
"""simple docstring"""





import argparse
import os
import re

import packaging.version


_a								=						"""examples/"""
_a								=						{
    """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"""),
}
_a								=						{
    """init""": """src/diffusers/__init__.py""",
    """setup""": """setup.py""",
}
_a								=						"""README.md"""



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					for folder, directories, fnames in os.walk(__snake_case      ):
										# 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(__snake_case,	__snake_case      ),	__snake_case,	pattern='''examples'''      )



def  lowerCamelCase__						(     __snake_case,	__snake_case=False      )				->						Dict:


					"""simple docstring"""





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



def  lowerCamelCase__						(     )				->						Union[str, Any]:


					"""simple docstring"""





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

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

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

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



def  lowerCamelCase__						(     )				->						str:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case=False      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    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:
										_UpperCamelCase									=    default_version.base_version
					elif patch:
										_UpperCamelCase									=    F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
					else:
										_UpperCamelCase									=    F'''{default_version.major}.{default_version.minor + 1}.0'''

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

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



def  lowerCamelCase__						(     )				->						Any:


					"""simple docstring"""





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

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

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


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


if __name__ == "__main__":
				_a								=						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.""")
				_a								=						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()








"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a								=						logging.get_logger(__name__)






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['pixel_values']



		def __init__(   self							,						__a = True							,						__a = None							,						__a = PILImageResampling.BICUBIC							,						__a = True							,						__a = True							,						__a = 1 / 2_55							,						__a = None							,						__a = True							,						__a = None							,						__a = None							,						**__a							,						)			->			None:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase									=    size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a)
							_UpperCamelCase									=    crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a							,						param_name='''crop_size''')

							_UpperCamelCase									=    do_resize
							_UpperCamelCase									=    do_rescale
							_UpperCamelCase									=    do_normalize
							_UpperCamelCase									=    do_center_crop
							_UpperCamelCase									=    crop_size
							_UpperCamelCase									=    size
							_UpperCamelCase									=    resample
							_UpperCamelCase									=    rescale_factor
							_UpperCamelCase									=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_UpperCamelCase									=    image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = PILImageResampling.BILINEAR							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a)

							if "shortest_edge" in size:
												_UpperCamelCase									=    get_resize_output_image_size(__a							,						size=size['''shortest_edge''']							,						default_to_square=__a)
												# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
												_UpperCamelCase									=    (size['''height'''], size['''width'''])
							else:
												raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
							return resize(__a							,						size=__a							,						resample=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
							return center_crop(__a							,						size=(size['''height'''], size['''width'''])							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a)			->			np.ndarray:






							'''simple docstring'''
							return rescale(__a							,						scale=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							return normalize(__a							,						mean=__a							,						std=__a							,						data_format=__a							,						**__a)




		def 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = ChannelDimension.FIRST							,						**__a							,						)			->			BatchFeature:






							'''simple docstring'''
							_UpperCamelCase									=    do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase									=    do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase									=    do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase									=    do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase									=    crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase									=    get_size_dict(__a							,						param_name='''crop_size'''							,						default_to_square=__a)
							_UpperCamelCase									=    resample if resample is not None else self.resample
							_UpperCamelCase									=    rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase									=    image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase									=    image_std if image_std is not None else self.image_std

							_UpperCamelCase									=    size if size is not None else self.size
							_UpperCamelCase									=    get_size_dict(__a)

							if not is_batched(__a):
												_UpperCamelCase									=    [images]

							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.''')

							if do_resize and size is None:
												raise ValueError('''Size 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.''')

							# All transformations expect numpy arrays.
							_UpperCamelCase									=    [to_numpy_array(__a) for image in images]

							if do_resize:
												_UpperCamelCase									=    [self.resize(image=__a							,						size=__a							,						resample=__a) for image in images]

							if do_center_crop:
												_UpperCamelCase									=    [self.center_crop(image=__a							,						size=__a) for image in images]

							if do_rescale:
												_UpperCamelCase									=    [self.rescale(image=__a							,						scale=__a) for image in images]

							if do_normalize:
												_UpperCamelCase									=    [self.normalize(image=__a							,						mean=__a							,						std=__a) for image in images]

							_UpperCamelCase									=    [to_channel_dimension_format(__a							,						__a) for image in images]

							_UpperCamelCase									=    {'''pixel_values''': images}
							return BatchFeature(data=__a							,						tensor_type=__a)






