
from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  if not inplace:
    __magic_name__    					=	ma + ma
  else:
    ma += ma

  return ma

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	-1 * len(A_ )
  __magic_name__    					=	list(range(len(tensor.shape[:zero_index] ) ) )
  return tensor.permute(first_inds + [zero_index + i for i in inds] )

def a__   (	A_,					A_ ):






  '''simple docstring'''





  return t.reshape(t.shape[:-no_dims] + (-1,) )

def a__   (	A_,					A_,					A_,					A_ = 1e-4 ):






  '''simple docstring'''





  __magic_name__    					=	mask.expand(*value.shape )
  return torch.sum(mask * value,					dim=A_ ) / (eps + torch.sum(A_,					dim=A_ ))

def a__   (	A_,					A_ = 2.3125,					A_ = 21.6875,					A_ = 64 ):






  '''simple docstring'''





  __magic_name__    					=	torch.linspace(A_,					A_,					no_bins - 1,					device=pts.device )
  __magic_name__    					=	torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2,					dim=-1 ) )
  return torch.bucketize(A_,					A_ )

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	dicts[0]
  __magic_name__    					=	{}
  for k, v in first.items():
    __magic_name__    					=	[d[k] for d in dicts]
    if isinstance(A_,					A_ ):
      __magic_name__    					=	dict_multimap(A_,					A_ )
    else:
      __magic_name__    					=	fn(A_ )

  return new_dict

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	v_bins.view(((1,) * len(x.shape )) + (len(A_ ),) )
  __magic_name__    					=	x[..., None] - reshaped_bins
  __magic_name__    					=	torch.argmin(torch.abs(A_ ),					dim=-1 )
  return nn.functional.one_hot(A_,					num_classes=len(A_ ) ).float()

def a__   (	A_,					A_,					A_ = 0,					A_ = 0 ):






  '''simple docstring'''





  __magic_name__    					=	[]
  for i, s in enumerate(data.shape[:no_batch_dims] ):
    __magic_name__    					=	torch.arange(A_ )
    __magic_name__    					=	r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
    ranges.append(A_ )

  __magic_name__    					=	[slice(A_ ) for _ in range(len(data.shape ) - no_batch_dims )]
  __magic_name__    					=	inds
  ranges.extend(A_ )
  # Matt note: Editing this to get around the behaviour of using a list as an array index changing
  # in recent Numpy versions
  return data[tuple(A_ )]


__lowerCAmelCase      :					List[Any]   	=       TypeVar('T')

def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	{}
  for k, v in dic.items():
    if isinstance(A_,					A_ ):
      __magic_name__    					=	dict_map(A_,					A_,					A_ )
    else:
      __magic_name__    					=	tree_map(A_,					A_,					A_ )

  return new_dict

@overload
def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  ...

@overload
def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  ...

@overload
def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  ...

@overload
def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  ...

def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  if isinstance(A_,					A_ ):
    return dict_map(A_,					A_,					A_ )
  elif isinstance(A_,					A_ ):
    return [tree_map(A_,					A_,					A_ ) for x in tree]
  elif isinstance(A_,					A_ ):
    return tuple(tree_map(A_,					A_,					A_ ) for x in tree )
  elif isinstance(A_,					A_ ):
    return fn(A_ )
  else:
    print(type(A_ ) )
    raise ValueError("""Not supported""" )





__lowerCAmelCase      :					List[str]   	=       partial(tree_map, leaf_type=torch.Tensor)



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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase      :					str   	=       logging.get_logger(__name__)




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""pixel_values"""]
  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Dict[str, int]] = None      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							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[Any]      ,							)      ->     None:







    """simple docstring"""






    super().__init__(**UpperCamelCase__   )
    __magic_name__    					=	size if size is not None else {"""height""": 224, """width""": 224}
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )
    __magic_name__    					=	do_resize
    __magic_name__    					=	do_rescale
    __magic_name__    					=	do_normalize
    __magic_name__    					=	size
    __magic_name__    					=	resample
    __magic_name__    					=	rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __magic_name__    					=	image_std if image_std is not None else IMAGENET_STANDARD_STD
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			Dict[str, int]      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Optional[int]      ,							)      ->     np.ndarray:







    """simple docstring"""






    __magic_name__    					=	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()}'''   )
    return resize(
        UpperCamelCase__      ,							size=(size["""height"""], size["""width"""])      ,							resample=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			float      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Union[str, Any]   )      ->     np.ndarray:







    """simple docstring"""






    return rescale(UpperCamelCase__      ,							scale=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			Union[float, List[float]]      ,							UpperCamelCase__      :			Union[float, List[float]]      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     np.ndarray:







    """simple docstring"""






    return normalize(UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )





  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			ImageInput      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Dict[str, int] = None      ,							UpperCamelCase__      :			PILImageResampling = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[float] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__      :			Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**UpperCamelCase__      :			List[str]      ,							)      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	do_resize if do_resize is not None else self.do_resize
    __magic_name__    					=	do_rescale if do_rescale is not None else self.do_rescale
    __magic_name__    					=	do_normalize if do_normalize is not None else self.do_normalize
    __magic_name__    					=	resample if resample is not None else self.resample
    __magic_name__    					=	rescale_factor if rescale_factor is not None else self.rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else self.image_mean
    __magic_name__    					=	image_std if image_std is not None else self.image_std

    __magic_name__    					=	size if size is not None else self.size
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )

    __magic_name__    					=	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_rescale and rescale_factor is None:
      raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

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

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

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

    if do_normalize:
      __magic_name__    					=	[self.normalize(image=UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__   ) for image in images]

    __magic_name__    					=	[to_channel_dimension_format(UpperCamelCase__      ,							UpperCamelCase__   ) for image in images]

    __magic_name__    					=	{"""pixel_values""": images}
    return BatchFeature(data=UpperCamelCase__      ,							tensor_type=UpperCamelCase__   )

