






"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





       """simple docstring"""





       _SCREAMING_SNAKE_CASE    =DiTPipeline
       _SCREAMING_SNAKE_CASE    =CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
       _SCREAMING_SNAKE_CASE    =PipelineTesterMixin.required_optional_params - {
           'latents',
           'num_images_per_prompt',
           'callback',
           'callback_steps',
       }
       _SCREAMING_SNAKE_CASE    =CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
       _SCREAMING_SNAKE_CASE    =False




       def  lowercase		(   self:     Optional[Any]    ):

            '''simple docstring'''

            torch.manual_seed(0    )
            a__          =  TransformeraDModel(
                sample_size=16				,		num_layers=2				,		patch_size=4				,		attention_head_dim=8				,		num_attention_heads=2				,		in_channels=4				,		out_channels=8				,		attention_bias=__A				,		activation_fn='''gelu-approximate'''				,		num_embeds_ada_norm=1000				,		norm_type='''ada_norm_zero'''				,		norm_elementwise_affine=__A				,		)
            a__          =  AutoencoderKL()
            a__          =  DDIMScheduler()
            a__          =  {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
            return components




       def  lowercase		(   self:     Tuple				,		__A:     List[str]				,		__A:     Any=0    ):

            '''simple docstring'''

            if str(__A    ).startswith('''mps'''    ):
                 a__          =  torch.manual_seed(__A    )
            else:
                 a__          =  torch.Generator(device=__A    ).manual_seed(__A    )
            a__          =  {
                '''class_labels''': [1],
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''output_type''': '''numpy''',
            }
            return inputs




       def  lowercase		(   self:     Any    ):

            '''simple docstring'''

            a__          =  '''cpu'''

            a__          =  self.get_dummy_components()
            a__          =  self.pipeline_class(**__A    )
            pipe.to(__A    )
            pipe.set_progress_bar_config(disable=__A    )

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

            self.assertEqual(image.shape				,		(1, 16, 16, 3)    )
            a__          =  np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]    )
            a__          =  np.abs(image_slice.flatten() - expected_slice    ).max()
            self.assertLessEqual(__A				,		1e-3    )




       def  lowercase		(   self:     Dict    ):

            '''simple docstring'''

            self._test_inference_batch_single_identical(relax_max_difference=__A				,		expected_max_diff=1e-3    )





       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available()				,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,		)
       def  lowercase		(   self:     int    ):

            '''simple docstring'''

            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







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





       """simple docstring"""





       def  lowercase		(   self:     Optional[Any]    ):

            '''simple docstring'''

            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




       def  lowercase		(   self:     int    ):

            '''simple docstring'''

            a__          =  torch.manual_seed(0    )

            a__          =  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''    )
            pipe.to('''cuda'''    )

            a__          =  ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
            a__          =  pipe.get_label_ids(__A    )

            a__          =  pipe(__A				,		generator=__A				,		num_inference_steps=40				,		output_type='''np'''    ).images

            for word, image in zip(__A				,		__A    ):
                 a__          =  load_numpy(
                     F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'    )
                 assert np.abs((expected_image - image).max()    ) < 1e-2





       def  lowercase		(   self:     Optional[int]    ):

            '''simple docstring'''

            a__          =  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''    )
            a__          =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
            pipe.to('''cuda'''    )

            a__          =  ['''vase''', '''umbrella''']
            a__          =  pipe.get_label_ids(__A    )

            a__          =  torch.manual_seed(0    )
            a__          =  pipe(__A				,		generator=__A				,		num_inference_steps=25				,		output_type='''np'''    ).images

            for word, image in zip(__A				,		__A    ):
                 a__          =  load_numpy(
                     '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                     F'/dit/{word}_512.npy'    )

                 assert np.abs((expected_image - image).max()    ) < 1e-1










"""simple docstring"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  OmegaConf.load(lowerCamelCase_)
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')['''model''']
					a__          =  list(state_dict.keys())

					# extract state_dict for VQVAE
					a__          =  {}
					a__          =  '''first_stage_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

    # extract state_dict for UNetLDM
					a__          =  {}
					a__          =  '''model.diffusion_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

					a__          =  config.model.params.first_stage_config.params
					a__          =  config.model.params.unet_config.params

					a__          =  VQModel(**lowerCamelCase_).eval()
					vqvae.load_state_dict(lowerCamelCase_)

					a__          =  UNetLDMModel(**lowerCamelCase_).eval()
					unet.load_state_dict(lowerCamelCase_)

					a__          =  DDIMScheduler(
					    timesteps=config.model.params.timesteps ,     beta_schedule='''scaled_linear''' ,     beta_start=config.model.params.linear_start ,     beta_end=config.model.params.linear_end ,     clip_sample=lowerCamelCase_ ,     )

					a__          =  LDMPipeline(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					pipeline.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	Tuple             = argparse.ArgumentParser()
	parser.add_argument('--checkpoint_path', type=str, required=True)
	parser.add_argument('--config_path', type=str, required=True)
	parser.add_argument('--output_path', type=str, required=True)
	__a       :	Optional[int]             = parser.parse_args()

	convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


