"""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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





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






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





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

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

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

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

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


__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      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





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






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





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






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=False ):






							"""simple docstring"""





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

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

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

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

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

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

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

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






def      UpperCAmelCase    (		UpperCamelCase__ = False ):






							"""simple docstring"""





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




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if isinstance(UpperCamelCase__				,			(list, tuple) ) and isinstance(videos[0]				,			(list, tuple) ) and is_valid_image(videos[0][0] ):
														return videos

							elif isinstance(UpperCamelCase__				,			(list, tuple) ) and is_valid_image(videos[0] ):
														return [videos]

							elif is_valid_image(UpperCamelCase__ ):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}''' )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = 1 / 2_55						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'shortest_edge': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													A__		   =							do_resize
													A__		   =							size
													A__		   =							do_center_crop
													A__		   =							crop_size
													A__		   =							resample
													A__		   =							do_rescale
													A__		   =							rescale_factor
													A__		   =							offset
													A__		   =							do_normalize
													A__		   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A__		   =							image_std if image_std is not None else IMAGENET_STANDARD_STD





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													if "shortest_edge" in size:
																				A__		   =							get_resize_output_image_size(__UpperCAmelCase						,size['shortest_edge']						,default_to_square=__UpperCAmelCase   )
													elif "height" in size and "width" in size:
																				A__		   =							(size['height'], size['width'])
													else:
																				raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
													return resize(__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''   )
													return center_crop(__UpperCAmelCase						,size=(size['height'], size['width'])						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													A__		   =							image.astype(np.floataa   )
													if offset:
																				A__		   =							image - (scale / 2)
													return rescale(__UpperCAmelCase						,scale=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													return normalize(__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,)    -> np.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_center_crop and crop_size is None:
																				raise ValueError('Crop size must be specified if do_center_crop 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.'   )

													if offset and not do_rescale:
																				raise ValueError('For offset, do_rescale must also be set to True.'   )

													# All transformations expect numpy arrays.
													A__		   =							to_numpy_array(__UpperCAmelCase   )

													if do_resize:
																				A__		   =							self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   )

													if do_center_crop:
																				A__		   =							self.center_crop(__UpperCAmelCase						,size=__UpperCAmelCase   )

													if do_rescale:
																				A__		   =							self.rescale(image=__UpperCAmelCase						,scale=__UpperCAmelCase						,offset=__UpperCAmelCase   )

													if do_normalize:
																				A__		   =							self.normalize(image=__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase   )

													A__		   =							to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   )
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__		   =							do_rescale if do_rescale is not None else self.do_rescale
													A__		   =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__		   =							offset if offset is not None else self.offset
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							image_mean if image_mean is not None else self.image_mean
													A__		   =							image_std if image_std is not None else self.image_std

													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else self.crop_size
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													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.'   )

													A__		   =							make_batched(__UpperCAmelCase   )

													A__		   =							[
													    [
													        self._preprocess_image(
													            image=__UpperCAmelCase						,do_resize=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,do_center_crop=__UpperCAmelCase						,crop_size=__UpperCAmelCase						,do_rescale=__UpperCAmelCase						,rescale_factor=__UpperCAmelCase						,offset=__UpperCAmelCase						,do_normalize=__UpperCAmelCase						,image_mean=__UpperCAmelCase						,image_std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,)
													        for img in video
													    ]
													    for video in videos
													]

													A__		   =							{'pixel_values': videos}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





