'''simple docstring'''




import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						Union[str, Any]      			=		CONFIG_NAME




      @register_to_config
      def __init__( self		:       str		,      A		:       bool = True		,      A		:       int = 8		,      A		:       str = "lanczos"		,      A		:       bool = True		,      A		:       bool = False		,      ):
             super().__init__()




      @staticmethod
      def   _A			( A		:       np.ndarray ):
             if images.ndim == 3:
                    _UpperCAmelCase  :						List[str]							   =					images[None, ...]
             _UpperCAmelCase  :						Union[str, Any]							   =					(images * 255).round().astype("uint8" )
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    _UpperCAmelCase  :						Optional[int]							   =					[Image.fromarray(image.squeeze()		,      mode="L" ) for image in images]
             else:
                    _UpperCAmelCase  :						Dict							   =					[Image.fromarray(A ) for image in images]

             return pil_images




      @staticmethod
      def   _A			( A		:       Union[List[PIL.Image.Image], PIL.Image.Image] ):
             if not isinstance(A		,      A ):
                    _UpperCAmelCase  :						List[str]							   =					[images]
             _UpperCAmelCase  :						int							   =					[np.array(A ).astype(np.floataa ) / 255.0 for image in images]
             _UpperCAmelCase  :						List[Any]							   =					np.stack(A		,      axis=0 )

             return images




      @staticmethod
      def   _A			( A		:       np.ndarray ):
             if images.ndim == 3:
                    _UpperCAmelCase  :						str							   =					images[..., None]

             _UpperCAmelCase  :						Any							   =					torch.from_numpy(images.transpose(0		,      3		,      1		,      2 ) )
             return images




      @staticmethod
      def   _A			( A		:       torch.FloatTensor ):
             _UpperCAmelCase  :						str							   =					images.cpu().permute(0		,      2		,      3		,      1 ).float().numpy()
             return images




      @staticmethod
      def   _A			( A		:       Tuple ):
             return 2.0 * images - 1.0




      @staticmethod
      def   _A			( A		:       Any ):
             return (images / 2 + 0.5).clamp(0		,      1 )




      @staticmethod
      def   _A			( A		:       PIL.Image.Image ):
             _UpperCAmelCase  :						List[str]							   =					image.convert("RGB" )
             return image




      def   _A			( self		:       Tuple		,      A		:       PIL.Image.Image		,      A		:       Optional[int] = None		,      A		:       Optional[int] = None		,      ):
             if height is None:
                    _UpperCAmelCase  :						Dict							   =					image.height
             if width is None:
                    _UpperCAmelCase  :						Tuple							   =					image.width

             _UpperCAmelCase   ,						_UpperCAmelCase  :						int							   =					(
                 x - x % self.config.vae_scale_factor for x in (width, height)
             )  # resize to integer multiple of vae_scale_factor
             _UpperCAmelCase  :						List[str]							   =					image.resize((width, height)		,      resample=PIL_INTERPOLATION[self.config.resample] )
             return image




      def   _A			( self		:       str		,      A		:       Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]		,      A		:       Optional[int] = None		,      A		:       Optional[int] = None		,      ):
             _UpperCAmelCase  :						Any							   =					(PIL.Image.Image, np.ndarray, torch.Tensor)
             if isinstance(A		,      A ):
                    _UpperCAmelCase  :						Any							   =					[image]
             elif not (isinstance(A		,      A ) and all(isinstance(A		,      A ) for i in image )):
                    raise ValueError(
                        F"""Input is in incorrect format: {[type(A ) for i in image]}. Currently, we only support {', '.join(A )}""" )

             if isinstance(image[0]		,      PIL.Image.Image ):
                    if self.config.do_convert_rgb:
                           _UpperCAmelCase  :						str							   =					[self.convert_to_rgb(A ) for i in image]
                    if self.config.do_resize:
                           _UpperCAmelCase  :						Optional[int]							   =					[self.resize(A		,      A		,      A ) for i in image]
                    _UpperCAmelCase  :						List[Any]							   =					self.pil_to_numpy(A )  # to np
                    _UpperCAmelCase  :						int							   =					self.numpy_to_pt(A )  # to pt

             elif isinstance(image[0]		,      np.ndarray ):
                    _UpperCAmelCase  :						Optional[Any]							   =					np.concatenate(A		,      axis=0 ) if image[0].ndim == 4 else np.stack(A		,      axis=0 )
                    _UpperCAmelCase  :						str							   =					self.numpy_to_pt(A )
                    _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					image.shape
                    if self.config.do_resize and (
                        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                    ):
                           raise ValueError(
                               F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                               F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""" )

             elif isinstance(image[0]		,      torch.Tensor ):
                    _UpperCAmelCase  :						Dict							   =					torch.cat(A		,      axis=0 ) if image[0].ndim == 4 else torch.stack(A		,      axis=0 )
                    _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					image.shape

                    # don't need any preprocess if the image is latents
                    if channel == 4:
                           return image

                    if self.config.do_resize and (
                        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                    ):
                           raise ValueError(
                               F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                               F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""" )

        # expected range [0,1], normalize to [-1,1]
             _UpperCAmelCase  :						str							   =					self.config.do_normalize
             if image.min() < 0:
                    warnings.warn(
                        "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
                        F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""		,      A		,      )
                    _UpperCAmelCase  :						Tuple							   =					False

             if do_normalize:
                    _UpperCAmelCase  :						List[Any]							   =					self.normalize(A )

             return image





      def   _A			( self		:       Union[str, Any]		,      A		:       torch.FloatTensor		,      A		:       str = "pil"		,      A		:       Optional[List[bool]] = None		,      ):
             if not isinstance(A		,      torch.Tensor ):
                    raise ValueError(
                        F"""Input for postprocessing is in incorrect format: {type(A )}. We only support pytorch tensor""" )
             if output_type not in ["latent", "pt", "np", "pil"]:
                    _UpperCAmelCase  :						Optional[int]							   =					(
                        F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                        "`pil`, `np`, `pt`, `latent`"
                    )
                    deprecate("Unsupported output_type"		,      "1.0.0"		,      A		,      standard_warn=A )
                    _UpperCAmelCase  :						str							   =					"np"

             if output_type == "latent":
                    return image

             if do_denormalize is None:
                    _UpperCAmelCase  :						str							   =					[self.config.do_normalize] * image.shape[0]

             _UpperCAmelCase  :						Optional[int]							   =					torch.stack(
                 [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

             if output_type == "pt":
                    return image

             _UpperCAmelCase  :						Any							   =					self.pt_to_numpy(A )

             if output_type == "np":
                    return image

             if output_type == "pil":
                    return self.numpy_to_pil(A )

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						str      			=		CONFIG_NAME




      @register_to_config
      def __init__( self		:       List[str]		,      A		:       bool = True		,      A		:       int = 8		,      A		:       str = "lanczos"		,      A		:       bool = True		,      ):
             super().__init__()




      @staticmethod
      def   _A			( A		:       Optional[int] ):
             if images.ndim == 3:
                    _UpperCAmelCase  :						List[str]							   =					images[None, ...]
             _UpperCAmelCase  :						Tuple							   =					(images * 255).round().astype("uint8" )
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    _UpperCAmelCase  :						Optional[int]							   =					[Image.fromarray(image.squeeze()		,      mode="L" ) for image in images]
             else:
                    _UpperCAmelCase  :						str							   =					[Image.fromarray(image[:, :, :3] ) for image in images]

             return pil_images




      @staticmethod
      def   _A			( A		:       List[Any] ):
             return image[:, :, 1] * 2**8 + image[:, :, 2]




      def   _A			( self		:       List[Any]		,      A		:       Any ):
             if images.ndim == 3:
                    _UpperCAmelCase  :						Optional[Any]							   =					images[None, ...]
             _UpperCAmelCase  :						str							   =					images[:, :, :, 3:]
             if images.shape[-1] == 6:
                    _UpperCAmelCase  :						Optional[int]							   =					(images_depth * 255).round().astype("uint8" )
                    _UpperCAmelCase  :						Union[str, Any]							   =					[
                        Image.fromarray(self.rgblike_to_depthmap(A )		,      mode="I;16" ) for image_depth in images_depth
                    ]
             elif images.shape[-1] == 4:
                    _UpperCAmelCase  :						Union[str, Any]							   =					(images_depth * 65_535.0).astype(np.uintaa )
                    _UpperCAmelCase  :						Any							   =					[Image.fromarray(A		,      mode="I;16" ) for image_depth in images_depth]
             else:
                    raise Exception("Not supported" )

             return pil_images





      def   _A			( self		:       List[str]		,      A		:       torch.FloatTensor		,      A		:       str = "pil"		,      A		:       Optional[List[bool]] = None		,      ):
             if not isinstance(A		,      torch.Tensor ):
                    raise ValueError(
                        F"""Input for postprocessing is in incorrect format: {type(A )}. We only support pytorch tensor""" )
             if output_type not in ["latent", "pt", "np", "pil"]:
                    _UpperCAmelCase  :						Dict							   =					(
                        F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                        "`pil`, `np`, `pt`, `latent`"
                    )
                    deprecate("Unsupported output_type"		,      "1.0.0"		,      A		,      standard_warn=A )
                    _UpperCAmelCase  :						Optional[Any]							   =					"np"

             if do_denormalize is None:
                    _UpperCAmelCase  :						List[Any]							   =					[self.config.do_normalize] * image.shape[0]

             _UpperCAmelCase  :						List[Any]							   =					torch.stack(
                 [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

             _UpperCAmelCase  :						List[str]							   =					self.pt_to_numpy(A )

             if output_type == "np":
                    if image.shape[-1] == 6:
                           _UpperCAmelCase  :						Tuple							   =					np.stack([self.rgblike_to_depthmap(im[:, :, 3:] ) for im in image]		,      axis=0 )
                    else:
                           _UpperCAmelCase  :						Tuple							   =					image[:, :, :, 3:]
                    return image[:, :, :, :3], image_depth

             if output_type == "pil":
                    return self.numpy_to_pil(A ), self.numpy_to_depth(A )
             else:
                    raise Exception(F"""This type {output_type} is not supported""" )


'''simple docstring'''




import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[str]					)					->					Union[str, Any]:


       """simple docstring"""







       return EnvironmentCommand()


class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      @staticmethod
      def   _A			( A		:       ArgumentParser ):
             _UpperCAmelCase  :						List[str]							   =					parser.add_parser("env"		,      help="Print relevant system environment info." )
             download_parser.set_defaults(func=A )




      def   _A			( self		:       List[str] ):
             _UpperCAmelCase  :						str							   =					{
                 "`datasets` version": version,
                 "Platform": platform.platform(),
                 "Python version": platform.python_version(),
                 "Huggingface_hub version": huggingface_hub.__version__,
                 "PyArrow version": pyarrow.__version__,
                 "Pandas version": pandas.__version__,
             }

             print("\nCopy-and-paste the text below in your GitHub issue.\n" )
             print(self.format_dict(A ) )

             return info





      @staticmethod
      def   _A			( A		:       Tuple ):
             return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"

