

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_	:			str  	=      logging.get_logger(__name__)



class    _a						(__magic_name__    ):


     '''simple docstring'''






     UpperCAmelCase__:       Optional[Any]  							=	['''pixel_values''']






     def __init__(			self   ,	A__ = True   ,	A__ = None   ,	A__ = PILImageResampling.BICUBIC   ,	A__ = True   ,	A__ = True   ,	A__ = 1 / 255   ,	A__ = None   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
      super().__init__(**A__      )
      A__							:   Optional[int]					       =		size if size is not None else {"""height""": 224, """width""": 224}
      A__							:   Optional[int]					       =		get_size_dict(A__      )
      A__							:   Tuple					       =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      A__							:   Dict					       =		get_size_dict(A__   ,	default_to_square=A__   ,	param_name="""crop_size"""      )

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






     def   __A      (			self   ,	A__   ,	A__   ,	A__ = PILImageResampling.BILINEAR   ,	A__ = None   ,	**A__   ,	):
      A__							:   str					       =		get_size_dict(A__      )

      if "shortest_edge" in size:
       A__							:   str					       =		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:
       A__							:   List[str]					       =		(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   ,	A__   ,	A__   ,	A__ = None   ,	**A__   ,	):
      A__							:   str					       =		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   ,	A__   ,	A__   ,	A__ = None   ,	**A__      ):
      return rescale(A__   ,	scale=A__   ,	data_format=A__   ,	**A__      )






     def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__ = None   ,	**A__   ,	):
      return normalize(A__   ,	mean=A__   ,	std=A__   ,	data_format=A__   ,	**A__      )






     def   __A      (			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__   ,	):
      A__							:   List[str]					       =		do_resize if do_resize is not None else self.do_resize
      A__							:   int					       =		do_rescale if do_rescale is not None else self.do_rescale
      A__							:   List[Any]					       =		do_normalize if do_normalize is not None else self.do_normalize
      A__							:   Union[str, Any]					       =		do_center_crop if do_center_crop is not None else self.do_center_crop
      A__							:   List[Any]					       =		crop_size if crop_size is not None else self.crop_size
      A__							:   Dict					       =		get_size_dict(A__   ,	param_name="""crop_size"""   ,	default_to_square=A__      )
      A__							:   Dict					       =		resample if resample is not None else self.resample
      A__							:   Dict					       =		rescale_factor if rescale_factor is not None else self.rescale_factor
      A__							:   Any					       =		image_mean if image_mean is not None else self.image_mean
      A__							:   Dict					       =		image_std if image_std is not None else self.image_std

      A__							:   int					       =		size if size is not None else self.size
      A__							:   Dict					       =		get_size_dict(A__      )

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

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

      if do_center_crop:
       A__							:   Union[str, Any]					       =		[self.center_crop(image=A__   ,	size=A__      ) for image in images]

      if do_rescale:
       A__							:   Optional[int]					       =		[self.rescale(image=A__   ,	scale=A__      ) for image in images]

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

      A__							:   Union[str, Any]					       =		[to_channel_dimension_format(A__   ,	A__      ) for image in images]

      A__							:   Optional[int]					       =		{"""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      )



