






import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			UpperCamelCase_				(      snake_case_	, unittest.TestCase    ):



							'''simple docstring'''




							lowerCAmelCase													=			KandinskyVaaPriorEmbaEmbPipeline
							lowerCAmelCase													=			['''prompt''', '''image''']
							lowerCAmelCase													=			['''prompt''', '''image''']
							lowerCAmelCase													=			[
							    '''num_images_per_prompt''',
							    '''strength''',
							    '''generator''',
							    '''num_inference_steps''',
							    '''latents''',
							    '''negative_prompt''',
							    '''guidance_scale''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowerCAmelCase													=			False




							@property
							def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
														return 32




							@property
							def 							_UpperCamelCase      (	self )	->   str:
														return 32




							@property
							def 							_UpperCamelCase      (	self )	->   List[str]:
														return self.time_input_dim




							@property
							def 							_UpperCamelCase      (	self )	->   int:
														return self.time_input_dim * 4




							@property
							def 							_UpperCamelCase      (	self )	->   List[str]:
														return 1_00




							@property
							def 							_UpperCamelCase      (	self )	->   Optional[Any]:
														snake_case_           =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
														return tokenizer




							@property
							def 							_UpperCamelCase      (	self )	->   Dict:
														torch.manual_seed(0 )
														snake_case_           =							CLIPTextConfig(
														    bos_token_id=0	,      eos_token_id=2	,      hidden_size=self.text_embedder_hidden_size	,      projection_dim=self.text_embedder_hidden_size	,      intermediate_size=37	,      layer_norm_eps=1E-05	,      num_attention_heads=4	,      num_hidden_layers=5	,      pad_token_id=1	,      vocab_size=10_00	,      )
														return CLIPTextModelWithProjection(a )




							@property
							def 							_UpperCamelCase      (	self )	->   int:
														torch.manual_seed(0 )

														snake_case_           =							{
														    'num_attention_heads': 2,
														    'attention_head_dim': 12,
														    'embedding_dim': self.text_embedder_hidden_size,
														    'num_layers': 1,
														}

														snake_case_           =							PriorTransformer(**a )
														# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
														snake_case_           =							nn.Parameter(torch.ones(model.clip_std.shape ) )
														return model




							@property
							def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
														torch.manual_seed(0 )
														snake_case_           =							CLIPVisionConfig(
														    hidden_size=self.text_embedder_hidden_size	,      image_size=2_24	,      projection_dim=self.text_embedder_hidden_size	,      intermediate_size=37	,      num_attention_heads=4	,      num_channels=3	,      num_hidden_layers=5	,      patch_size=14	,      )

														snake_case_           =							CLIPVisionModelWithProjection(a )
														return model




							@property
							def 							_UpperCamelCase      (	self )	->   str:
														snake_case_           =							CLIPImageProcessor(
														    crop_size=2_24	,      do_center_crop=a	,      do_normalize=a	,      do_resize=a	,      image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]	,      image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]	,      resample=3	,      size=2_24	,      )

														return image_processor




							def 							_UpperCamelCase      (	self )	->   Any:
														snake_case_           =							self.dummy_prior
														snake_case_           =							self.dummy_image_encoder
														snake_case_           =							self.dummy_text_encoder
														snake_case_           =							self.dummy_tokenizer
														snake_case_           =							self.dummy_image_processor

														snake_case_           =							UnCLIPScheduler(
														    variance_type='fixed_small_log'	,      prediction_type='sample'	,      num_train_timesteps=10_00	,      clip_sample=a	,      clip_sample_range=10.0	,      )

														snake_case_           =							{
														    'prior': prior,
														    'image_encoder': image_encoder,
														    'text_encoder': text_encoder,
														    'tokenizer': tokenizer,
														    'scheduler': scheduler,
														    'image_processor': image_processor,
														}

														return components




							def 							_UpperCamelCase      (	self	,      a	,      a=0 )	->   str:
														if str(a ).startswith('mps' ):
																					snake_case_           =							torch.manual_seed(a )
														else:
																					snake_case_           =							torch.Generator(device=a ).manual_seed(a )

														snake_case_           =							floats_tensor((1, 3, 64, 64)	,      rng=random.Random(a ) ).to(a )
														snake_case_           =							image.cpu().permute(0	,      2	,      3	,      1 )[0]
														snake_case_           =							Image.fromarray(np.uinta(a ) ).convert('RGB' ).resize((2_56, 2_56) )

														snake_case_           =							{
														    'prompt': 'horse',
														    'image': init_image,
														    'strength': 0.5,
														    'generator': generator,
														    'guidance_scale': 4.0,
														    'num_inference_steps': 2,
														    'output_type': 'np',
														}
														return inputs




							def 							_UpperCamelCase      (	self )	->   Optional[Any]:
														snake_case_           =							'cpu'

														snake_case_           =							self.get_dummy_components()

														snake_case_           =							self.pipeline_class(**a )
														snake_case_           =							pipe.to(a )

														pipe.set_progress_bar_config(disable=a )

														snake_case_           =							pipe(**self.get_dummy_inputs(a ) )
														snake_case_           =							output.image_embeds

														snake_case_           =							pipe(
														    **self.get_dummy_inputs(a )	,      return_dict=a	,      )[0]

														snake_case_           =							image[0, -10:]
														snake_case_           =							image_from_tuple[0, -10:]

														assert image.shape == (1, 32)

														snake_case_           =							np.array(
														    [
														        0.1_071_284,
														        1.3_330_271,
														        0.61_260_223,
														        -0.6_691_065,
														        -0.3_846_852,
														        -1.0_303_661,
														        0.22_716_111,
														        0.03_348_901,
														        0.30_040_675,
														        -0.24_805_029,
														    ] )

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




							@skip_mps
							def 							_UpperCamelCase      (	self )	->   Any:
														snake_case_           =							torch_device == 'cpu'
														snake_case_           =							True
														snake_case_           =							False

														self._test_inference_batch_single_identical(
														    test_max_difference=a	,      relax_max_difference=a	,      test_mean_pixel_difference=a	,      )






							@skip_mps
							def 							_UpperCamelCase      (	self )	->   int:
														snake_case_           =							torch_device == 'cpu'
														snake_case_           =							False

														self._test_attention_slicing_forward_pass(
														    test_max_difference=a	,      test_mean_pixel_difference=a	,      )













import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 			UpperCamelCase_				(      utils.Parser    ):



	'''simple docstring'''




	lowerCAmelCase													=			"halfcheetah-medium-expert-v2"
	lowerCAmelCase													=			"config.offline"






def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_      ,		snake_case_           =							utils.load_model(a_							,					a_							,					a_							,					epoch=a_							,					device=a_)
							snake_case_           =							TrajectoryTransformerModel(gpt.config)

							trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict())
							snake_case_           =							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__":
	lowercase  							= Parser().parse_args("plan")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)





