
import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor

class 		__magic_name__				( unittest.TestCase					):


  """simple docstring"""





  @property
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						1
    _lowerCamelCase      =						3
    _lowerCamelCase      =						8
    _lowerCamelCase      =						8

    _lowerCamelCase      =						torch.rand((batch_size, num_channels, height, width)		)

    return sample


  def     _UpperCAmelCase  (					self	,		a__		):





    if isinstance(image[0]	,		PIL.Image.Image		):
      return np.stack([np.array(__A		) for i in image]	,		axis=0		)
    elif isinstance(__A	,		torch.Tensor		):
      return image.cpu().numpy().transpose(0	,		2	,		3	,		1		)
    return image


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						VaeImageProcessor(do_resize=__A	,		do_normalize=__A		)

    _lowerCamelCase      =						self.dummy_sample
    _lowerCamelCase      =						self.to_np(__A		)

    for output_type in ["pt", "np", "pil"]:
      _lowerCamelCase      =						image_processor.postprocess(
          image_processor.preprocess(__A		)	,		output_type=__A	,		)
      _lowerCamelCase      =						self.to_np(__A		)
      _lowerCamelCase      =						(input_np * 2_55).round() if output_type == '''pil''' else input_np
      assert (
          np.abs(in_np - out_np		).max() < 1E-6
      ), f'''decoded output does not match input for output_type {output_type}'''


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						VaeImageProcessor(do_resize=__A	,		do_normalize=__A		)
    _lowerCamelCase      =						self.dummy_sample.cpu().numpy().transpose(0	,		2	,		3	,		1		)

    for output_type in ["pt", "np", "pil"]:
      _lowerCamelCase      =						image_processor.postprocess(image_processor.preprocess(__A		)	,		output_type=__A		)

      _lowerCamelCase      =						self.to_np(__A		)
      _lowerCamelCase      =						(input_np * 2_55).round() if output_type == '''pil''' else input_np
      assert (
          np.abs(in_np - out_np		).max() < 1E-6
      ), f'''decoded output does not match input for output_type {output_type}'''


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						VaeImageProcessor(do_resize=__A	,		do_normalize=__A		)

    _lowerCamelCase      =						self.dummy_sample.cpu().numpy().transpose(0	,		2	,		3	,		1		)
    _lowerCamelCase      =						image_processor.numpy_to_pil(__A		)

    for output_type in ["pt", "np", "pil"]:
      _lowerCamelCase      =						image_processor.postprocess(image_processor.preprocess(__A		)	,		output_type=__A		)
      for i, o in zip(__A	,		__A		):
        _lowerCamelCase      =						np.array(__A		)
        _lowerCamelCase      =						self.to_np(__A		) if output_type == '''pil''' else (self.to_np(__A		) * 2_55).round()
        assert (
            np.abs(in_np - out_np		).max() < 1E-6
        ), f'''decoded output does not match input for output_type {output_type}'''


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						VaeImageProcessor(do_resize=__A	,		do_normalize=__A		)

    _lowerCamelCase      =						self.dummy_sample
    _lowerCamelCase      =						input_pt_ad.squeeze(0		)

    _lowerCamelCase      =						image_processor.postprocess(
        image_processor.preprocess(__A		)	,		output_type='''np'''	,		)
    _lowerCamelCase      =						image_processor.postprocess(
        image_processor.preprocess(__A		)	,		output_type='''np'''	,		)

    _lowerCamelCase      =						self.to_np(self.dummy_sample		)
    _lowerCamelCase      =						input_np_ad.squeeze(0		)

    _lowerCamelCase      =						image_processor.postprocess(
        image_processor.preprocess(__A		)	,		output_type='''np'''	,		)
    _lowerCamelCase      =						image_processor.postprocess(
        image_processor.preprocess(__A		)	,		output_type='''np'''	,		)

    assert np.abs(out_pt_ad - out_pt_ad		).max() < 1E-6
    assert np.abs(out_np_ad - out_np_ad		).max() < 1E-6


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						VaeImageProcessor(do_resize=__A	,		do_normalize=__A		)

    _lowerCamelCase      =						self.dummy_sample
    _lowerCamelCase      =						list(__A		)

    _lowerCamelCase      =						image_processor.postprocess(
        image_processor.preprocess(__A		)	,		output_type='''np'''	,		)

    _lowerCamelCase      =						image_processor.postprocess(
        image_processor.preprocess(__A		)	,		output_type='''np'''	,		)

    _lowerCamelCase      =						self.to_np(self.dummy_sample		)
    list(__A		)

    _lowerCamelCase      =						image_processor.postprocess(
        image_processor.preprocess(__A		)	,		output_type='''np'''	,		)

    _lowerCamelCase      =						image_processor.postprocess(
        image_processor.preprocess(__A		)	,		output_type='''np'''	,		)

    assert np.abs(out_pt_ad - out_pt_list		).max() < 1E-6
    assert np.abs(out_np_ad - out_np_list		).max() < 1E-6









'''simple docstring'''







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 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	bool						) ->							torch.Tensor:
							# The first operation in a checkpoint can't be in-place, but it's
							# nice to have in-place addition during inference. Thus...
							if not inplace:
														lowerCAmelCase__  =   ma + ma
							else:
														ma += ma

							return ma
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	List[int]						) ->							torch.Tensor:
							lowerCAmelCase__  =   -1 * len(UpperCAmelCase_						)
							lowerCAmelCase__  =   list(range(len(tensor.shape[:zero_index]						)						)						)
							return tensor.permute(first_inds + [zero_index + i for i in inds]						)
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	int						) ->							torch.Tensor:
							return t.reshape(t.shape[:-no_dims] + (-1,)						)
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	float = 1E-4						) ->							torch.Tensor:
							lowerCAmelCase__  =   mask.expand(*value.shape						)
							return torch.sum(mask * value					, dim=UpperCAmelCase_						) / (eps + torch.sum(UpperCAmelCase_					, dim=UpperCAmelCase_						))
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.types.Number = 2.31_25					, UpperCAmelCase_      :	torch.types.Number = 21.68_75					, UpperCAmelCase_      :	int = 64						) ->							torch.Tensor:
							lowerCAmelCase__  =   torch.linspace(UpperCAmelCase_					, UpperCAmelCase_					, no_bins - 1					, device=pts.device						)
							lowerCAmelCase__  =   torch.sqrt(torch.sum((pts.unsqueeze(-2						) - pts.unsqueeze(-3						)) ** 2					, dim=-1						)						)
							return torch.bucketize(UpperCAmelCase_					, UpperCAmelCase_						)
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[list], Any]					, UpperCAmelCase_      :	List[dict]						) ->							dict:
							lowerCAmelCase__  =   dicts[0]
							lowerCAmelCase__  =   {}
							for k, v in first.items():
														lowerCAmelCase__  =   [d[k] for d in dicts]
														if isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
																					lowerCAmelCase__  =   dict_multimap(UpperCAmelCase_					, UpperCAmelCase_						)
														else:
																					lowerCAmelCase__  =   fn(UpperCAmelCase_						)

							return new_dict
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.Tensor						) ->							torch.Tensor:
							lowerCAmelCase__  =   v_bins.view(((1,) * len(x.shape						)) + (len(UpperCAmelCase_						),)						)
							lowerCAmelCase__  =   x[..., None] - reshaped_bins
							lowerCAmelCase__  =   torch.argmin(torch.abs(UpperCAmelCase_						)					, dim=-1						)
							return nn.functional.one_hot(UpperCAmelCase_					, num_classes=len(UpperCAmelCase_						)						).float()
def 			_lowerCAmelCase(      UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	torch.Tensor					, UpperCAmelCase_      :	int = 0					, UpperCAmelCase_      :	int = 0						) ->							torch.Tensor:
							lowerCAmelCase__  =   []
							for i, s in enumerate(data.shape[:no_batch_dims]						):
														lowerCAmelCase__  =   torch.arange(UpperCAmelCase_						)
														lowerCAmelCase__  =   r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape						) - i - 1)))						)
														ranges.append(UpperCAmelCase_						)

							lowerCAmelCase__  =   [slice(UpperCAmelCase_						) for _ in range(len(data.shape						) - no_batch_dims						)]
							lowerCAmelCase__  =   inds
							ranges.extend(UpperCAmelCase_						)
							# 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(UpperCAmelCase_						)]


_UpperCamelCase  						=			TypeVar("""T""")
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	Dict[Any, Union[dict, list, tuple, T]]					, UpperCAmelCase_      :	Type[T]						) ->							Dict[Any, Union[dict, list, tuple, Any]]:
							lowerCAmelCase__  =   {}
							for k, v in dic.items():
														if isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
																					lowerCAmelCase__  =   dict_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						)
														else:
																					lowerCAmelCase__  =   tree_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						)

							return new_dict
@overload
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	T					, UpperCAmelCase_      :	Type[T]						) ->							Any:
							...
@overload
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	dict					, UpperCAmelCase_      :	Type[T]						) ->							dict:
							...
@overload
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	list					, UpperCAmelCase_      :	Type[T]						) ->							list:
							...
@overload
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Callable[[T], Any]					, UpperCAmelCase_      :	tuple					, UpperCAmelCase_      :	Type[T]						) ->							tuple:
							...
def 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	Dict					, UpperCAmelCase_      :	Union[str, Any]						) ->							List[str]:
							if isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
														return dict_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						)
							elif isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
														return [tree_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						) for x in tree]
							elif isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
														return tuple(tree_map(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						) for x in tree						)
							elif isinstance(UpperCAmelCase_					, UpperCAmelCase_						):
														return fn(UpperCAmelCase_						)
							else:
														print(type(UpperCAmelCase_						)						)
														raise ValueError("""Not supported"""						)






_UpperCamelCase  						=			partial(tree_map, leaf_type=torch.Tensor)





