






"""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		:       Union[str, Any]  				=		logging.get_logger(__name__)





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   ["pixel_values"]




					def __init__(				self     :						str    ,						A     :						bool = True    ,						A     :						Optional[Dict[str, int]] = None    ,						A     :						PILImageResampling = PILImageResampling.BICUBIC    ,						A     :						bool = True    ,						A     :						bool = True    ,						A     :						Union[int, float] = 1 / 2_55    ,						A     :						Dict[str, int] = None    ,						A     :						bool = True    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						**A     :						Dict    ,						)  ->  None:
							super().__init__(**A   )
							lowercase_ : Tuple							       =   size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							lowercase_ : Dict							       =   get_size_dict(A   )
							lowercase_ : Union[str, Any]							       =   crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							lowercase_ : List[Any]							       =   get_size_dict(A    ,						default_to_square=A    ,						param_name='''crop_size'''   )

							lowercase_ : Optional[Any]							       =   do_resize
							lowercase_ : List[str]							       =   do_rescale
							lowercase_ : Optional[int]							       =   do_normalize
							lowercase_ : List[str]							       =   do_center_crop
							lowercase_ : Union[str, Any]							       =   crop_size
							lowercase_ : Optional[Any]							       =   size
							lowercase_ : Optional[Any]							       =   resample
							lowercase_ : Optional[Any]							       =   rescale_factor
							lowercase_ : Tuple							       =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							lowercase_ : Tuple							       =   image_std if image_std is not None else IMAGENET_DEFAULT_STD




					def   A  (				self     :						str    ,						A     :						np.ndarray    ,						A     :						Dict[str, int]    ,						A     :						PILImageResampling = PILImageResampling.BILINEAR    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						List[str]    ,						)  ->  np.ndarray:
							lowercase_ : str							       =   get_size_dict(A   )

							if "shortest_edge" in size:
									lowercase_ : Dict							       =   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:
									lowercase_ : List[Any]							       =   (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   A  (				self     :						str    ,						A     :						np.ndarray    ,						A     :						Dict[str, int]    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Dict    ,						)  ->  np.ndarray:
							lowercase_ : List[Any]							       =   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   A  (				self     :						Tuple    ,						A     :						np.ndarray    ,						A     :						float    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Any   )  ->  np.ndarray:
							return rescale(A    ,						scale=A    ,						data_format=A    ,						**A   )




					def   A  (				self     :						Optional[int]    ,						A     :						np.ndarray    ,						A     :						Union[float, List[float]]    ,						A     :						Union[float, List[float]]    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Tuple    ,						)  ->  np.ndarray:
							return normalize(A    ,						mean=A    ,						std=A    ,						data_format=A    ,						**A   )






					def   A  (				self     :						Dict    ,						A     :						ImageInput    ,						A     :						Optional[bool] = None    ,						A     :						Dict[str, int] = None    ,						A     :						PILImageResampling = None    ,						A     :						bool = None    ,						A     :						int = None    ,						A     :						Optional[bool] = None    ,						A     :						Optional[float] = None    ,						A     :						Optional[bool] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[str, TensorType]] = None    ,						A     :						Union[str, ChannelDimension] = ChannelDimension.FIRST    ,						**A     :						List[str]    ,						)  ->  BatchFeature:
							lowercase_ : str							       =   do_resize if do_resize is not None else self.do_resize
							lowercase_ : int							       =   do_rescale if do_rescale is not None else self.do_rescale
							lowercase_ : str							       =   do_normalize if do_normalize is not None else self.do_normalize
							lowercase_ : Any							       =   do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase_ : str							       =   crop_size if crop_size is not None else self.crop_size
							lowercase_ : int							       =   get_size_dict(A    ,						param_name='''crop_size'''    ,						default_to_square=A   )
							lowercase_ : Union[str, Any]							       =   resample if resample is not None else self.resample
							lowercase_ : Any							       =   rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase_ : Tuple							       =   image_mean if image_mean is not None else self.image_mean
							lowercase_ : Tuple							       =   image_std if image_std is not None else self.image_std

							lowercase_ : Union[str, Any]							       =   size if size is not None else self.size
							lowercase_ : str							       =   get_size_dict(A   )

							if not is_batched(A   ):
									lowercase_ : List[Any]							       =   [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.
							lowercase_ : Tuple							       =   [to_numpy_array(A   ) for image in images]

							if do_resize:
									lowercase_ : List[str]							       =   [self.resize(image=A    ,						size=A    ,						resample=A   ) for image in images]

							if do_center_crop:
									lowercase_ : List[Any]							       =   [self.center_crop(image=A    ,						size=A   ) for image in images]

							if do_rescale:
									lowercase_ : List[Any]							       =   [self.rescale(image=A    ,						scale=A   ) for image in images]

							if do_normalize:
									lowercase_ : Dict							       =   [self.normalize(image=A    ,						mean=A    ,						std=A   ) for image in images]

							lowercase_ : List[str]							       =   [to_channel_dimension_format(A    ,						A   ) for image in images]

							lowercase_ : Optional[Any]							       =   {'''pixel_values''': images}
							return BatchFeature(data=A    ,						tensor_type=A   )











"""simple docstring"""



import argparse
import os
import re

import packaging.version


__A		:       List[str]  				=		'''examples/'''
__A		:       int  				=		{
    '''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		:       Dict  				=		{
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
__A		:       Optional[int]  				=		'''README.md'''



def 						lowercase    (				__snake_case				: int		,					__snake_case				: Any		,					__snake_case				: int	):
		with open(__snake_case		,					'''r'''		,					encoding='''utf-8'''		,					newline='''\n'''	) as f:
				lowercase_ : int							       =   f.read()
		lowercase_       ,					lowercase_ : List[str]							       =   REPLACE_PATTERNS[pattern]
		lowercase_ : Union[str, Any]							       =   replace.replace('''VERSION'''		,					__snake_case	)
		lowercase_ : Optional[Any]							       =   re_pattern.sub(__snake_case		,					__snake_case	)
		with open(__snake_case		,					'''w'''		,					encoding='''utf-8'''		,					newline='''\n'''	) as f:
				f.write(__snake_case	)



def 						lowercase    (				__snake_case				: int	):
		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 						lowercase    (				__snake_case				: Optional[Any]		,					__snake_case				: Optional[Any]=False	):
		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 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   '''🤗 Transformers currently provides the following architectures'''
		lowercase_ : Union[str, Any]							       =   '''1. Want to contribute a new model?'''
		with open(__snake_case		,					'''r'''		,					encoding='''utf-8'''		,					newline='''\n'''	) as f:
				lowercase_ : List[str]							       =   f.readlines()

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

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

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



def 						lowercase    (				):
		with open(REPLACE_FILES['''init''']		,					'''r'''	) as f:
				lowercase_ : List[Any]							       =   f.read()
		lowercase_ : List[str]							       =   REPLACE_PATTERNS['''init'''][0].search(__snake_case	).groups()[0]
		return packaging.version.parse(__snake_case	)



def 						lowercase    (				__snake_case				: Optional[Any]=False	):
		lowercase_ : str							       =   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:
				lowercase_ : Optional[Any]							       =   default_version.base_version
		elif patch:
				lowercase_ : Optional[int]							       =   F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
		else:
				lowercase_ : Optional[int]							       =   F'''{default_version.major}.{default_version.minor + 1}.0'''

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

		print(F'''Updating version to {version}.'''	)
		global_version_update(__snake_case		,					patch=__snake_case	)
		if not patch:
				print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''	)
				clean_main_ref_in_model_list()



def 						lowercase    (				):
		lowercase_ : List[Any]							       =   get_version()
		lowercase_ : List[str]							       =   F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
		lowercase_ : Any							       =   current_version.base_version

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



