"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







def 					A       ( UpperCamelCase_   :       List[Any] )   ->    int:







			'''simple docstring'''



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







def 					A       ( )   ->    Any:







			'''simple docstring'''



			with open(os.path.join(UpperCamelCase_  ,		"__init__.py" )  ,		"r"  ,		encoding="utf-8"  ,		newline="\n" ) as f:
						lowerCAmelCase__									=							f.readlines()

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

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

									lowerCAmelCase__									=							[]
									# Until we unindent, add backend objects to the list
									while len(lines[line_index] ) <= 1 or lines[line_index].startswith(" " * 8 ):
												lowerCAmelCase__									=							lines[line_index]
												lowerCAmelCase__									=							_re_single_line_import.search(UpperCamelCase_ )
												if single_line_import_search is not None:
															objects.extend(single_line_import_search.groups()[0].split(", " ) )
												elif line.startswith(" " * 12 ):
															objects.append(line[12:-2] )
												line_index += 1

									lowerCAmelCase__									=							objects
						else:
									line_index += 1

			return backend_specific_objects







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       int )   ->    Optional[int]:







			'''simple docstring'''



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







def 					A       ( UpperCamelCase_   :       int=None )   ->    List[str]:







			'''simple docstring'''



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

			for backend, objects in backend_specific_objects.items():
						lowerCAmelCase__									=							"[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_" ) ) + "]"
						lowerCAmelCase__									=							"# 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(UpperCamelCase_  ,		UpperCamelCase_ ) for o in objects] )
						lowerCAmelCase__									=							dummy_file

			return dummy_files







def 					A       ( UpperCamelCase_   :       Optional[Any]=False )   ->    Union[str, Any]:







			'''simple docstring'''



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

			# Locate actual dummy modules and read their content.
			lowerCAmelCase__									=							os.path.join(UpperCamelCase_  ,		"utils" )
			lowerCAmelCase__									=							{
			    backend: os.path.join(UpperCamelCase_  ,		F"""dummy_{short_names.get(UpperCamelCase_  ,		UpperCamelCase_ )}_objects.py""" )
			    for backend in dummy_files.keys()
			}

			lowerCAmelCase__									=							{}
			for backend, file_path in dummy_file_paths.items():
						if os.path.isfile(UpperCamelCase_ ):
									with open(UpperCamelCase_  ,		"r"  ,		encoding="utf-8"  ,		newline="\n" ) as f:
												lowerCAmelCase__									=							f.read()
						else:
									lowerCAmelCase__									=							""

			for backend in dummy_files.keys():
						if dummy_files[backend] != actual_dummies[backend]:
									if overwrite:
												print(
												    F"""Updating transformers.utils.dummy_{short_names.get(UpperCamelCase_  ,		UpperCamelCase_ )}_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(UpperCamelCase_  ,		UpperCamelCase_ )}_objects.py. Run `make fix-copies` """
												    "to fix this." )


if __name__ == "__main__":
							UpperCAmelCase__				:	Union[str, Any]					=						argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							UpperCAmelCase__				:	str					=						parser.parse_args()

							check_dummies(args.fix_and_overwrite)







'''simple docstring'''

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__				:	Tuple					=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Dict				       =       ['safety_checker', 'feature_extractor']





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


				"""simple docstring"""

				super().__init__()
				if hasattr(scheduler.config ,							"steps_offset"							) and scheduler.config.steps_offset != 1:
							lowerCAmelCase__									=							(
							    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" ,							__magic_name__ ,							standard_warn=__magic_name__							)
							lowerCAmelCase__									=							dict(scheduler.config							)
							lowerCAmelCase__									=							1
							lowerCAmelCase__									=							FrozenDict(__magic_name__							)

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





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Optional[Union[str, int]] = "auto"							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				self.enable_attention_slicing(__magic_name__							)





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


				"""simple docstring"""

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





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


				"""simple docstring"""

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







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


				"""simple docstring"""

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





