

'''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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


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



def   __lowerCAmelCase					(							lowerCamelCase   :						int					):






	'''simple docstring'''




	if isinstance(_A				,		(list, tuple)					) and isinstance(videos[0]				,		(list, tuple)					) and is_valid_image(videos[0][0]					):
		return videos

	elif isinstance(_A				,		(list, tuple)					) and is_valid_image(videos[0]					):
		return [videos]

	elif is_valid_image(_A					):
		return [[videos]]

	raise ValueError(f'''Could not make batched video from {videos}'''					)






class 					UpperCAmelCase__	(		_UpperCAmelCase ):
					a      :     List[str]        =							['pixel_values']



					def __init__(    self			,				UpperCamelCase = True			,				UpperCamelCase = None			,				UpperCamelCase = PILImageResampling.BILINEAR			,				UpperCamelCase = True			,				UpperCamelCase = None			,				UpperCamelCase = True			,				UpperCamelCase = 1 / 255			,				UpperCamelCase = True			,				UpperCamelCase = None			,				UpperCamelCase = None			,				**UpperCamelCase			,				)     ->			None:
						super().__init__(**__UpperCamelCase     )
						__lowerCAmelCase												=  size if size is not None else {"shortest_edge": 224}
						__lowerCAmelCase												=  get_size_dict(__UpperCamelCase			,				default_to_square=__UpperCamelCase     )
						__lowerCAmelCase												=  crop_size if crop_size is not None else {"height": 224, "width": 224}
						__lowerCAmelCase												=  get_size_dict(__UpperCamelCase			,				param_name="crop_size"     )

						__lowerCAmelCase												=  do_resize
						__lowerCAmelCase												=  size
						__lowerCAmelCase												=  do_center_crop
						__lowerCAmelCase												=  crop_size
						__lowerCAmelCase												=  resample
						__lowerCAmelCase												=  do_rescale
						__lowerCAmelCase												=  rescale_factor
						__lowerCAmelCase												=  do_normalize
						__lowerCAmelCase												=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase												=  image_std if image_std is not None else IMAGENET_STANDARD_STD



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = PILImageResampling.BILINEAR			,				UpperCamelCase = None			,				**UpperCamelCase			,				)     ->			np.ndarray:
						__lowerCAmelCase												=  get_size_dict(__UpperCamelCase			,				default_to_square=__UpperCamelCase     )
						if "shortest_edge" in size:
							__lowerCAmelCase												=  get_resize_output_image_size(__UpperCamelCase			,				size["shortest_edge"]			,				default_to_square=__UpperCamelCase     )
						elif "height" in size and "width" in size:
							__lowerCAmelCase												=  (size["height"], size["width"])
						else:
							raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''     )
						return resize(__UpperCamelCase			,				size=__UpperCamelCase			,				resample=__UpperCamelCase			,				data_format=__UpperCamelCase			,				**__UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				**UpperCamelCase			,				)     ->			np.ndarray:
						__lowerCAmelCase												=  get_size_dict(__UpperCamelCase     )
						if "height" not in size or "width" not in size:
							raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''     )
						return center_crop(__UpperCamelCase			,				size=(size["height"], size["width"])			,				data_format=__UpperCamelCase			,				**__UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				**UpperCamelCase			,				)     ->			int:
						return rescale(__UpperCamelCase			,				scale=__UpperCamelCase			,				data_format=__UpperCamelCase			,				**__UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase = None			,				**UpperCamelCase			,				)     ->			np.ndarray:
						return normalize(__UpperCamelCase			,				mean=__UpperCamelCase			,				std=__UpperCamelCase			,				data_format=__UpperCamelCase			,				**__UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = ChannelDimension.FIRST			,				)     ->			np.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_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."     )

						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."     )

						# All transformations expect numpy arrays.
						__lowerCAmelCase												=  to_numpy_array(__UpperCamelCase     )

						if do_resize:
							__lowerCAmelCase												=  self.resize(image=__UpperCamelCase			,				size=__UpperCamelCase			,				resample=__UpperCamelCase     )

						if do_center_crop:
							__lowerCAmelCase												=  self.center_crop(__UpperCamelCase			,				size=__UpperCamelCase     )

						if do_rescale:
							__lowerCAmelCase												=  self.rescale(image=__UpperCamelCase			,				scale=__UpperCamelCase     )

						if do_normalize:
							__lowerCAmelCase												=  self.normalize(image=__UpperCamelCase			,				mean=__UpperCamelCase			,				std=__UpperCamelCase     )

						__lowerCAmelCase												=  to_channel_dimension_format(__UpperCamelCase			,				__UpperCamelCase     )
						return image



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = ChannelDimension.FIRST			,				**UpperCamelCase			,				)     ->			PIL.Image.Image:
						__lowerCAmelCase												=  do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase												=  resample if resample is not None else self.resample
						__lowerCAmelCase												=  do_center_crop if do_center_crop is not None else self.do_center_crop
						__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_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase												=  image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase												=  image_std if image_std is not None else self.image_std

						__lowerCAmelCase												=  size if size is not None else self.size
						__lowerCAmelCase												=  get_size_dict(__UpperCamelCase			,				default_to_square=__UpperCamelCase     )
						__lowerCAmelCase												=  crop_size if crop_size is not None else self.crop_size
						__lowerCAmelCase												=  get_size_dict(__UpperCamelCase			,				param_name="crop_size"     )

						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."     )

						__lowerCAmelCase												=  make_batched(__UpperCamelCase     )

						__lowerCAmelCase												=  [
						    [
						        self._preprocess_image(
						            image=__UpperCamelCase			,				do_resize=__UpperCamelCase			,				size=__UpperCamelCase			,				resample=__UpperCamelCase			,				do_center_crop=__UpperCamelCase			,				crop_size=__UpperCamelCase			,				do_rescale=__UpperCamelCase			,				rescale_factor=__UpperCamelCase			,				do_normalize=__UpperCamelCase			,				image_mean=__UpperCamelCase			,				image_std=__UpperCamelCase			,				data_format=__UpperCamelCase			,				)
						        for img in video
						    ]
						    for video in videos
						]

						__lowerCAmelCase												=  {"pixel_values": videos}
						return BatchFeature(data=__UpperCamelCase			,				tensor_type=__UpperCamelCase     )




'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand



def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]					):






	'''simple docstring'''




	return DownloadCommand(args.model				,		args.cache_dir				,		args.force				,		args.trust_remote_code					)






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase     )     ->			Tuple:
						__lowerCAmelCase												=  parser.add_parser("download"     )
						download_parser.add_argument(
						    "--cache-dir"			,				type=UpperCamelCase			,				default=UpperCamelCase			,				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=UpperCamelCase			,				help="Name of the model to download"     )
						download_parser.set_defaults(func=UpperCamelCase     )



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			List[str]:
						__lowerCAmelCase												=  model
						__lowerCAmelCase												=  cache
						__lowerCAmelCase												=  force
						__lowerCAmelCase												=  trust_remote_code



					def        UpperCAmelCase_ (    self     )     ->			Any:
						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     )