


'''simple docstring'''

import argparse
import os
import re


lowerCAmelCase				:Union[str, Any]        =      '''src/diffusers'''

# Pattern that looks at the indentation in a line.
lowerCAmelCase				:Tuple        =      re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCAmelCase				:Tuple        =      re.compile(r'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCAmelCase				:List[str]        =      re.compile(r'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCAmelCase				:List[str]        =      re.compile(r'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCAmelCase				:int        =      re.compile(r'''\[([^\]]+)\]''')





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					_re_indent.search(a__		)
	return "" if search is None else search.groups()[0]





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     List[str]=""		,		lowerCAmelCase    :     str=None		,		lowerCAmelCase    :     Any=None		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					0
	__magic_name__	:   str					     =					code.split('\n'		)
	if start_prompt is not None:
		while not lines[index].startswith(a__		):
			index += 1
		__magic_name__	:   Tuple					     =					['''\n'''.join(lines[:index]		)]
	else:
		__magic_name__	:   Optional[Any]					     =					[]

	# We split into blocks until we get to the `end_prompt` (or the end of the block).
	__magic_name__	:   str					     =					[lines[index]]
	index += 1
	while index < len(a__		) and (end_prompt is None or not lines[index].startswith(a__		)):
		if len(lines[index]		) > 0 and get_indent(lines[index]		) == indent_level:
			if len(a__		) > 0 and get_indent(current_block[-1]		).startswith(indent_level + ' '		):
				current_block.append(lines[index]		)
				blocks.append('\n'.join(a__		)		)
				if index < len(a__		) - 1:
					__magic_name__	:   Tuple					     =					[lines[index + 1]]
					index += 1
				else:
					__magic_name__	:   int					     =					[]
			else:
				blocks.append('\n'.join(a__		)		)
				__magic_name__	:   Dict					     =					[lines[index]]
		else:
			current_block.append(lines[index]		)
		index += 1

	# Adds current block if it's nonempty.
	if len(a__		) > 0:
		blocks.append('\n'.join(a__		)		)

	# Add final block after end_prompt if provided.
	if end_prompt is not None and index < len(a__		):
		blocks.append('\n'.join(lines[index:]		)		)

	return blocks





def lowerCamelCase      (      lowerCAmelCase    :     Any		):





	"""simple docstring"""







	def _inner(lowerCAmelCase    :     str		):
		return key(a__		).lower().replace('_'		,		''		)

	return _inner





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     List[Any]=None		):





	"""simple docstring"""







	def noop(lowerCAmelCase    :     str		):
		return x

	if key is None:
		__magic_name__	:   Any					     =					noop
	# Constants are all uppercase, they go first.
	__magic_name__	:   Any					     =					[obj for obj in objects if key(a__		).isupper()]
	# Classes are not all uppercase but start with a capital, they go second.
	__magic_name__	:   Optional[Any]					     =					[obj for obj in objects if key(a__		)[0].isupper() and not key(a__		).isupper()]
	# Functions begin with a lowercase, they go last.
	__magic_name__	:   Optional[int]					     =					[obj for obj in objects if not key(a__		)[0].isupper()]

	__magic_name__	:   Tuple					     =					ignore_underscore(a__		)
	return sorted(a__		,		key=a__		) + sorted(a__		,		key=a__		) + sorted(a__		,		key=a__		)





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	def _replace(lowerCAmelCase    :     Optional[Any]		):
		__magic_name__	:   List[Any]					     =					match.groups()[0]
		if "," not in imports:
			return f'[{imports}]'
		__magic_name__	:   Optional[int]					     =					[part.strip().replace('"'		,		''		) for part in imports.split(','		)]
		# We will have a final empty element if the line finished with a comma.
		if len(keys[-1]		) == 0:
			__magic_name__	:   Optional[Any]					     =					keys[:-1]
		return "[" + ", ".join([f'\"{k}\"' for k in sort_objects(a__		)]		) + "]"

	__magic_name__	:   Optional[Any]					     =					import_statement.split('\n'		)
	if len(a__		) > 3:
		# Here we have to sort internal imports that are on several lines (one per name):
		# key: [
		#     "object1",
		#     "object2",
		#     ...
		# ]

		# We may have to ignore one or two lines on each side.
		__magic_name__	:   Any					     =					2 if lines[1].strip() == '''[''' else 1
		__magic_name__	:   List[Any]					     =					[(i, _re_strip_line.search(a__		).groups()[0]) for i, line in enumerate(lines[idx:-idx]		)]
		__magic_name__	:   List[Any]					     =					sort_objects(a__		,		key=lambda lowerCAmelCase		: x[1]		)
		__magic_name__	:   Optional[Any]					     =					[lines[x[0] + idx] for x in sorted_indices]
		return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]		)
	elif len(a__		) == 3:
		# Here we have to sort internal imports that are on one separate line:
		# key: [
		#     "object1", "object2", ...
		# ]
		if _re_bracket_content.search(lines[1]		) is not None:
			__magic_name__	:   Tuple					     =					_re_bracket_content.sub(_replace		,		lines[1]		)
		else:
			__magic_name__	:   Union[str, Any]					     =					[part.strip().replace('"'		,		''		) for part in lines[1].split(','		)]
			# We will have a final empty element if the line finished with a comma.
			if len(keys[-1]		) == 0:
				__magic_name__	:   Optional[int]					     =					keys[:-1]
			__magic_name__	:   Union[str, Any]					     =					get_indent(lines[1]		) + ''', '''.join([f'\"{k}\"' for k in sort_objects(a__		)]		)
		return "\n".join(a__		)
	else:
		# Finally we have to deal with imports fitting on one line
		__magic_name__	:   Optional[int]					     =					_re_bracket_content.sub(_replace		,		a__		)
		return import_statement





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Tuple=True		):





	"""simple docstring"""







	with open(a__		,		'r'		) as f:
		__magic_name__	:   Optional[Any]					     =					f.read()

	if "_import_structure" not in code:
		return

	# Blocks of indent level 0
	__magic_name__	:   List[Any]					     =					split_code_in_indented_blocks(
	    a__		,		start_prompt='_import_structure = {'		,		end_prompt='if TYPE_CHECKING:'		)

	# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
	for block_idx in range(1		,		len(a__		) - 1		):
		# Check if the block contains some `_import_structure`s thingy to sort.
		__magic_name__	:   Tuple					     =					main_blocks[block_idx]
		__magic_name__	:   int					     =					block.split('\n'		)

		# Get to the start of the imports.
		__magic_name__	:   Optional[Any]					     =					0
		while line_idx < len(a__		) and "_import_structure" not in block_lines[line_idx]:
			# Skip dummy import blocks
			if "import dummy" in block_lines[line_idx]:
				__magic_name__	:   int					     =					len(a__		)
			else:
				line_idx += 1
		if line_idx >= len(a__		):
			continue

		# Ignore beginning and last line: they don't contain anything.
		__magic_name__	:   Optional[Any]					     =					'''\n'''.join(block_lines[line_idx:-1]		)
		__magic_name__	:   Any					     =					get_indent(block_lines[1]		)
		# Slit the internal block into blocks of indent level 1.
		__magic_name__	:   Tuple					     =					split_code_in_indented_blocks(a__		,		indent_level=a__		)
		# We have two categories of import key: list or _import_structure[key].append/extend
		__magic_name__	:   Union[str, Any]					     =					_re_direct_key if '''_import_structure''' in block_lines[0] else _re_indirect_key
		# Grab the keys, but there is a trap: some lines are empty or just comments.
		__magic_name__	:   Dict					     =					[(pattern.search(a__		).groups()[0] if pattern.search(a__		) is not None else None) for b in internal_blocks]
		# We only sort the lines with a key.
		__magic_name__	:   Optional[int]					     =					[(i, key) for i, key in enumerate(a__		) if key is not None]
		__magic_name__	:   Tuple					     =					[x[0] for x in sorted(a__		,		key=lambda lowerCAmelCase		: x[1]		)]

		# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
		__magic_name__	:   Optional[Any]					     =					0
		__magic_name__	:   Tuple					     =					[]
		for i in range(len(a__		)		):
			if keys[i] is None:
				reordered_blocks.append(internal_blocks[i]		)
			else:
				__magic_name__	:   List[Any]					     =					sort_objects_in_import(internal_blocks[sorted_indices[count]]		)
				reordered_blocks.append(a__		)
				count += 1

        # And we put our main block back together with its first and last line.
		__magic_name__	:   Any					     =					'''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]		)

	if code != "\n".join(a__		):
		if check_only:
			return True
		else:
			print(f'Overwriting {file}.'		)
			with open(a__		,		'w'		) as f:
				f.write('\n'.join(a__		)		)





def lowerCamelCase      (      lowerCAmelCase    :     Any=True		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					[]
	for root, _, files in os.walk(a__		):
		if "__init__.py" in files:
			__magic_name__	:   Union[str, Any]					     =					sort_imports(os.path.join(a__		,		'__init__.py'		)		,		check_only=a__		)
			if result:
				__magic_name__	:   Union[str, Any]					     =					[os.path.join(a__		,		'__init__.py'		)]
	if len(a__		) > 0:
		raise ValueError(f'Would overwrite {len(a__		)} files, run `make style`.'		)


if __name__ == "__main__":
							lowerCAmelCase				:Union[str, Any]        =      argparse.ArgumentParser()
							parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
							lowerCAmelCase				:Union[str, Any]        =      parser.parse_args()

							sort_imports_in_all_inits(check_only=args.check_only)



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase				:Optional[Any]        =      transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		torch.Tensor		):
		return image
	elif isinstance(lowerCAmelCase		,		PIL.Image.Image		):
		__magic_name__	:   List[Any]					     =					[image]

	__magic_name__	:   List[Any]					     =					[trans(img.convert('RGB'		)		) for img in image]
	__magic_name__	:   Dict					     =					torch.stack(lowerCAmelCase		)
	return image





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Optional[Any]  ,					_A		:						str  ,					_A		:						int      )  ->			Dict:
			super().__init__()

			# make sure scheduler can always be converted to DDIM
			__magic_name__	:   Optional[int]					     =					DDIMScheduler.from_config(scheduler.config      )

			self.register_modules(unet=_A  ,					scheduler=_A      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Union[str, Any]      )  ->			Optional[int]:
			if strength < 0 or strength > 1:
				raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						List[str]  ,					_A		:						Optional[Any]  ,					_A		:						int      )  ->			List[Any]:
			# get the original timestep using init_timestep
			__magic_name__	:   Tuple					     =					min(int(num_inference_steps * strength      )  ,					_A      )

			__magic_name__	:   Any					     =					max(num_inference_steps - init_timestep  ,					0      )
			__magic_name__	:   List[str]					     =					self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str  ,					_A		:						Optional[int]  ,					_A		:						Tuple  ,					_A		:						List[str]  ,					_A		:						str  ,					_A		:						Optional[int]=None      )  ->			Dict:
			if not isinstance(_A  ,					(torch.Tensor, PIL.Image.Image, list)      ):
				raise ValueError(
				    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A      )}'      )

			__magic_name__	:   Union[str, Any]					     =					image.to(device=_A  ,					dtype=_A      )

			if isinstance(_A  ,					_A      ) and len(_A      ) != batch_size:
				raise ValueError(
				    F'You have passed a list of generators of length {len(_A      )}, but requested an effective batch'
				    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

			__magic_name__	:   Tuple					     =					init_latents.shape
			__magic_name__	:   Any					     =					randn_tensor(_A  ,					generator=_A  ,					device=_A  ,					dtype=_A      )

			# get latents
			print('add noise to latents at timestep'  ,					_A      )
			__magic_name__	:   List[str]					     =					self.scheduler.add_noise(_A  ,					_A  ,					_A      )
			__magic_name__	:   List[str]					     =					init_latents

			return latents







		@torch.no_grad()
		def __call__( self		:						Tuple  ,					_A		:						Union[torch.FloatTensor, PIL.Image.Image] = None  ,					_A		:						float = 0.8  ,					_A		:						int = 1  ,					_A		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,					_A		:						float = 0.0  ,					_A		:						int = 50  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[str] = "pil"  ,					_A		:						bool = True  ,					)  ->			Union[ImagePipelineOutput, Tuple]:
			self.check_inputs(_A      )

			# 2. Preprocess image
			__magic_name__	:   int					     =					preprocess(_A      )

			# 3. set timesteps
			self.scheduler.set_timesteps(_A  ,					device=self.device      )
			__magic_name__     ,    __magic_name__	:   Dict					     =					self.get_timesteps(_A  ,					_A  ,					self.device      )
			__magic_name__	:   Dict					     =					timesteps[:1].repeat(_A      )

			# 4. Prepare latent variables
			__magic_name__	:   Optional[Any]					     =					self.prepare_latents(_A  ,					_A  ,					_A  ,					self.unet.dtype  ,					self.device  ,					_A      )
			__magic_name__	:   Optional[Any]					     =					latents

			# 5. Denoising loop
			for t in self.progress_bar(_A      ):
				# 1. predict noise model_output
				__magic_name__	:   Dict					     =					self.unet(_A  ,					_A      ).sample

				# 2. predict previous mean of image x_t-1 and add variance depending on eta
				# eta corresponds to η in paper and should be between [0, 1]
				# do x_t -> x_t-1
				__magic_name__	:   List[Any]					     =					self.scheduler.step(
				    _A  ,					_A  ,					_A  ,					eta=_A  ,					use_clipped_model_output=_A  ,					generator=_A  ,					).prev_sample

			__magic_name__	:   Union[str, Any]					     =					(image / 2 + 0.5).clamp(0  ,					1      )
			__magic_name__	:   List[Any]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      ).numpy()
			if output_type == "pil":
				__magic_name__	:   Dict					     =					self.numpy_to_pil(_A      )

			if not return_dict:
				return (image, latent_timestep.item())

			return ImagePipelineOutput(images=_A      )