






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class    _SCREAMING_SNAKE_CASE							(		_UpperCAmelCase       ,_UpperCAmelCase       ,unittest.TestCase	):


			'''simple docstring'''





			__a							:							Any					     =					VQModel
			__a							:							Optional[int]					     =					"sample"






			@property
			def 				A      (   self							:						Any ,      lowercase							:						List[Any]=(3_2, 3_2)			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = 4
									UpperCamelCase__         = 3

									UpperCamelCase__         = floats_tensor((batch_size, num_channels) + sizes			).to(lowercase__			)

									return {"sample": image}






			@property
			def 				A      (   self							:						List[str]			)							->    Union[str, Any]:

									'''simple docstring'''







									return (3, 3_2, 3_2)






			@property
			def 				A      (   self							:						Any			)							->    Union[str, Any]:

									'''simple docstring'''







									return (3, 3_2, 3_2)






			def 				A      (   self							:						Optional[int]			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = {
									    "block_out_channels": [3_2, 6_4],
									    "in_channels": 3,
									    "out_channels": 3,
									    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
									    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
									    "latent_channels": 3,
									}
									UpperCamelCase__         = self.dummy_input
									return init_dict, inputs_dict






			def 				A      (   self							:						List[Any]			)							->    int:

									'''simple docstring'''







									pass






			def 				A      (   self							:						List[str]			)							->    Union[str, Any]:

									'''simple docstring'''







									pass






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = VQModel.from_pretrained("""fusing/vqgan-dummy""" ,      output_loading_info=lowercase__			)
									self.assertIsNotNone(lowercase__			)
									self.assertEqual(len(loading_info["""missing_keys"""]			) ,      0			)

									model.to(lowercase__			)
									UpperCamelCase__         = model(**self.dummy_input			)

									assert image is not None, "Make sure output is not None"







			def 				A      (   self							:						Union[str, Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = VQModel.from_pretrained("""fusing/vqgan-dummy"""			)
									model.to(lowercase__			).eval()

									torch.manual_seed(0			)
									if torch.cuda.is_available():
															torch.cuda.manual_seed_all(0			)

									UpperCamelCase__         = torch.randn(1 ,      model.config.in_channels ,      model.config.sample_size ,      model.config.sample_size			)
									UpperCamelCase__         = image.to(lowercase__			)
									with torch.no_grad():
															UpperCamelCase__         = model(lowercase__			).sample

									UpperCamelCase__         = output[0, -1, -3:, -3:].flatten().cpu()
									# fmt: off
									UpperCamelCase__         = torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]			)
									# fmt: on
									self.assertTrue(torch.allclose(lowercase__ ,      lowercase__ ,      atol=1e-3			)			)








'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_				: Optional[int]    	=							logging.get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[Any]					     =					["pixel_values"]






			def __init__(   self							:						str ,      lowercase							:						bool = True ,      lowercase							:						int = 3_2 ,      lowercase							:						List[Any]=PILImageResampling.BILINEAR ,      lowercase							:						bool = True ,      **lowercase							:						str ,      )							->    None:

									'''simple docstring'''







									UpperCamelCase__         = do_resize
									UpperCamelCase__         = do_rescale
									UpperCamelCase__         = size_divisor
									UpperCamelCase__         = resample
									super().__init__(**lowercase			)






			def 				A      (   self							:						Optional[int] ,      lowercase							:						np.ndarray ,      lowercase							:						int ,      lowercase							:						List[Any] ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						Any			)							->    np.ndarray:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = get_image_size(lowercase			)
									# Rounds the height and width down to the closest multiple of size_divisor
									UpperCamelCase__         = height // size_divisor * size_divisor
									UpperCamelCase__         = width // size_divisor * size_divisor
									UpperCamelCase__         = resize(lowercase ,      (new_h, new_w) ,      resample=lowercase ,      data_format=lowercase ,      **lowercase			)
									return image






			def 				A      (   self							:						int ,      lowercase							:						np.ndarray ,      lowercase							:						float ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						List[str]			)							->    np.ndarray:

									'''simple docstring'''







									return rescale(image=lowercase ,      scale=lowercase ,      data_format=lowercase ,      **lowercase			)







			def 				A      (   self							:						int ,      lowercase							:						Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[int] = None ,      lowercase							:						Optional[Any]=None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[Union[TensorType, str]] = None ,      lowercase							:						ChannelDimension = ChannelDimension.FIRST ,      **lowercase							:						str ,      )							->    BatchFeature:

									'''simple docstring'''







									UpperCamelCase__         = do_resize if do_resize is not None else self.do_resize
									UpperCamelCase__         = do_rescale if do_rescale is not None else self.do_rescale
									UpperCamelCase__         = size_divisor if size_divisor is not None else self.size_divisor
									UpperCamelCase__         = resample if resample is not None else self.resample

									if do_resize and size_divisor is None:
															raise ValueError("""size_divisor is required for resizing"""			)

									UpperCamelCase__         = make_list_of_images(lowercase			)

									if not valid_images(lowercase			):
															raise ValueError("""Invalid image(s)"""			)

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

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

									if do_rescale:
															UpperCamelCase__         = [self.rescale(lowercase ,      scale=1 / 2_5_5			) for image in images]

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

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