



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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
			import PIL

if is_torch_available():
			import torch


lowerCAmelCase__						:	Any					=logging.get_logger(__name__)

class   UpperCAmelCase_    (  lowerCAmelCase__			):

						'''simple docstring'''




						UpperCamelCase__     :					Optional[Any]							    =    ["""pixel_values"""]






						def __init__(       self ,				_A = True ,				_A = None ,				_A = PILImageResampling.BILINEAR ,				_A = True ,				_A = 1 / 255 ,				_A = True ,				_A = None ,				_A = True ,				**_A ,				):







													'''simple docstring'''

													super().__init__(**__a						)
													__SCREAMING_SNAKE_CASE										=   size if size is not None else {'shortest_edge': 224}
													__SCREAMING_SNAKE_CASE										=   get_size_dict(__a ,				default_to_square=__a						)
													__SCREAMING_SNAKE_CASE										=   crop_size if crop_size is not None else {'height': 256, 'width': 256}
													__SCREAMING_SNAKE_CASE										=   get_size_dict(__a ,				param_name='crop_size'						)

													__SCREAMING_SNAKE_CASE										=   do_resize
													__SCREAMING_SNAKE_CASE										=   size
													__SCREAMING_SNAKE_CASE										=   resample
													__SCREAMING_SNAKE_CASE										=   do_rescale
													__SCREAMING_SNAKE_CASE										=   rescale_factor
													__SCREAMING_SNAKE_CASE										=   do_center_crop
													__SCREAMING_SNAKE_CASE										=   crop_size
													__SCREAMING_SNAKE_CASE										=   do_flip_channel_order






						def 			_A      (       self ,				_A ,				_A ,				_A = PIL.Image.BILINEAR ,				_A = None ,				**_A ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   get_size_dict(__a ,				default_to_square=__a						)
													if "shortest_edge" not in size:
																				raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""						)
													__SCREAMING_SNAKE_CASE										=   get_resize_output_image_size(__a ,				size=size['shortest_edge'] ,				default_to_square=__a						)
													return resize(__a ,				size=__a ,				resample=__a ,				data_format=__a ,				**__a						)






						def 			_A      (       self ,				_A ,				_A ,				_A = None ,				**_A ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   get_size_dict(__a						)
													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()}"""						)
													return center_crop(__a ,				size=(size['height'], size['width']) ,				data_format=__a ,				**__a						)






						def 			_A      (       self ,				_A ,				_A ,				_A = None ,				**_A ,				):







													'''simple docstring'''

													return rescale(__a ,				scale=__a ,				data_format=__a ,				**__a						)






						def 			_A      (       self ,				_A ,				_A = None						):







													'''simple docstring'''

													return flip_channel_order(__a ,				data_format=__a						)






						def 			_A      (       self ,				_A ,				_A = None ,				_A = None ,				_A = None ,				_A = None ,				_A = None ,				_A = None ,				_A = None ,				_A = None ,				_A = None ,				_A = ChannelDimension.FIRST ,				**_A ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   do_resize if do_resize is not None else self.do_resize
													__SCREAMING_SNAKE_CASE										=   resample if resample is not None else self.resample
													__SCREAMING_SNAKE_CASE										=   do_rescale if do_rescale is not None else self.do_rescale
													__SCREAMING_SNAKE_CASE										=   rescale_factor if rescale_factor is not None else self.rescale_factor
													__SCREAMING_SNAKE_CASE										=   do_center_crop if do_center_crop is not None else self.do_center_crop
													__SCREAMING_SNAKE_CASE										=   (
													    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
													)

													__SCREAMING_SNAKE_CASE										=   size if size is not None else self.size
													__SCREAMING_SNAKE_CASE										=   get_size_dict(__a ,				default_to_square=__a						)
													__SCREAMING_SNAKE_CASE										=   crop_size if crop_size is not None else self.crop_size
													__SCREAMING_SNAKE_CASE										=   get_size_dict(__a ,				param_name='crop_size'						)

													__SCREAMING_SNAKE_CASE										=   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_resize and size is None:
																				raise ValueError('Size 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_center_crop and crop_size is None:
																				raise ValueError('Crop size must be specified if do_center_crop is True.'						)

													# All transformations expect numpy arrays.
													__SCREAMING_SNAKE_CASE										=   [to_numpy_array(__a						) for image in images]

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

													if do_center_crop:
																				__SCREAMING_SNAKE_CASE										=   [self.center_crop(image=__a ,				size=__a						) for image in images]

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

													# the pretrained checkpoints assume images are BGR, not RGB
													if do_flip_channel_order:
																				__SCREAMING_SNAKE_CASE										=   [self.flip_channel_order(image=__a						) for image in images]

													__SCREAMING_SNAKE_CASE										=   [to_channel_dimension_format(__a ,				__a						) for image in images]

													__SCREAMING_SNAKE_CASE										=   {'pixel_values': images}
													return BatchFeature(data=__a ,				tensor_type=__a						)






						def 			_A      (       self ,				_A ,				_A = None						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   outputs.logits

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

																				if is_torch_tensor(__a						):
																											__SCREAMING_SNAKE_CASE										=   target_sizes.numpy()

																				__SCREAMING_SNAKE_CASE										=   []

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

													return semantic_segmentation










"""simple docstring"""


from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 =""""""
							__UpperCAmelCase		:       Optional[int]							 ="""hf-legacy"""  # "hf://"" is reserved for hffs

							def __init__(	self       ,  __a = None       ,  __a = None       ,  **__a       ,  ):
										super().__init__(self       ,  **__a      )
										__lowerCAmelCase							       =     repo_info
										__lowerCAmelCase							       =     token
										__lowerCAmelCase							       =     None

							def      snake_case							(	self      ):
										if self.dir_cache is None:
													__lowerCAmelCase							       =     {}
													for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																__lowerCAmelCase							       =     {
																    "name": hf_file.rfilename,
																    "size": None,
																    "type": "file",
																}
																self.dir_cache.update(
																    {
																        str(__a      ): {"name": str(__a      ), "size": None, "type": "directory"}
																        for d in list(PurePosixPath(hf_file.rfilename      ).parents      )[:-1]
																    }      )

							def      snake_case							(	self       ,  __a       ,  __a = "rb"       ,  **__a       ,  ):
										if not isinstance(self.repo_info       ,  __a      ):
													raise NotImplementedError(f"Open is only implemented for dataset repositories, but got {self.repo_info}"      )
										__lowerCAmelCase							       =     hf_hub_url(self.repo_info.id       ,  __a       ,  revision=self.repo_info.sha      )
										return fsspec.open(
										    __a       ,  mode=__a       ,  headers=get_authentication_headers_for_url(__a       ,  use_auth_token=self.token      )       ,  client_kwargs={"trust_env": True}       ,  ).open()

							def      snake_case							(	self       ,  __a       ,  **__a      ):
										self._get_dirs()
										__lowerCAmelCase							       =     self._strip_protocol(__a      )
										if path in self.dir_cache:
													return self.dir_cache[path]
										else:
													raise FileNotFoundError(__a      )



							def      snake_case							(	self       ,  __a       ,  __a=False       ,  **__a      ):
										self._get_dirs()
										__lowerCAmelCase							       =     PurePosixPath(path.strip("/"      )      )
										__lowerCAmelCase							       =     {}
										for p, f in self.dir_cache.items():
													__lowerCAmelCase							       =     PurePosixPath(p.strip("/"      )      )
													__lowerCAmelCase							       =     p.parent
													if root == path:
																__lowerCAmelCase							       =     f
										__lowerCAmelCase							       =     list(paths.values()      )
										if detail:
													return out
										else:
													return sorted(f["name"] for f in out      )




