



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class     __snake_case						(					UpperCamelCase_						,UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a      				=			StableDiffusionImageVariationPipeline
							_a      				=			IMAGE_VARIATION_PARAMS
							_a      				=			IMAGE_VARIATION_BATCH_PARAMS
							_a      				=			frozenset([]						)
							# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							_a      				=			frozenset([]						)




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									torch.manual_seed(0)
									lowerCAmelCase_      :					Optional[Any]     =			UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)    ,    layers_per_block=2    ,    sample_size=3_2    ,    in_channels=4    ,    out_channels=4    ,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,    cross_attention_dim=3_2    ,    )
									lowerCAmelCase_      :					Optional[int]     =			PNDMScheduler(skip_prk_steps=A_)
									torch.manual_seed(0)
									lowerCAmelCase_      :					Optional[int]     =			AutoencoderKL(
									    block_out_channels=[3_2, 6_4]    ,    in_channels=3    ,    out_channels=3    ,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,    latent_channels=4    ,    )
									torch.manual_seed(0)
									lowerCAmelCase_      :					int     =			CLIPVisionConfig(
									    hidden_size=3_2    ,    projection_dim=3_2    ,    intermediate_size=3_7    ,    layer_norm_eps=1e-05    ,    num_attention_heads=4    ,    num_hidden_layers=5    ,    image_size=3_2    ,    patch_size=4    ,    )
									lowerCAmelCase_      :					str     =			CLIPVisionModelWithProjection(A_)
									lowerCAmelCase_      :					Optional[Any]     =			CLIPImageProcessor(crop_size=3_2    ,    size=3_2)

									lowerCAmelCase_      :					Union[str, Any]     =			{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''image_encoder''': image_encoder,
									    '''feature_extractor''': feature_extractor,
									    '''safety_checker''': None,
									}
									return components




							def        UpperCAmelCase__      (	self					:	Optional[Any]    ,    A_					:	List[str]    ,    A_					:	Tuple=0):
									lowerCAmelCase_      :					List[str]     =			floats_tensor((1, 3, 3_2, 3_2)    ,    rng=random.Random(A_))
									lowerCAmelCase_      :					List[Any]     =			image.cpu().permute(0    ,    2    ,    3    ,    1)[0]
									lowerCAmelCase_      :					List[str]     =			Image.fromarray(np.uinta(A_)).convert('''RGB''').resize((3_2, 3_2))
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_      :					Tuple     =			torch.manual_seed(A_)
									else:
											lowerCAmelCase_      :					Optional[Any]     =			torch.Generator(device=A_).manual_seed(A_)
									lowerCAmelCase_      :					Optional[Any]     =			{
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs




							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					str     =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									lowerCAmelCase_      :					List[Any]     =			self.get_dummy_components()
									lowerCAmelCase_      :					Optional[Any]     =			StableDiffusionImageVariationPipeline(**A_)
									lowerCAmelCase_      :					List[str]     =			sd_pipe.to(A_)
									sd_pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_      :					Dict     =			self.get_dummy_inputs(A_)
									lowerCAmelCase_      :					Dict     =			sd_pipe(**A_).images
									lowerCAmelCase_      :					List[str]     =			image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)
									lowerCAmelCase_      :					List[str]     =			np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921])

									assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					List[str]     =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									lowerCAmelCase_      :					List[Any]     =			self.get_dummy_components()
									lowerCAmelCase_      :					Tuple     =			StableDiffusionImageVariationPipeline(**A_)
									lowerCAmelCase_      :					Dict     =			sd_pipe.to(A_)
									sd_pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_      :					Tuple     =			self.get_dummy_inputs(A_)
									lowerCAmelCase_      :					Any     =			2 * [inputs['''image''']]
									lowerCAmelCase_      :					Tuple     =			sd_pipe(**A_)

									lowerCAmelCase_      :					List[str]     =			output.images

									lowerCAmelCase_      :					Any     =			image[-1, -3:, -3:, -1]

									assert image.shape == (2, 6_4, 6_4, 3)
									lowerCAmelCase_      :					List[Any]     =			np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289])

									assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3







							def        UpperCAmelCase__      (	self					:	Optional[int]):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3)





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




							def        UpperCAmelCase__      (	self					:	Any):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Union[str, Any]    ,    A_					:	Any="cpu"    ,    A_					:	Tuple=torch.floataa    ,    A_					:	str=0):
									lowerCAmelCase_      :					int     =			torch.Generator(device=A_).manual_seed(A_)
									lowerCAmelCase_      :					Tuple     =			load_image(
									    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
									    '''/stable_diffusion_imgvar/input_image_vermeer.png''')
									lowerCAmelCase_      :					Any     =			np.random.RandomState(A_).standard_normal((1, 4, 6_4, 6_4))
									lowerCAmelCase_      :					int     =			torch.from_numpy(A_).to(device=A_    ,    dtype=A_)
									lowerCAmelCase_      :					str     =			{
									    '''image''': init_image,
									    '''latents''': latents,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									lowerCAmelCase_      :					Any     =			StableDiffusionImageVariationPipeline.from_pretrained(
									    '''lambdalabs/sd-image-variations-diffusers'''    ,    safety_checker=A_)
									lowerCAmelCase_      :					Tuple     =			sd_pipe.to(A_)
									sd_pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_      :					int     =			self.get_inputs(A_)
									lowerCAmelCase_      :					int     =			sd_pipe(**A_).images
									lowerCAmelCase_      :					List[Any]     =			image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									lowerCAmelCase_      :					List[str]     =			np.array([0.8_4491, 0.9_0789, 0.7_5708, 0.7_8734, 0.8_3485, 0.7_0099, 0.6_6938, 0.6_8727, 0.6_1379])
									assert np.abs(image_slice - expected_slice).max() < 6e-3




							def        UpperCAmelCase__      (	self					:	str):
									lowerCAmelCase_      :					Union[str, Any]     =			0

									def callback_fn(A_					:	int    ,    A_					:	int    ,    A_					:	torch.FloatTensor) -> None:
											lowerCAmelCase_      :					Dict     =			True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
													lowerCAmelCase_      :					List[str]     =			latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 6_4, 6_4)
													lowerCAmelCase_      :					List[Any]     =			latents[0, -3:, -3:, -1]
													lowerCAmelCase_      :					List[str]     =			np.array(
													    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309])

													assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
											elif step == 2:
													lowerCAmelCase_      :					Tuple     =			latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 6_4, 6_4)
													lowerCAmelCase_      :					List[Any]     =			latents[0, -3:, -3:, -1]
													lowerCAmelCase_      :					Dict     =			np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273])

													assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2

									lowerCAmelCase_      :					Optional[Any]     =			False

									lowerCAmelCase_      :					Any     =			StableDiffusionImageVariationPipeline.from_pretrained(
									    '''fusing/sd-image-variations-diffusers'''    ,    safety_checker=A_    ,    torch_dtype=torch.floataa    ,    )
									pipe.to(A_)
									pipe.set_progress_bar_config(disable=A_)
									pipe.enable_attention_slicing()

									lowerCAmelCase_      :					List[Any]     =			self.get_inputs(A_    ,    dtype=torch.floataa)
									pipe(**A_    ,    callback=A_    ,    callback_steps=1)
									assert callback_fn.has_been_called
									assert number_of_steps == inputs["num_inference_steps"]







							def        UpperCAmelCase__      (	self					:	Any):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									lowerCAmelCase_      :					Any     =			'''fusing/sd-image-variations-diffusers'''
									lowerCAmelCase_      :					List[Any]     =			StableDiffusionImageVariationPipeline.from_pretrained(
									    A_    ,    safety_checker=A_    ,    torch_dtype=torch.floataa)
									lowerCAmelCase_      :					str     =			pipe.to(A_)
									pipe.set_progress_bar_config(disable=A_)
									pipe.enable_attention_slicing(1)
									pipe.enable_sequential_cpu_offload()

									lowerCAmelCase_      :					str     =			self.get_inputs(A_    ,    dtype=torch.floataa)
									lowerCAmelCase_      :					Optional[Any]     =			pipe(**A_)

									lowerCAmelCase_      :					Tuple     =			torch.cuda.max_memory_allocated()
									# make sure that less than 2.6 GB is allocated
									assert mem_bytes < 2.6 * 1_0**9





@nightly
@require_torch_gpu
class     __snake_case						(					unittest.TestCase						):




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




							def        UpperCAmelCase__      (	self					:	int    ,    A_					:	Any    ,    A_					:	List[Any]="cpu"    ,    A_					:	Optional[int]=torch.floataa    ,    A_					:	List[str]=0):
									lowerCAmelCase_      :					Union[str, Any]     =			torch.Generator(device=A_).manual_seed(A_)
									lowerCAmelCase_      :					Optional[Any]     =			load_image(
									    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
									    '''/stable_diffusion_imgvar/input_image_vermeer.png''')
									lowerCAmelCase_      :					Dict     =			np.random.RandomState(A_).standard_normal((1, 4, 6_4, 6_4))
									lowerCAmelCase_      :					Optional[Any]     =			torch.from_numpy(A_).to(device=A_    ,    dtype=A_)
									lowerCAmelCase_      :					Union[str, Any]     =			{
									    '''image''': init_image,
									    '''latents''': latents,
									    '''generator''': generator,
									    '''num_inference_steps''': 5_0,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs




							def        UpperCAmelCase__      (	self					:	int):
									lowerCAmelCase_      :					Union[str, Any]     =			StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers''')
									sd_pipe.to(A_)
									sd_pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_      :					Optional[Any]     =			self.get_inputs(A_)
									lowerCAmelCase_      :					Dict     =			sd_pipe(**A_).images[0]

									lowerCAmelCase_      :					Tuple     =			load_numpy(
									    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
									    '''/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy''')
									lowerCAmelCase_      :					Dict     =			np.abs(expected_image - image).max()
									assert max_diff < 1e-3







							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									lowerCAmelCase_      :					List[str]     =			StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers''')
									lowerCAmelCase_      :					str     =			DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
									sd_pipe.to(A_)
									sd_pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_      :					Dict     =			self.get_inputs(A_)
									lowerCAmelCase_      :					Optional[int]     =			2_5
									lowerCAmelCase_      :					str     =			sd_pipe(**A_).images[0]

									lowerCAmelCase_      :					int     =			load_numpy(
									    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
									    '''/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy''')
									lowerCAmelCase_      :					Tuple     =			np.abs(expected_image - image).max()
									assert max_diff < 1e-3










import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



class     __snake_case						(					utils.Parser						):
							_a      				=			"halfcheetah-medium-expert-v2"
							_a      				=			"config.offline"





def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		Any					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		List[Any]			):
		lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[Any]     =			utils.load_model(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase					,epoch=__UpperCamelCase					,device=__UpperCamelCase			)
		lowerCAmelCase_      :					List[str]     =			TrajectoryTransformerModel(gpt.config			)

		trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
		lowerCAmelCase_      :					str     =			gpt.pos_emb
		trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()			)
		trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()			)
		trajectory_transformer.head.load_state_dict(gpt.head.state_dict()			)

		for i, block in enumerate(gpt.blocks			):
				trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
				trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
				trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()			)

				trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()			)
				trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()			)
				trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()			)
				trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()			)

		torch.save(trajectory_transformer.state_dict()					,'''pytorch_model.bin'''			)


if __name__ == "__main__":
						A__  :  Any       = Parser().parse_args('''plan''')
						convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
						    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
						)





