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
__UpperCamelCase	: str    		=  "src/diffusers"
__UpperCamelCase	: Dict    		=  "."


# This is to make sure the diffusers module imported is the one in the repo.
__UpperCamelCase	: Any    		=  importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
__UpperCamelCase	: Optional[Any]    		=  spec.loader.load_module()
def    A			(					_lowercase	,						_lowercase		)     ->   Union[str, Any]:
						return line.startswith(lowerCamelCase__		) or len(lowerCamelCase__		) <= 1 or re.search(R'''^\s*\)(\s*->.*:|:)\s*$'''	,						lowerCamelCase__		) is not None
def    A			(					_lowercase		)     ->   str:
						SCREAMING_SNAKE_CASE							:					Any           =							object_name.split('''.'''		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							0

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

						with open(os.path.join(lowerCamelCase__	,						f"""{module}.py"""		)	,						'''r'''	,						encoding='''utf-8'''	,						newline='''\n'''		) as f:
												SCREAMING_SNAKE_CASE							:					List[str]           =							f.readlines()

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

						SCREAMING_SNAKE_CASE							:					Any           =							lines[start_index:line_index]
						return "".join(lowerCamelCase__		)


__UpperCamelCase	: List[Any]    		=  re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
__UpperCamelCase	: Tuple    		=  re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
__UpperCamelCase	: Tuple    		=  re.compile(R'<FILL\s+[^>]*>')
def    A			(					_lowercase		)     ->   Dict:
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							code.split('''\n'''		)
						SCREAMING_SNAKE_CASE							:					Any           =							0
						while idx < len(lowerCamelCase__		) and len(lines[idx]		) == 0:
												idx += 1
						if idx < len(lowerCamelCase__		):
												return re.search(R'''^(\s*)\S'''	,						lines[idx]		).groups()[0]
						return ""
def    A			(					_lowercase		)     ->   Optional[Any]:
						SCREAMING_SNAKE_CASE							:					Dict           =							len(get_indent(lowerCamelCase__		)		) > 0
						if has_indent:
												SCREAMING_SNAKE_CASE							:					Tuple           =							f"""class Bla:\n{code}"""
						SCREAMING_SNAKE_CASE							:					int           =							black.Mode(target_versions={black.TargetVersion.PYaa}	,						line_length=119	,						preview=lowerCamelCase__		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							black.format_str(lowerCamelCase__	,						mode=lowerCamelCase__		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							style_docstrings_in_code(lowerCamelCase__		)
						return result[len('''class Bla:\n'''		) :] if has_indent else result
def    A			(					_lowercase	,						_lowercase=False		)     ->   Any:


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

												SCREAMING_SNAKE_CASE							:					Tuple           =							line_index + 1 if indent == theoretical_indent else line_index + 2
												SCREAMING_SNAKE_CASE							:					str           =							theoretical_indent
												SCREAMING_SNAKE_CASE							:					Any           =							start_index

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

												SCREAMING_SNAKE_CASE							:					Dict           =							lines[start_index:line_index]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							"".join(lowerCamelCase__		)

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

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

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

						if overwrite and len(lowerCamelCase__		) > 0:
												# Warn the user a file has been modified.
												print(f"""Detected changes, rewriting {filename}."""		)
												with open(lowerCamelCase__	,						'''w'''	,						encoding='''utf-8'''	,						newline='''\n'''		) as f:
																		f.writelines(lowerCamelCase__		)
						return diffs
def    A			(					_lowercase = False		)     ->   Optional[int]:
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							glob.glob(os.path.join(lowerCamelCase__	,						'''**/*.py'''		)	,						recursive=lowerCamelCase__		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]
						for filename in all_files:
												SCREAMING_SNAKE_CASE							:					Tuple           =							is_copy_consistent(lowerCamelCase__	,						lowerCamelCase__		)
												diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
						if not overwrite and len(lowerCamelCase__		) > 0:
												SCREAMING_SNAKE_CASE							:					Dict           =							"\n".join(lowerCamelCase__		)
												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__":
					__UpperCamelCase	: Union[str, Any]    		=  argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					__UpperCamelCase	: Any    		=  parser.parse_args()

					check_copies(args.fix_and_overwrite)






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


__UpperCamelCase	: int    		=  logging.get_logger(__name__)
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						def constraint_to_multiple_of(_lowercase	,						_lowercase	,						_lowercase=0	,						_lowercase=None		):
												SCREAMING_SNAKE_CASE							:					int           =							round(val / multiple		) * multiple

												if max_val is not None and x > max_val:
																		SCREAMING_SNAKE_CASE							:					Dict           =							math.floor(val / multiple		) * multiple

												if x < min_val:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							math.ceil(val / multiple		) * multiple

												return x

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(output_size, output_size) if isinstance(_lowercase	,						_lowercase		) else output_size

						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_image_size(_lowercase		)
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[int]           =							output_size

						# determine new height and width
						SCREAMING_SNAKE_CASE							:					Dict           =							output_height / input_height
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							output_width / input_width

						if keep_aspect_ratio:
												# scale as little as possible
												if abs(1 - scale_width		) < abs(1 - scale_height		):
																		# fit width
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_width
												else:
																		# fit height
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scale_height

						SCREAMING_SNAKE_CASE							:					List[str]           =							constraint_to_multiple_of(scale_height * input_height	,						multiple=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							constraint_to_multiple_of(scale_width * input_width	,						multiple=_lowercase		)

						return (new_height, new_width)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""pixel_values"""]


						def __init__( self			:       int					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Dict[str, int] = None					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BILINEAR					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Union[int, float] = 1 / 255					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							size if size is not None else {'''height''': 384, '''width''': 384}
												SCREAMING_SNAKE_CASE							:					Any           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							do_resize
												SCREAMING_SNAKE_CASE							:					Any           =							size
												SCREAMING_SNAKE_CASE							:					str           =							keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					int           =							resample
												SCREAMING_SNAKE_CASE							:					Any           =							do_rescale
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rescale_factor
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							do_normalize
												SCREAMING_SNAKE_CASE							:					str           =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							image_std if image_std is not None else IMAGENET_STANDARD_STD


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Dict[str, int]					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       int = 1					,	UpperCamelCase__			:       PILImageResampling = PILImageResampling.BICUBIC					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							get_size_dict(UpperCamelCase__		)
												if "height" not in size or "width" not in size:
																		raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""		)
												SCREAMING_SNAKE_CASE							:					Any           =							get_resize_output_image_size(
												    UpperCamelCase__					,	output_size=(size['''height'''], size['''width'''])					,	keep_aspect_ratio=UpperCamelCase__					,	multiple=UpperCamelCase__					,	)
												return resize(UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[int, float]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												return rescale(UpperCamelCase__					,	scale=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       np.ndarray					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Union[float, List[float]]					,	UpperCamelCase__			:       Optional[Union[str, ChannelDimension]] = None					,	**UpperCamelCase__			:       List[str]					,	):





												'''simple docstring'''





												return normalize(UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__					,	data_format=UpperCamelCase__					,	**UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       ImageInput					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       int = None					,	UpperCamelCase__			:       PILImageResampling = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       float = None					,	UpperCamelCase__			:       bool = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[float, List[float]]] = None					,	UpperCamelCase__			:       Optional[Union[str, TensorType]] = None					,	UpperCamelCase__			:       ChannelDimension = ChannelDimension.FIRST					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							size if size is not None else self.size
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_size_dict(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												SCREAMING_SNAKE_CASE							:					List[str]           =							ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												SCREAMING_SNAKE_CASE							:					Tuple           =							resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE							:					str           =							do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE							:					Dict           =							make_list_of_images(UpperCamelCase__		)

												if not valid_images(UpperCamelCase__		):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

												if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''		)

												if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

												if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

												# All transformations expect numpy arrays.
												SCREAMING_SNAKE_CASE							:					Tuple           =							[to_numpy_array(UpperCamelCase__		) for image in images]

												if do_resize:
																		SCREAMING_SNAKE_CASE							:					Dict           =							[self.resize(image=UpperCamelCase__					,	size=UpperCamelCase__					,	resample=UpperCamelCase__		) for image in images]

												if do_rescale:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.rescale(image=UpperCamelCase__					,	scale=UpperCamelCase__		) for image in images]

												if do_normalize:
																		SCREAMING_SNAKE_CASE							:					Any           =							[self.normalize(image=UpperCamelCase__					,	mean=UpperCamelCase__					,	std=UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[to_channel_dimension_format(UpperCamelCase__					,	UpperCamelCase__		) for image in images]

												SCREAMING_SNAKE_CASE							:					Tuple           =							{'''pixel_values''': images}
												return BatchFeature(data=UpperCamelCase__					,	tensor_type=UpperCamelCase__		)



						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Tuple] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																		if len(UpperCamelCase__		) != len(UpperCamelCase__		):
																								raise ValueError(
																								    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''		)

																		if is_torch_tensor(UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							target_sizes.numpy()

																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]

																		for idx in range(len(UpperCamelCase__		)		):
																								SCREAMING_SNAKE_CASE							:					List[str]           =							torch.nn.functional.interpolate(
																								    logits[idx].unsqueeze(dim=0		)					,	size=target_sizes[idx]					,	mode='''bilinear'''					,	align_corners=UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Dict           =							resized_logits[0].argmax(dim=0		)
																								semantic_segmentation.append(UpperCamelCase__		)
												else:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							logits.argmax(dim=1		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

												return semantic_segmentation





