




from argparse import ArgumentParser

from . import BaseTransformersCLICommand



def   __lowerCAmelCase      (       UpperCamelCase   )  ->						Union[str, Any]:
  return DownloadCommand(args.model						,   args.cache_dir						,   args.force						,   args.trust_remote_code   )






class 			_lowerCAmelCase  (	__UpperCamelCase						):

      @staticmethod
      def 						__magic_name__( __UpperCAmelCase		):
        lowerCAmelCase__					:	str									=  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		):
        lowerCAmelCase__					:	List[str]									=  model
        lowerCAmelCase__					:	Tuple									=  cache
        lowerCAmelCase__					:	Tuple									=  force
        lowerCAmelCase__					:	Optional[Any]									=  trust_remote_code







      def 						__magic_name__( 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		)








import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
    import torch

if is_vision_available():
    import PIL


_UpperCAmelCase							:			str        =							logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Tuple[int, int]:
    def constraint_to_multiple_of(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=0		,      _UpperCAmelCase=None					):
        lowerCamelCase__       : List[str]   					=						round(val / multiple					) * multiple

        if max_val is not None and x > max_val:
            lowerCamelCase__       : Optional[Any]   					=						math.floor(val / multiple					) * multiple

        if x < min_val:
            lowerCamelCase__       : List[Any]   					=						math.ceil(val / multiple					) * multiple

        return x

    lowerCamelCase__       : Dict   					=						(output_size, output_size) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) else output_size

    lowerCamelCase__					,				lowerCamelCase__       : str   					=						get_image_size(_UpperCAmelCase					)
    lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						output_size

    # determine new height and width
    lowerCamelCase__       : List[str]   					=						output_height / input_height
    lowerCamelCase__       : str   					=						output_width / input_width

    if keep_aspect_ratio:
        # scale as little as possible
        if abs(1 - scale_width					) < abs(1 - scale_height					):
            # fit width
            lowerCamelCase__       : Optional[int]   					=						scale_width
        else:
            # fit height
            lowerCamelCase__       : Tuple   					=						scale_height

    lowerCamelCase__       : str   					=						constraint_to_multiple_of(scale_height * input_height		,      multiple=_UpperCAmelCase					)
    lowerCamelCase__       : Any   					=						constraint_to_multiple_of(scale_width * input_width		,      multiple=_UpperCAmelCase					)

    return (new_height, new_width)


class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""pixel_values"""]
 def __init__( self    : Any     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BILINEAR     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Union[int, float] = 1 / 255     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	**UpperCAmelCase    : Optional[int]     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )
     lowerCamelCase__       : Optional[int]   					=						size if size is not None else {'height': 384, 'width': 384}
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						do_resize
     lowerCamelCase__       : List[str]   					=						size
     lowerCamelCase__       : List[Any]   					=						keep_aspect_ratio
     lowerCamelCase__       : Dict   					=						ensure_multiple_of
     lowerCamelCase__       : str   					=						resample
     lowerCamelCase__       : int   					=						do_rescale
     lowerCamelCase__       : Any   					=						rescale_factor
     lowerCamelCase__       : List[str]   					=						do_normalize
     lowerCamelCase__       : Tuple   					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__       : str   					=						image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 	A_      ( self    : int     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Optional[Any]     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Tuple   					=						get_size_dict(UpperCAmelCase      )
     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()}"""      )
     lowerCamelCase__       : List[Any]   					=						get_resize_output_image_size(
         UpperCAmelCase     ,	output_size=(size['height'], size['width'])     ,	keep_aspect_ratio=UpperCAmelCase     ,	multiple=UpperCAmelCase     ,	)
     return resize(UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[int, float]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Dict     ,	)     ->      str:
     return rescale(UpperCAmelCase     ,	scale=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : List[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Tuple     ,	)     ->      np.ndarray:
     return normalize(UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : float = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[str, TensorType]] = None     ,	UpperCAmelCase    : ChannelDimension = ChannelDimension.FIRST     ,	**UpperCAmelCase    : Union[str, Any]     ,	)     ->      PIL.Image.Image:
     lowerCamelCase__       : int   					=						do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__       : Optional[Any]   					=						size if size is not None else self.size
     lowerCamelCase__       : List[str]   					=						get_size_dict(UpperCAmelCase      )
     lowerCamelCase__       : int   					=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     lowerCamelCase__       : Dict   					=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     lowerCamelCase__       : List[str]   					=						resample if resample is not None else self.resample
     lowerCamelCase__       : Optional[int]   					=						do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__       : List[Any]   					=						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__       : Any   					=						do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__       : List[Any]   					=						image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__       : int   					=						image_std if image_std is not None else self.image_std

     lowerCamelCase__       : Any   					=						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_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.'      )

     # All transformations expect numpy arrays.
     lowerCamelCase__       : Dict   					=						[to_numpy_array(UpperCAmelCase      ) for image in images]

     if do_resize:
         lowerCamelCase__       : Dict   					=						[self.resize(image=UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase      ) for image in images]

     if do_rescale:
         lowerCamelCase__       : Tuple   					=						[self.rescale(image=UpperCAmelCase     ,	scale=UpperCAmelCase      ) for image in images]

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

     lowerCamelCase__       : Optional[Any]   					=						[to_channel_dimension_format(UpperCAmelCase     ,	UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Any   					=						{'pixel_values': images}
     return BatchFeature(data=UpperCAmelCase     ,	tensor_type=UpperCAmelCase      )







 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : List[Tuple] = None      )     ->      Dict:
     lowerCamelCase__       : Tuple   					=						outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
         if len(UpperCAmelCase      ) != len(UpperCAmelCase      ):
             raise ValueError(
                 'Make sure that you pass in as many target sizes as the batch dimension of the logits'      )

         if is_torch_tensor(UpperCAmelCase      ):
             lowerCamelCase__       : List[str]   					=						target_sizes.numpy()

         lowerCamelCase__       : Tuple   					=						[]

         for idx in range(len(UpperCAmelCase      )      ):
             lowerCamelCase__       : Any   					=						torch.nn.functional.interpolate(
                 logits[idx].unsqueeze(dim=0      )     ,	size=target_sizes[idx]     ,	mode='bilinear'     ,	align_corners=UpperCAmelCase      )
             lowerCamelCase__       : Any   					=						resized_logits[0].argmax(dim=0      )
             semantic_segmentation.append(UpperCAmelCase      )
     else:
         lowerCamelCase__       : Dict   					=						logits.argmax(dim=1      )
         lowerCamelCase__       : Tuple   					=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

     return semantic_segmentation





