

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


A_	:			List[Any]  	=      logging.get_logger(__name__)



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	['''pixel_values''']






					def __init__(			self   ,	A__ = True   ,	A__ = 1 / 255   ,	A__ = True   ,	A__ = 8   ,	**A__   ,	):
						super().__init__(**A__      )

						A__							:   str					       =		do_rescale
						A__							:   Optional[Any]					       =		rescale_factor
						A__							:   Optional[int]					       =		do_pad
						A__							:   str					       =		pad_size






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = None   ,	**A__      ):
						return rescale(A__   ,	scale=A__   ,	data_format=A__   ,	**A__      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = None      ):
						A__       ,       A__							:   Dict					       =		get_image_size(A__      )
						A__							:   Tuple					       =		(old_height // size + 1) * size - old_height
						A__							:   Dict					       =		(old_width // size + 1) * size - old_width

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






					def   __A      (			self   ,	A__   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = ChannelDimension.FIRST   ,	**A__   ,	):
						A__							:   List[str]					       =		do_rescale if do_rescale is not None else self.do_rescale
						A__							:   str					       =		rescale_factor if rescale_factor is not None else self.rescale_factor
						A__							:   List[str]					       =		do_pad if do_pad is not None else self.do_pad
						A__							:   Any					       =		pad_size if pad_size is not None else self.pad_size

						A__							:   str					       =		make_list_of_images(A__      )

						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_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

						# All transformations expect numpy arrays.
						A__							:   str					       =		[to_numpy_array(A__      ) for image in images]

						if do_rescale:
							A__							:   Union[str, Any]					       =		[self.rescale(image=A__   ,	scale=A__      ) for image in images]

						if do_pad:
							A__							:   Optional[Any]					       =		[self.pad(A__   ,	size=A__      ) for image in images]

						A__							:   Optional[int]					       =		[to_channel_dimension_format(A__   ,	A__      ) for image in images]

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






from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def 						UpperCamelCase (lowercase_:			int )					->				str:
	return DownloadCommand(args.model   ,				args.cache_dir   ,				args.force   ,				args.trust_remote_code )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser("""download"""      )
						download_parser.add_argument(
						    """--cache-dir"""   ,	type=A__   ,	default=A__   ,	help="""Path to location to store the models"""      )
						download_parser.add_argument(
						    """--force"""   ,	action="""store_true"""   ,	help="""Force the model to be download even if already in cache-dir"""      )
						download_parser.add_argument(
						    """--trust-remote-code"""   ,	action="""store_true"""   ,	help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""   ,	)
						download_parser.add_argument("""model"""   ,	type=A__   ,	help="""Name of the model to download"""      )
						download_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Union[str, Any]					       =		model
						A__							:   Dict					       =		cache
						A__							:   str					       =		force
						A__							:   Tuple					       =		trust_remote_code






					def   __A      (			self      ):
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )
						AutoTokenizer.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )



