



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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowerCamelCase						    =  logging.get_logger(__name__)


if is_vision_available():
      import PIL

class       _SCREAMING_SNAKE_CASE	(UpperCAmelCase__ ):
     lowerCAmelCase					=			['pixel_values']





     def __init__(       self :							int     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							PILImageResampling = PILImageResampling.BICUBIC     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Union[int, float] = 1 / 2_5_5     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = None     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = None     ,  UpperCamelCase :							bool = True     ,  **UpperCamelCase :							List[Any]     ,  )->None:
      super().__init__(**UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	size if size is not None else {"""shortest_edge""": 2_2_4}
      __SCREAMING_SNAKE_CASE       :    Tuple       =	get_size_dict(UpperCamelCase     ,  default_to_square=UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	get_size_dict(UpperCamelCase     ,  default_to_square=UpperCamelCase     ,  param_name="crop_size"				)

      __SCREAMING_SNAKE_CASE       :    int       =	do_resize
      __SCREAMING_SNAKE_CASE       :    int       =	size
      __SCREAMING_SNAKE_CASE       :    Any       =	resample
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	do_center_crop
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	crop_size
      __SCREAMING_SNAKE_CASE       :    Dict       =	do_rescale
      __SCREAMING_SNAKE_CASE       :    int       =	rescale_factor
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	do_normalize
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      __SCREAMING_SNAKE_CASE       :    str       =	image_std if image_std is not None else OPENAI_CLIP_STD
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	do_convert_rgb





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							Dict[str, int]     ,  UpperCamelCase :							PILImageResampling = PILImageResampling.BICUBIC     ,  UpperCamelCase :							Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase :							List[Any]     ,  )->np.ndarray:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	get_size_dict(UpperCamelCase     ,  default_to_square=UpperCamelCase				)
      if "shortest_edge" not in size:
       raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	get_resize_output_image_size(UpperCamelCase     ,  size=size["shortest_edge"]     ,  default_to_square=UpperCamelCase				)
      return resize(UpperCamelCase     ,  size=UpperCamelCase     ,  resample=UpperCamelCase     ,  data_format=UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							Dict[str, int]     ,  UpperCamelCase :							Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase :							int     ,  )->np.ndarray:
      __SCREAMING_SNAKE_CASE       :    int       =	get_size_dict(UpperCamelCase				)
      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(UpperCamelCase     ,  size=(size["height"], size["width"])     ,  data_format=UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							Union[int, float]     ,  UpperCamelCase :							Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase :							Optional[int]     ,  )->Optional[Any]:
      return rescale(UpperCamelCase     ,  scale=UpperCamelCase     ,  data_format=UpperCamelCase     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Any     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							Union[float, List[float]]     ,  UpperCamelCase :							Union[float, List[float]]     ,  UpperCamelCase :							Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase :							str     ,  )->np.ndarray:
      return normalize(UpperCamelCase     ,  mean=UpperCamelCase     ,  std=UpperCamelCase     ,  data_format=UpperCamelCase     ,  **UpperCamelCase				)






     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							ImageInput     ,  UpperCamelCase :							bool = None     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							PILImageResampling = None     ,  UpperCamelCase :							bool = None     ,  UpperCamelCase :							int = 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 :							bool = None     ,  UpperCamelCase :							Optional[Union[str, TensorType]] = None     ,  UpperCamelCase :							Optional[ChannelDimension] = ChannelDimension.FIRST     ,  **UpperCamelCase :							Optional[int]     ,  )->PIL.Image.Image:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE       :    Tuple       =	size if size is not None else self.size
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	get_size_dict(UpperCamelCase     ,  param_name="size"     ,  default_to_square=UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	resample if resample is not None else self.resample
      __SCREAMING_SNAKE_CASE       :    Dict       =	do_center_crop if do_center_crop is not None else self.do_center_crop
      __SCREAMING_SNAKE_CASE       :    Any       =	crop_size if crop_size is not None else self.crop_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	get_size_dict(UpperCamelCase     ,  param_name="crop_size"     ,  default_to_square=UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_rescale if do_rescale is not None else self.do_rescale
      __SCREAMING_SNAKE_CASE       :    str       =	rescale_factor if rescale_factor is not None else self.rescale_factor
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE       :    Tuple       =	image_mean if image_mean is not None else self.image_mean
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_std if image_std is not None else self.image_std
      __SCREAMING_SNAKE_CASE       :    Dict       =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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:
       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."				)

      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."				)

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	[convert_to_rgb(UpperCamelCase				) for image in images]

      # All transformations expect numpy arrays.
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	[to_numpy_array(UpperCamelCase				) for image in images]

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

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

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

      if do_normalize:
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[self.normalize(image=UpperCamelCase     ,  mean=UpperCamelCase     ,  std=UpperCamelCase				) for image in images]

      __SCREAMING_SNAKE_CASE       :    Tuple       =	[to_channel_dimension_format(UpperCamelCase     ,  UpperCamelCase				) for image in images]

      __SCREAMING_SNAKE_CASE       :    Tuple       =	{"""pixel_values""": images}
      return BatchFeature(data=UpperCamelCase     ,  tensor_type=UpperCamelCase				)










import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_lowerCamelCase						    =  importlib.util.find_spec("""s3fs""") is not None

if _has_safs:
      from .safilesystem import SaFileSystem  # noqa: F401

_lowerCamelCase						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
      if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
            warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
      fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 if "://" in dataset_path:
  __SCREAMING_SNAKE_CASE       :    Any       =	dataset_path.split("://"     )[1]
 return dataset_path






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem     ):



 """simple docstring"""





 if fs is not None and fs.protocol != "file":
  return True
 else:
  return False






def 	_lowerCAmelCase						(				__lowerCamelCase   :     fsspec.AbstractFileSystem		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	not is_remote_filesystem(__lowerCamelCase     )
 if is_local:
  # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
  shutil.move(fs._strip_protocol(__lowerCamelCase     )		,	fs._strip_protocol(__lowerCamelCase     )     )
 else:
  fs.mv(__lowerCamelCase		,	__lowerCamelCase		,	recursive=__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 if hasattr(fsspec.asyn		,	"reset_lock"     ):
  # for future fsspec>2022.05.0
  fsspec.asyn.reset_lock()
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	None
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None
  __SCREAMING_SNAKE_CASE       :    Dict       =	threading.Lock()






