

import argparse
import os
import re

import packaging.version


lowerCAmelCase							:  Optional[Any]            =						"""examples/"""
lowerCAmelCase							:  Dict            =						{
    """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"""),
}
lowerCAmelCase							:  Optional[int]            =						{
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
lowerCAmelCase							:  int            =						"""README.md"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						with open(_UpperCAmelCase    ,     "r"    ,     encoding="utf-8"    ,     newline="\n"   ) as f:
												SCREAMING_SNAKE_CASE_:			Dict  		=	f.read()
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Dict  		=	REPLACE_PATTERNS[pattern]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	replace.replace("VERSION"    ,     _UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Any  		=	re_pattern.sub(_UpperCAmelCase    ,     _UpperCAmelCase   )
						with open(_UpperCAmelCase    ,     "w"    ,     encoding="utf-8"    ,     newline="\n"   ) as f:
												f.write(_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase   ):
						for folder, directories, fnames in os.walk(_UpperCAmelCase   ):
												# 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(_UpperCAmelCase    ,     _UpperCAmelCase   )    ,     _UpperCAmelCase    ,     pattern="examples"   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
						for pattern, fname in REPLACE_FILES.items():
												update_version_in_file(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
						if not patch:
												update_version_in_examples(_UpperCAmelCase   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	"🤗 Transformers currently provides the following architectures"
						SCREAMING_SNAKE_CASE_:			List[str]  		=	"1. Want to contribute a new model?"
						with open(_UpperCAmelCase    ,     "r"    ,     encoding="utf-8"    ,     newline="\n"   ) as f:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	f.readlines()

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

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

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







def       A_				(   ):
						with open(REPLACE_FILES["init"]    ,     "r"   ) as f:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	f.read()
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	REPLACE_PATTERNS["init"][0].search(_UpperCAmelCase   ).groups()[0]
						return packaging.version.parse(_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	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:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	default_version.base_version
						elif patch:
												SCREAMING_SNAKE_CASE_:			Any  		=	f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
						else:
												SCREAMING_SNAKE_CASE_:			Dict  		=	f"{default_version.major}.{default_version.minor + 1}.0"

						# Now let's ask nicely if that's the right one.
						SCREAMING_SNAKE_CASE_:			int  		=	input(f"Which version are you releasing? [{default_version}]"   )
						if len(_UpperCAmelCase   ) == 0:
												SCREAMING_SNAKE_CASE_:			str  		=	default_version

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







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			int  		=	get_version()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	f"{current_version.major}.{current_version.minor + 1}.0.dev0"
						SCREAMING_SNAKE_CASE_:			List[str]  		=	current_version.base_version

						# Check with the user we got that right.
						SCREAMING_SNAKE_CASE_:			int  		=	input(f"Which version are we developing now? [{dev_version}]"   )
						if len(_UpperCAmelCase   ) == 0:
												SCREAMING_SNAKE_CASE_:			int  		=	dev_version

						print(f"Updating version to {version}."   )
						global_version_update(_UpperCAmelCase   )
						print("Cleaning main README, don't forget to run `make fix-copies`."   )
						clean_main_ref_in_model_list()


if __name__ == "__main__":
						lowerCAmelCase							:  str            =						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.""")
						lowerCAmelCase							:  int            =						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()









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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCAmelCase							:  Optional[Any]            =						logging.get_logger(__name__)



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					['''pixel_values''']



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Dict[str, int] = None       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Union[int, float] = 1 / 255       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					bool = True       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else {"height": 384, "width": 384}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Dict  		=	size
									SCREAMING_SNAKE_CASE_:			int  		=	resample
									SCREAMING_SNAKE_CASE_:			str  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			str  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_std if image_std is not None else OPENAI_CLIP_STD
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Dict[str, int]       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									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_:			int  		=	(size["height"], size["width"])
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[int, float]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return rescale(lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					ImageInput       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Dict[str, int]] = None       ,						lowerCAmelCase__  :					PILImageResampling = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[float] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[str, TensorType]] = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					ChannelDimension = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Dict       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Any  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	make_list_of_images(lowerCAmelCase__)

									if not valid_images(lowerCAmelCase__):
															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.")

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[convert_to_rgb(lowerCAmelCase__) for image in images]

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[to_numpy_array(lowerCAmelCase__) for image in images]

									if do_resize:
															SCREAMING_SNAKE_CASE_:			int  		=	[self.resize(image=lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__) for image in images]

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__) for image in images]

									if do_normalize:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.normalize(image=lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			str  		=	[to_channel_dimension_format(lowerCAmelCase__       ,						lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BatchFeature(data={"pixel_values": images}       ,						tensor_type=lowerCAmelCase__)

									return encoded_outputs






