
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     (   lowercase		):
							lowerCamelCase__:				Optional[int]      =							CONFIG_NAME



							@register_to_config
							def __init__(     self	,				__snake_case = True	,				__snake_case = 8	,				__snake_case = "lanczos"	,				__snake_case = True	,				__snake_case = False	,				)						->     Tuple:






								"""simple docstring"""



								super().__init__()



							@staticmethod
							def     A__ (     __snake_case							)						->     PIL.Image.Image:






								"""simple docstring"""



								if images.ndim == 3:
									UpperCAmelCase:					str 						=     images[None, ...]
								UpperCAmelCase:					Dict 						=     (images * 2_5_5).round().astype("uint8"							)
								if images.shape[-1] == 1:
									# special case for grayscale (single channel) images
									UpperCAmelCase:					Dict 						=     [Image.fromarray(image.squeeze()	,				mode="L"							) for image in images]
								else:
									UpperCAmelCase:					Dict 						=     [Image.fromarray(__snake_case							) for image in images]

								return pil_images



							@staticmethod
							def     A__ (     __snake_case							)						->     np.ndarray:






								"""simple docstring"""



								if not isinstance(__snake_case	,				__snake_case							):
									UpperCAmelCase:					Dict 						=     [images]
								UpperCAmelCase:					Optional[Any] 						=     [np.array(__snake_case							).astype(np.floataa							) / 2_55.0 for image in images]
								UpperCAmelCase:					str 						=     np.stack(__snake_case	,				axis=0							)

								return images



							@staticmethod
							def     A__ (     __snake_case							)						->     torch.FloatTensor:






								"""simple docstring"""



								if images.ndim == 3:
									UpperCAmelCase:					Union[str, Any] 						=     images[..., None]

								UpperCAmelCase:					Optional[Any] 						=     torch.from_numpy(images.transpose(0	,				3	,				1	,				2							)							)
								return images



							@staticmethod
							def     A__ (     __snake_case							)						->     np.ndarray:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     images.cpu().permute(0	,				2	,				3	,				1							).float().numpy()
								return images



							@staticmethod
							def     A__ (     __snake_case							)						->     List[str]:






								"""simple docstring"""



								return 2.0 * images - 1.0



							@staticmethod
							def     A__ (     __snake_case							)						->     List[str]:






								"""simple docstring"""



								return (images / 2 + 0.5).clamp(0	,				1							)



							@staticmethod
							def     A__ (     __snake_case							)						->     PIL.Image.Image:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     image.convert("RGB"							)
								return image



							def     A__ (     self	,				__snake_case	,				__snake_case = None	,				__snake_case = None	,				)						->     PIL.Image.Image:






								"""simple docstring"""



								if height is None:
									UpperCAmelCase:					List[str] 						=     image.height
								if width is None:
									UpperCAmelCase:					Optional[int] 						=     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[Any] 						=     image.resize((width, height)	,				resample=PIL_INTERPOLATION[self.config.resample]							)
								return image



							def     A__ (     self	,				__snake_case	,				__snake_case = None	,				__snake_case = None	,				)						->     torch.Tensor:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     (PIL.Image.Image, np.ndarray, torch.Tensor)
								if isinstance(__snake_case	,				__snake_case							):
									UpperCAmelCase:					Tuple 						=     [image]
								elif not (isinstance(__snake_case	,				__snake_case							) and all(isinstance(__snake_case	,				__snake_case							) for i in image							)):
									raise ValueError(
									    F'Input is in incorrect format: {[type(__snake_case							) for i in image]}. Currently, we only support {", ".join(__snake_case							)}'							)

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

								elif isinstance(image[0]	,				np.ndarray							):
									UpperCAmelCase:					List[str] 						=     np.concatenate(__snake_case	,				axis=0							) if image[0].ndim == 4 else np.stack(__snake_case	,				axis=0							)
									UpperCAmelCase:					int 						=     self.numpy_to_pt(__snake_case							)
									UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Optional[int] 						=     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:					List[Any] 						=     torch.cat(__snake_case	,				axis=0							) if image[0].ndim == 4 else torch.stack(__snake_case	,				axis=0							)
									UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Union[str, Any] 						=     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:					int 						=     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()}]'	,				__snake_case	,				)
									UpperCAmelCase:					List[Any] 						=     False

								if do_normalize:
									UpperCAmelCase:					int 						=     self.normalize(__snake_case							)

								return image




							def     A__ (     self	,				__snake_case	,				__snake_case = "pil"	,				__snake_case = None	,				)						->     Any:






								"""simple docstring"""



								if not isinstance(__snake_case	,				torch.Tensor							):
									raise ValueError(
									    F'Input for postprocessing is in incorrect format: {type(__snake_case							)}. We only support pytorch tensor'							)
								if output_type not in ["latent", "pt", "np", "pil"]:
									UpperCAmelCase:					Optional[Any] 						=     (
									    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"	,				__snake_case	,				standard_warn=__snake_case							)
									UpperCAmelCase:					Optional[Any] 						=     "np"

								if output_type == "latent":
									return image

								if do_denormalize is None:
									UpperCAmelCase:					Union[str, 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]							)]							)

								if output_type == "pt":
									return image

								UpperCAmelCase:					str 						=     self.pt_to_numpy(__snake_case							)

								if output_type == "np":
									return image

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





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				List[str]      =							CONFIG_NAME



							@register_to_config
							def __init__(     self	,				__snake_case = True	,				__snake_case = 8	,				__snake_case = "lanczos"	,				__snake_case = True	,				)						->     List[Any]:






								"""simple docstring"""



								super().__init__()



							@staticmethod
							def     A__ (     __snake_case							)						->     str:






								"""simple docstring"""



								if images.ndim == 3:
									UpperCAmelCase:					int 						=     images[None, ...]
								UpperCAmelCase:					Union[str, Any] 						=     (images * 2_5_5).round().astype("uint8"							)
								if images.shape[-1] == 1:
									# special case for grayscale (single channel) images
									UpperCAmelCase:					Tuple 						=     [Image.fromarray(image.squeeze()	,				mode="L"							) for image in images]
								else:
									UpperCAmelCase:					int 						=     [Image.fromarray(image[:, :, :3]							) for image in images]

								return pil_images



							@staticmethod
							def     A__ (     __snake_case							)						->     str:






								"""simple docstring"""



								return image[:, :, 1] * 2**8 + image[:, :, 2]



							def     A__ (     self	,				__snake_case							)						->     int:






								"""simple docstring"""



								if images.ndim == 3:
									UpperCAmelCase:					int 						=     images[None, ...]
								UpperCAmelCase:					Tuple 						=     images[:, :, :, 3:]
								if images.shape[-1] == 6:
									UpperCAmelCase:					List[Any] 						=     (images_depth * 2_5_5).round().astype("uint8"							)
									UpperCAmelCase:					List[Any] 						=     [
									    Image.fromarray(self.rgblike_to_depthmap(__snake_case							)	,				mode="I;16"							) for image_depth in images_depth
									]
								elif images.shape[-1] == 4:
									UpperCAmelCase:					Optional[int] 						=     (images_depth * 6_55_35.0).astype(np.uintaa							)
									UpperCAmelCase:					Union[str, Any] 						=     [Image.fromarray(__snake_case	,				mode="I;16"							) for image_depth in images_depth]
								else:
									raise Exception("Not supported"							)

								return pil_images




							def     A__ (     self	,				__snake_case	,				__snake_case = "pil"	,				__snake_case = None	,				)						->     Tuple:






								"""simple docstring"""



								if not isinstance(__snake_case	,				torch.Tensor							):
									raise ValueError(
									    F'Input for postprocessing is in incorrect format: {type(__snake_case							)}. We only support pytorch tensor'							)
								if output_type not in ["latent", "pt", "np", "pil"]:
									UpperCAmelCase:					str 						=     (
									    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"	,				__snake_case	,				standard_warn=__snake_case							)
									UpperCAmelCase:					str 						=     "np"

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

								UpperCAmelCase:					Union[str, Any] 						=     torch.stack(
								    [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

								UpperCAmelCase:					Optional[Any] 						=     self.pt_to_numpy(__snake_case							)

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

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


import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				int      =							UNetaDModel
							lowerCamelCase__:				Any      =							'''sample'''



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     4
								UpperCAmelCase:					Optional[Any] 						=     1_4
								UpperCAmelCase:					str 						=     1_6

								UpperCAmelCase:					Optional[int] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

								return {"sample": noise, "timestep": time_step}



							@property
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								return (4, 1_4, 1_6)



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






								"""simple docstring"""



								return (4, 1_4, 1_6)



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								pass



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								super().test_determinism()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								super().test_outputs_equivalence()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								super().test_from_save_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     str:






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								super().test_model_from_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								super().test_output()



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "time_embedding_type": "positional",
								    "use_timestep_embedding": True,
								    "flip_sin_to_cos": False,
								    "freq_shift": 1.0,
								    "out_block_type": "OutConv1DBlock",
								    "mid_block_type": "MidResTemporalBlock1D",
								    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
								    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
								    "act_fn": "mish",
								}
								UpperCAmelCase:					Tuple 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="unet"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(loading_info["missing_keys"]							)	,				0							)

								model.to(__snake_case							)
								UpperCAmelCase:					Tuple 						=     model(**self.dummy_input							)

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



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"	,				subfolder="unet"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					int 						=     model.config.in_channels
								UpperCAmelCase:					List[str] 						=     1_6
								UpperCAmelCase:					Union[str, Any] 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					List[str] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(__snake_case	,				__snake_case							).sample.permute(0	,				2	,				1							)

								UpperCAmelCase:					str 						=     output[0, -3:, -3:].flatten()
								# fmt: off
								UpperCAmelCase:					Dict 						=     torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass




							@slow
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     "harmonai/maestro-150k"
								UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(__snake_case	,				subfolder="unet"							)
								model.to(__snake_case							)

								UpperCAmelCase:					Any 						=     6_5_5_3_6
								UpperCAmelCase:					str 						=     torch.sin(torch.arange(__snake_case							)[None, None, :].repeat(1	,				2	,				1							)							).to(__snake_case							)
								UpperCAmelCase:					Dict 						=     torch.tensor([1]							).to(__snake_case							)

								with torch.no_grad():
									UpperCAmelCase:					Optional[Any] 						=     model(__snake_case	,				__snake_case							).sample

								UpperCAmelCase:					List[Any] 						=     output.abs().sum()
								UpperCAmelCase:					Dict 						=     output.abs().max()

								assert (output_sum - 2_24.08_96).abs() < 0.5
								assert (output_max - 0.06_07).abs() < 4e-4





class 		__lowerCamelCase     (   lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				Dict      =							UNetaDModel
							lowerCamelCase__:				int      =							'''sample'''



							@property
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					List[Any] 						=     4
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					Optional[int] 						=     1_6

								UpperCAmelCase:					List[str] 						=     floats_tensor((batch_size, num_features, seq_len)							).to(__snake_case							)
								UpperCAmelCase:					Any 						=     torch.tensor([1_0] * batch_size							).to(__snake_case							)

								return {"sample": noise, "timestep": time_step}



							@property
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								return (4, 1_4, 1_6)



							@property
							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								return (4, 1_4, 1)



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								super().test_determinism()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								super().test_outputs_equivalence()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								super().test_from_save_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								super().test_from_save_pretrained_variant()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								super().test_model_from_pretrained()



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[int] 						=     self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase:					Tuple 						=     self.model_class(**__snake_case							)
								model.to(__snake_case							)
								model.eval()

								with torch.no_grad():
									UpperCAmelCase:					Union[str, Any] 						=     model(**__snake_case							)

									if isinstance(__snake_case	,				__snake_case							):
										UpperCAmelCase:					List[str] 						=     output.sample

								self.assertIsNotNone(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     torch.Size((inputs_dict["sample"].shape[0], 1)							)
								self.assertEqual(output.shape	,				__snake_case	,				"Input and output shapes do not match"							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								pass



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {
								    "in_channels": 1_4,
								    "out_channels": 1_4,
								    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
								    "up_block_types": [],
								    "out_block_type": "ValueFunction",
								    "mid_block_type": "ValueFunctionMidBlock1D",
								    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
								    "layers_per_block": 1,
								    "downsample_each_block": True,
								    "use_timestep_embedding": True,
								    "freq_shift": 1.0,
								    "flip_sin_to_cos": False,
								    "time_embedding_type": "positional",
								    "act_fn": "mish",
								}
								UpperCAmelCase:					Union[str, Any] 						=     self.dummy_input
								return init_dict, inputs_dict



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								self.assertIsNotNone(__snake_case							)
								self.assertEqual(len(vf_loading_info["missing_keys"]							)	,				0							)

								value_function.to(__snake_case							)
								UpperCAmelCase:					Union[str, Any] 						=     value_function(**self.dummy_input							)

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



							@unittest.skipIf(torch_device == "mps"	,				"mish op not supported in MPS"							)
							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     UNetaDModel.from_pretrained(
								    "bglick13/hopper-medium-v2-value-function-hor32"	,				output_loading_info=__snake_case	,				subfolder="value_function"							)
								torch.manual_seed(0							)
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0							)

								UpperCAmelCase:					str 						=     value_function.config.in_channels
								UpperCAmelCase:					int 						=     1_4
								UpperCAmelCase:					int 						=     torch.randn((1, seq_len, num_features)							).permute(
								    0	,				2	,				1							)  # match original, we can update values and remove
								UpperCAmelCase:					Union[str, Any] 						=     torch.full((num_features,)	,				0							)

								with torch.no_grad():
									UpperCAmelCase:					Dict 						=     value_function(__snake_case	,				__snake_case							).sample

								# fmt: off
								UpperCAmelCase:					Tuple 						=     torch.tensor([1_65.25] * seq_len							)
								# fmt: on
								self.assertTrue(torch.allclose(__snake_case	,				__snake_case	,				rtol=1e-3							)							)




							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								pass
