



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        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		DDIMPipeline
							_lowerCamelCase						=		UNCONDITIONAL_IMAGE_GENERATION_PARAMS
							_lowerCamelCase						=		PipelineTesterMixin.required_optional_params - {
							    'num_images_per_prompt',
							    'latents',
							    'callback',
							    'callback_steps',
							}
							_lowerCamelCase						=		UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
							_lowerCamelCase						=		False



							def 	UpperCamelCase	(						self    ):
													torch.manual_seed(0    )
													_snake_case						:					int    =   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")				,    )
													_snake_case						:					Tuple    =   DDIMScheduler()
													_snake_case						:					int    =   {"unet": unet, "scheduler": scheduler}
													return components



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_=0    ):

													if str(lowerCamelCase_    ).startswith("mps"    ):
																			_snake_case						:					Optional[Any]    =   torch.manual_seed(lowerCamelCase_    )
													else:
																			_snake_case						:					List[str]    =   torch.Generator(device=lowerCamelCase_    ).manual_seed(lowerCamelCase_    )
													_snake_case						:					Tuple    =   {
													    "batch_size": 1,
													    "generator": generator,
													    "num_inference_steps": 2,
													    "output_type": "numpy",
													}
													return inputs



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   "cpu"

													_snake_case						:					int    =   self.get_dummy_components()
													_snake_case						:					str    =   self.pipeline_class(**lowerCamelCase_    )
													pipe.to(lowerCamelCase_    )
													pipe.set_progress_bar_config(disable=lowerCamelCase_    )

													_snake_case						:					Union[str, Any]    =   self.get_dummy_inputs(lowerCamelCase_    )
													_snake_case						:					Any    =   pipe(**lowerCamelCase_    ).images
													_snake_case						:					Optional[int]    =   image[0, -3:, -3:, -1]

													self.assertEqual(image.shape				,    (1, 32, 32, 3)    )
													_snake_case						:					int    =   np.array(
													    [1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04]    )
													_snake_case						:					Tuple    =   np.abs(image_slice.flatten() - expected_slice    ).max()
													self.assertLessEqual(lowerCamelCase_				,    1e-3    )



							def 	UpperCamelCase	(						self    ):
													super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )



							def 	UpperCamelCase	(						self    ):
													super().test_save_load_local(expected_max_difference=3e-3    )



							def 	UpperCamelCase	(						self    ):
													super().test_save_load_optional_components(expected_max_difference=3e-3    )



							def 	UpperCamelCase	(						self    ):
													super().test_inference_batch_single_identical(expected_max_diff=3e-3    )




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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   "google/ddpm-cifar10-32"

													_snake_case						:					Any    =   UNetaDModel.from_pretrained(lowerCamelCase_    )
													_snake_case						:					List[Any]    =   DDIMScheduler()

													_snake_case						:					Tuple    =   DDIMPipeline(unet=lowerCamelCase_				,    scheduler=lowerCamelCase_    )
													ddim.to(lowerCamelCase_    )
													ddim.set_progress_bar_config(disable=lowerCamelCase_    )

													_snake_case						:					str    =   torch.manual_seed(0    )
													_snake_case						:					List[str]    =   ddim(generator=lowerCamelCase_				,    eta=0.0				,    output_type="numpy"    ).images

													_snake_case						:					Union[str, Any]    =   image[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													_snake_case						:					Optional[Any]    =   np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]    )

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   "google/ddpm-ema-bedroom-256"

													_snake_case						:					str    =   UNetaDModel.from_pretrained(lowerCamelCase_    )
													_snake_case						:					int    =   DDIMScheduler.from_pretrained(lowerCamelCase_    )

													_snake_case						:					Optional[Any]    =   DDIMPipeline(unet=lowerCamelCase_				,    scheduler=lowerCamelCase_    )
													ddpm.to(lowerCamelCase_    )
													ddpm.set_progress_bar_config(disable=lowerCamelCase_    )

													_snake_case						:					Optional[int]    =   torch.manual_seed(0    )
													_snake_case						:					Optional[Any]    =   ddpm(generator=lowerCamelCase_				,    output_type="numpy"    ).images

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

													assert image.shape == (1, 256, 256, 3)
													_snake_case						:					Union[str, Any]    =   np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]    )

													assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'M-CLIP'



							def __init__(						self				,    lowercase_=1_024				,    lowercase_=768				,    **lowercase_    ):
													_snake_case						:					str    =   transformerDimSize
													_snake_case						:					Union[str, Any]    =   imageDimSize
													super().__init__(**lowercase_    )




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		MCLIPConfig



							def __init__(						self				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													super().__init__(lowercase_				,    *lowercase_				,    **lowercase_    )
													_snake_case						:					List[Any]    =   XLMRobertaModel(lowercase_    )
													_snake_case						:					int    =   torch.nn.Linear(
													    in_features=config.transformerDimensions				,    out_features=config.numDims    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   self.transformer(input_ids=lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Tuple    =   (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
													return self.LinearTransformation(lowercase_    ), embs