



'''simple docstring'''






import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor



class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		str     	=					1
        _UpperCAmelCase    :		Dict     	=					3
        _UpperCAmelCase    :		Dict     	=					8
        _UpperCAmelCase    :		Dict     	=					8

        _UpperCAmelCase    :		List[Any]     	=					torch.rand((batch_size, num_channels, height, width)				)

        return sample







 def _snake_case					(				self    ,a_				)						->  Optional[int]:
        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 _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Union[str, Any]     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)

        _UpperCAmelCase    :		Optional[Any]     	=					self.dummy_sample
        _UpperCAmelCase    :		List[Any]     	=					self.to_np(a_				)

        for output_type in ["pt", "np", "pil"]:
               _UpperCAmelCase    :		Tuple     	=					image_processor.postprocess(
                   image_processor.preprocess(a_				)    ,output_type=a_    ,)
               _UpperCAmelCase    :		List[Any]     	=					self.to_np(a_				)
               _UpperCAmelCase    :		Union[str, Any]     	=					(input_np * 255).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 _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		Any     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)
        _UpperCAmelCase    :		Tuple     	=					self.dummy_sample.cpu().numpy().transpose(0    ,2    ,3    ,1				)

        for output_type in ["pt", "np", "pil"]:
               _UpperCAmelCase    :		str     	=					image_processor.postprocess(image_processor.preprocess(a_				)    ,output_type=a_				)

               _UpperCAmelCase    :		List[str]     	=					self.to_np(a_				)
               _UpperCAmelCase    :		Optional[Any]     	=					(input_np * 255).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 _snake_case					(				self				)						->  Union[str, Any]:
        _UpperCAmelCase    :		Dict     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)

        _UpperCAmelCase    :		int     	=					self.dummy_sample.cpu().numpy().transpose(0    ,2    ,3    ,1				)
        _UpperCAmelCase    :		Any     	=					image_processor.numpy_to_pil(a_				)

        for output_type in ["pt", "np", "pil"]:
               _UpperCAmelCase    :		Dict     	=					image_processor.postprocess(image_processor.preprocess(a_				)    ,output_type=a_				)
               for i, o in zip(a_    ,a_				):
                      _UpperCAmelCase    :		List[Any]     	=					np.array(a_				)
                      _UpperCAmelCase    :		int     	=					self.to_np(a_				) if output_type == """pil""" else (self.to_np(a_				) * 255).round()
                      assert (
                          np.abs(in_np - out_np				).max() < 1E-6
                      ), f'''decoded output does not match input for output_type {output_type}'''







 def _snake_case					(				self				)						->  Union[str, Any]:
        _UpperCAmelCase    :		List[Any]     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)

        _UpperCAmelCase    :		Optional[Any]     	=					self.dummy_sample
        _UpperCAmelCase    :		Union[str, Any]     	=					input_pt_ad.squeeze(0				)

        _UpperCAmelCase    :		Any     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)
        _UpperCAmelCase    :		Any     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[int]     	=					self.to_np(self.dummy_sample				)
        _UpperCAmelCase    :		Tuple     	=					input_np_ad.squeeze(0				)

        _UpperCAmelCase    :		Dict     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)
        _UpperCAmelCase    :		List[str]     	=					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 _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Tuple     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)

        _UpperCAmelCase    :		Dict     	=					self.dummy_sample
        _UpperCAmelCase    :		int     	=					list(a_				)

        _UpperCAmelCase    :		Tuple     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[int]     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[int]     	=					self.to_np(self.dummy_sample				)
        list(a_				)

        _UpperCAmelCase    :		str     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		List[Any]     	=					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











from __future__ import annotations

from typing import Any
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : Any			,		UpperCAmelCase__ : Any						):  # noqa: A002

    """simple docstring"""






    lowercase__    :						Optional[int]       =			item
    lowercase__    :						Union[str, Any]       =			next
class 							__snake_case:



 def __init__(						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						Node | None       =			None
    lowercase__    :						Optional[Any]       =			0



 def     UpperCAmelCase     (						self : List[Any]			,		UpperCAmelCase__ : Any						):

    """simple docstring"""






    lowercase__    :						Any       =			Node(UpperCAmelCase__			,		self.head						)
    self.size += 1



 def     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






    if self.head is None:
       return None
    else:
       lowercase__    :						List[Any]       =			self.head.item
       lowercase__    :						Optional[Any]       =			self.head.next
       self.size -= 1
       return item



 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    return self.head is None



 def __str__(						self : List[str]						):

    """simple docstring"""






    if self.is_empty():
       return ""
    else:
       lowercase__    :						Optional[Any]       =			self.head
       lowercase__    :						str       =			""""""
       lowercase__    :						list[str]       =			[]
       while iterate:
          item_list.append(str(iterate.item						)						)
          lowercase__    :						List[str]       =			iterate.next

       lowercase__    :						List[str]       =			""" --> """.join(UpperCAmelCase__						)

       return item_str







 def __len__(						self : List[Any]						):

    """simple docstring"""






    return self.size


