




import argparse
import os
import re

import packaging.version


UpperCAmelCase_									=	'examples/'
UpperCAmelCase_									=	{
    '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'),
}
UpperCAmelCase_									=	{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
UpperCAmelCase_									=	'README.md'




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					Dict  ,			A__       :					Tuple	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Optional[int]	):





       '''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 					lowerCamelCase__       (						A__       :					List[Any]  ,			A__       :					List[Any]=False	):





       '''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 					lowerCamelCase__       (						):





       '''simple docstring'''



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

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

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

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




def 					lowerCamelCase__       (						):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Any=False	):





       '''simple docstring'''



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

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

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




def 					lowerCamelCase__       (						):





       '''simple docstring'''



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

       # Check with the user we got that right.
       __lowerCamelCase   =						input(f'Which version are we developing now? [{dev_version}]'	)
       if len(A__	) == 0:
              __lowerCamelCase   =						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__":
       UpperCAmelCase_									=	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.')
       UpperCAmelCase_									=	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 Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: int = 8    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						pad_size
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Tuple   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None   ):
             __lowerCamelCase,   __lowerCamelCase   =						get_image_size(UpperCamelCase_   )
             __lowerCamelCase   =						(old_height // size + 1) * size - old_height
             __lowerCamelCase   =						(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase_    ,       ((0, pad_height), (0, pad_width))    ,       mode="""symmetric"""    ,       data_format=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Any    ,       ):
             __lowerCamelCase   =						do_rescale if do_rescale is not None else self.do_rescale
             __lowerCamelCase   =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __lowerCamelCase   =						do_pad if do_pad is not None else self.do_pad
             __lowerCamelCase   =						pad_size if pad_size is not None else self.pad_size

             __lowerCamelCase   =						make_list_of_images(UpperCamelCase_   )

             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."""   )

             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.
             __lowerCamelCase   =						[to_numpy_array(UpperCamelCase_   ) for image in images]

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

             if do_pad:
                    __lowerCamelCase   =						[self.pad(UpperCamelCase_    ,       size=UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )

