

import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 				a  (	unittest.TestCase				):


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		List[Any] 		=    UNetaDModel(
										    block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=("DownBlock2D", "AttnDownBlock2D")						,					up_block_types=("AttnUpBlock2D", "UpBlock2D")						,					)
										return model





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self.dummy_uncond_unet
										_UpperCAmelCase       :		Tuple 		=    ScoreSdeVeScheduler()

										_UpperCAmelCase       :		Dict 		=    ScoreSdeVePipeline(unet=A_						,					scheduler=A_	)
										sde_ve.to(A_	)
										sde_ve.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Optional[int] 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		str 		=    sde_ve(num_inference_steps=2						,					output_type="numpy"						,					generator=A_	).images

										_UpperCAmelCase       :		str 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		int 		=    sde_ve(num_inference_steps=2						,					output_type="numpy"						,					generator=A_						,					return_dict=A_	)[
										    0
										]

										_UpperCAmelCase       :		List[Any] 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		Any 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										_UpperCAmelCase       :		Optional[Any] 		=    np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







@slow
@require_torch
class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    "google/ncsnpp-church-256"
										_UpperCAmelCase       :		Dict 		=    UNetaDModel.from_pretrained(A_	)

										_UpperCAmelCase       :		int 		=    ScoreSdeVeScheduler.from_pretrained(A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    ScoreSdeVePipeline(unet=A_						,					scheduler=A_	)
										sde_ve.to(A_	)
										sde_ve.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		List[str] 		=    sde_ve(num_inference_steps=10						,					output_type="numpy"						,					generator=A_	).images

										_UpperCAmelCase       :		List[str] 		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 256, 256, 3)

										_UpperCAmelCase       :		Any 		=    np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"timesformer"


						def __init__(       self						,					A_=224						,					A_=16						,					A_=3						,					A_=8						,					A_=768						,					A_=12						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.0						,					A_=0.0						,					A_=0.02						,					A_=1e-6						,					A_=True						,					A_="divided_space_time"						,					A_=0						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    image_size
										_UpperCAmelCase       :		Tuple 		=    patch_size
										_UpperCAmelCase       :		Any 		=    num_channels
										_UpperCAmelCase       :		List[Any] 		=    num_frames

										_UpperCAmelCase       :		str 		=    hidden_size
										_UpperCAmelCase       :		Any 		=    num_hidden_layers
										_UpperCAmelCase       :		Dict 		=    num_attention_heads
										_UpperCAmelCase       :		Optional[int] 		=    intermediate_size
										_UpperCAmelCase       :		Union[str, Any] 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Tuple 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Union[str, Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_eps
										_UpperCAmelCase       :		str 		=    qkv_bias

										_UpperCAmelCase       :		Tuple 		=    attention_type
										_UpperCAmelCase       :		List[Any] 		=    drop_path_rate


