
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

SCREAMING_SNAKE_CASE							:List[str]										=    logging.get_logger(__name__)


def 						UpperCAmelCase      (     a_						)		->     List[List[ImageInput]]:







				"""simple docstring"""



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

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

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

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





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					["pixel_values"]






	def __init__(	self :			Union[str, Any]							,A :			bool = True							,A :			Dict[str, int] = None							,A :			PILImageResampling = PILImageResampling.BILINEAR							,A :			bool = True							,A :			Dict[str, int] = None							,A :			bool = True							,A :			Union[int, float] = 1 / 2_55							,A :			bool = True							,A :			bool = True							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[Union[float, List[float]]] = None							,**A :			Optional[Any]							,):
					super().__init__(**A			)
					__A             =			size if size is not None else {"shortest_edge": 2_56}
					__A             =			get_size_dict(A							,default_to_square=A			)
					__A             =			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
					__A             =			get_size_dict(A							,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     UpperCamelCase_  (	self :			List[str]							,A :			np.ndarray							,A :			Dict[str, int]							,A :			PILImageResampling = PILImageResampling.BILINEAR							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			int							,):
					__A             =			get_size_dict(A							,default_to_square=A			)
					if "shortest_edge" in size:
									__A             =			get_resize_output_image_size(A							,size["shortest_edge"]							,default_to_square=A			)
					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(A							,size=A							,resample=A							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			List[Any]							,A :			np.ndarray							,A :			Dict[str, int]							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			Dict							,):
					__A             =			get_size_dict(A			)
					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(A							,size=(size["height"], size["width"])							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			np.ndarray							,A :			Union[int, float]							,A :			bool = True							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			List[Any]							,):
					__A             =			image.astype(np.floataa			)
					if offset:
									__A             =			image - (scale / 2)
					return rescale(A							,scale=A							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			Any							,A :			np.ndarray							,A :			Union[float, List[float]]							,A :			Union[float, List[float]]							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			Dict							,):
					return normalize(A							,mean=A							,std=A							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			Any							,A :			ImageInput							,A :			bool = None							,A :			Dict[str, int] = None							,A :			PILImageResampling = None							,A :			bool = None							,A :			Dict[str, int] = None							,A :			bool = None							,A :			float = None							,A :			bool = None							,A :			bool = None							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[ChannelDimension] = ChannelDimension.FIRST							,):
					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(A			)

					if do_resize:
									__A             =			self.resize(image=A							,size=A							,resample=A			)

					if do_center_crop:
									__A             =			self.center_crop(A							,size=A			)

					if do_rescale:
									__A             =			self.rescale(image=A							,scale=A							,offset=A			)

					if do_normalize:
									__A             =			self.normalize(image=A							,mean=A							,std=A			)

					__A             =			to_channel_dimension_format(A							,A			)
					return image







	def     UpperCamelCase_  (	self :			str							,A :			ImageInput							,A :			bool = None							,A :			Dict[str, int] = None							,A :			PILImageResampling = None							,A :			bool = None							,A :			Dict[str, int] = None							,A :			bool = None							,A :			float = None							,A :			bool = None							,A :			bool = None							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[Union[str, TensorType]] = None							,A :			ChannelDimension = ChannelDimension.FIRST							,**A :			Dict							,):
					__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(A							,default_to_square=A			)
					__A             =			crop_size if crop_size is not None else self.crop_size
					__A             =			get_size_dict(A							,param_name="crop_size"			)

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

					__A             =			make_batched(A			)

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

					__A             =			{"pixel_values": videos}
					return BatchFeature(data=A							,tensor_type=A			)


import argparse
import os
import re

import packaging.version


SCREAMING_SNAKE_CASE							:int										=    'examples/'
SCREAMING_SNAKE_CASE							:str										=    {
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
SCREAMING_SNAKE_CASE							:int										=    {
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
SCREAMING_SNAKE_CASE							:List[str]										=    'README.md'


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				with open(a_		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
								__A             =			f.read()
				__A      ,							__A             =			REPLACE_PATTERNS[pattern]
				__A             =			replace.replace("VERSION"		,       a_						)
				__A             =			re_pattern.sub(a_		,       a_						)
				with open(a_		,       "w"		,       encoding="utf-8"		,       newline="\n"						) as f:
								f.write(a_						)


def 						UpperCAmelCase      (     a_						)		->     Optional[Any]:







				"""simple docstring"""



				for folder, directories, fnames in os.walk(a_						):
								# Removing some of the folders with non-actively maintained examples from the walk
								if "research_projects" in directories:
												directories.remove("research_projects"						)
								if "legacy" in directories:
												directories.remove("legacy"						)
								for fname in fnames:
												if fname.endswith(".py"						):
																update_version_in_file(os.path.join(a_		,       a_						)		,       a_		,       pattern="examples"						)


def 						UpperCAmelCase      (     a_		,       a_=False						)		->     str:







				"""simple docstring"""



				for pattern, fname in REPLACE_FILES.items():
								update_version_in_file(a_		,       a_		,       a_						)
				if not patch:
								update_version_in_examples(a_						)


def 						UpperCAmelCase      (     )		->     Optional[int]:







				"""simple docstring"""



				__A             =			"🤗 Transformers currently provides the following architectures"
				__A             =			"1. Want to contribute a new model?"
				with open(a_		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
								__A             =			f.readlines()

				# Find the start of the list.
				__A             =			0
				while not lines[start_index].startswith(_start_prompt						):
								start_index += 1
				start_index += 1

				__A             =			start_index
				# Update the lines in the model list.
				while not lines[index].startswith(_end_prompt						):
								if lines[index].startswith("1."						):
												__A             =			lines[index].replace(
												    "https://huggingface.co/docs/diffusers/main/model_doc"		,       "https://huggingface.co/docs/diffusers/model_doc"		,       )
								index += 1

				with open(a_		,       "w"		,       encoding="utf-8"		,       newline="\n"						) as f:
								f.writelines(a_						)


def 						UpperCAmelCase      (     )		->     Dict:







				"""simple docstring"""



				with open(REPLACE_FILES["init"]		,       "r"						) as f:
								__A             =			f.read()
				__A             =			REPLACE_PATTERNS["init"][0].search(a_						).groups()[0]
				return packaging.version.parse(a_						)


def 						UpperCAmelCase      (     a_=False						)		->     str:







				"""simple docstring"""



				__A             =			get_version()
				if patch and default_version.is_devrelease:
								raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"						)
				if default_version.is_devrelease:
								__A             =			default_version.base_version
				elif patch:
								__A             =			F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
				else:
								__A             =			F'''{default_version.major}.{default_version.minor + 1}.0'''

				# Now let's ask nicely if that's the right one.
				__A             =			input(F'''Which version are you releasing? [{default_version}]'''						)
				if len(a_						) == 0:
								__A             =			default_version

				print(F'''Updating version to {version}.'''						)
				global_version_update(a_		,       patch=a_						)


def 						UpperCAmelCase      (     )		->     Optional[int]:







				"""simple docstring"""



				__A             =			get_version()
				__A             =			F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
				__A             =			current_version.base_version

				# Check with the user we got that right.
				__A             =			input(F'''Which version are we developing now? [{dev_version}]'''						)
				if len(a_						) == 0:
								__A             =			dev_version

				print(F'''Updating version to {version}.'''						)
				global_version_update(a_						)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    argparse.ArgumentParser()
					parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
					parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print('Nothing to do after a patch :-)')
					else:
										post_release_work()
