
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			__A( a  ,   unittest.TestCase			):
				snake_case_						   =    DDIMPipeline
				snake_case_						   =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				snake_case_						   =    PipelineTesterMixin.required_optional_params - {
				    '''num_images_per_prompt''',
				    '''latents''',
				    '''callback''',
				    '''callback_steps''',
				}
				snake_case_						   =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
				snake_case_						   =    False




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


								'''simple docstring'''




								torch.manual_seed(0				)
								__a										=    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''')		,     )
								__a										=    DDIMScheduler()
								__a										=    {'''unet''': unet, '''scheduler''': scheduler}
								return components




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=0				)    ->      List[str]:


								'''simple docstring'''




								if str(_snake_case				).startswith('''mps'''				):
												__a										=    torch.manual_seed(_snake_case				)
								else:
												__a										=    torch.Generator(device=_snake_case				).manual_seed(_snake_case				)
								__a										=    {
								    '''batch_size''': 1,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''numpy''',
								}
								return inputs




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    '''cpu'''

								__a										=    self.get_dummy_components()
								__a										=    self.pipeline_class(**_snake_case				)
								pipe.to(_snake_case				)
								pipe.set_progress_bar_config(disable=_snake_case				)

								__a										=    self.get_dummy_inputs(_snake_case				)
								__a										=    pipe(**_snake_case				).images
								__a										=    image[0, -3:, -3:, -1]

								self.assertEqual(image.shape		,     (1, 32, 32, 3)				)
								__a										=    np.array(
								    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]				)
								__a										=    np.abs(image_slice.flatten() - expected_slice				).max()
								self.assertLessEqual(_snake_case		,     1E-3				)




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


								'''simple docstring'''




								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								super().test_save_load_local(expected_max_difference=3E-3				)




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


								'''simple docstring'''




								super().test_save_load_optional_components(expected_max_difference=3E-3				)




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


								'''simple docstring'''




								super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







@slow
@require_torch_gpu
class 			__A( unittest.TestCase			):




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


								'''simple docstring'''




								__a										=    '''google/ddpm-cifar10-32'''

								__a										=    UNetaDModel.from_pretrained(_snake_case				)
								__a										=    DDIMScheduler()

								__a										=    DDIMPipeline(unet=_snake_case		,     scheduler=_snake_case				)
								ddim.to(_snake_case				)
								ddim.set_progress_bar_config(disable=_snake_case				)

								__a										=    torch.manual_seed(0				)
								__a										=    ddim(generator=_snake_case		,     eta=0.0		,     output_type='''numpy'''				).images

								__a										=    image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								__a										=    np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]				)

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




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


								'''simple docstring'''




								__a										=    '''google/ddpm-ema-bedroom-256'''

								__a										=    UNetaDModel.from_pretrained(_snake_case				)
								__a										=    DDIMScheduler.from_pretrained(_snake_case				)

								__a										=    DDIMPipeline(unet=_snake_case		,     scheduler=_snake_case				)
								ddpm.to(_snake_case				)
								ddpm.set_progress_bar_config(disable=_snake_case				)

								__a										=    torch.manual_seed(0				)
								__a										=    ddpm(generator=_snake_case		,     output_type='''numpy'''				).images

								__a										=    image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)
								__a										=    np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]				)

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

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A     :	List[str]    = logging.get_logger(__name__)

A     :	Optional[int]    = {
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 			__A( a			):
				snake_case_						   =    '''levit'''




				def __init__(     self		,     _snake_case=224		,     _snake_case=3		,     _snake_case=3		,     _snake_case=2		,     _snake_case=1		,     _snake_case=16		,     _snake_case=[128, 256, 384]		,     _snake_case=[4, 8, 12]		,     _snake_case=[4, 4, 4]		,     _snake_case=[16, 16, 16]		,     _snake_case=0		,     _snake_case=[2, 2, 2]		,     _snake_case=[2, 2, 2]		,     _snake_case=0.02		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    image_size
								__a										=    num_channels
								__a										=    kernel_size
								__a										=    stride
								__a										=    padding
								__a										=    hidden_sizes
								__a										=    num_attention_heads
								__a										=    depths
								__a										=    key_dim
								__a										=    drop_path_rate
								__a										=    patch_size
								__a										=    attention_ratio
								__a										=    mlp_ratio
								__a										=    initializer_range
								__a										=    [
								    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-4