
'''simple docstring'''






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 							_snake_case     (  utils.Parser ):
	"""simple docstring"""





	_UpperCamelCase							:  str								=		"halfcheetah-medium-expert-v2"
	_UpperCamelCase							:  str								=		"config.offline"







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_							,		lowerCAmelCase_   :       Tuple    							=utils.load_model(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					epoch=_SCREAMING_SNAKE_CASE  ,					device=_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Tuple    							=TrajectoryTransformerModel(gpt.config  )

		trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()  )
		lowerCAmelCase_   :       List[Any]    							=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
							)








'''simple docstring'''






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import SegformerImageProcessor

class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	def __init__(						self				: int					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: Any=7					,						UpperCamelCase_				: Optional[Any]=3					,						UpperCamelCase_				: Optional[Any]=30					,						UpperCamelCase_				: List[Any]=400					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: List[str]=None					,						UpperCamelCase_				: str=True					,						UpperCamelCase_				: Tuple=[0.5, 0.5, 0.5]					,						UpperCamelCase_				: int=[0.5, 0.5, 0.5]					,						UpperCamelCase_				: str=False					,						):
			lowerCAmelCase_   :       Tuple    							=size if size is not None else {'''height''': 30, '''width''': 30}
			lowerCAmelCase_   :       List[Any]    							=parent
			lowerCAmelCase_   :       int    							=batch_size
			lowerCAmelCase_   :       int    							=num_channels
			lowerCAmelCase_   :       Union[str, Any]    							=min_resolution
			lowerCAmelCase_   :       Tuple    							=max_resolution
			lowerCAmelCase_   :       Tuple    							=do_resize
			lowerCAmelCase_   :       List[str]    							=size
			lowerCAmelCase_   :       int    							=do_normalize
			lowerCAmelCase_   :       int    							=image_mean
			lowerCAmelCase_   :       str    							=image_std
			lowerCAmelCase_   :       List[Any]    							=do_reduce_labels





	def 	__A      (						self				: Optional[Any]      ):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_reduce_labels": self.do_reduce_labels,
			}







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       Optional[int]    							=load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,					split='''test'''  )

		lowerCAmelCase_   :       Optional[Any]    							=Image.open(dataset[0]['''file''']  )
		lowerCAmelCase_   :       Tuple    							=Image.open(dataset[1]['''file''']  )

		return image, map







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       Tuple    							=load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,					split='''test'''  )

		lowerCAmelCase_   :       Tuple    							=Image.open(dataset[0]['''file''']  )
		lowerCAmelCase_   :       Tuple    							=Image.open(dataset[1]['''file''']  )
		lowerCAmelCase_   :       Optional[int]    							=Image.open(dataset[2]['''file''']  )
		lowerCAmelCase_   :       Union[str, Any]    							=Image.open(dataset[3]['''file''']  )

		return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 							_snake_case     (  lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  List[Any]								=		SegformerImageProcessor if is_vision_available() else None




	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       List[str]    							=SegformerImageProcessingTester(self      )




	@property
	def 	__A      (						self				: str      ):
			return self.image_processor_tester.prepare_image_processor_dict()




	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       Optional[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''do_resize'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''size'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''do_normalize'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''image_mean'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''image_std'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''do_reduce_labels'''      )      )




	def 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       str    							=self.image_processing_class.from_dict(self.image_processor_dict      )
			self.assertEqual(image_processor.size					,						{'''height''': 30, '''width''': 30}      )
			self.assertEqual(image_processor.do_reduce_labels					,						UpperCamelCase_      )

			lowerCAmelCase_   :       Tuple    							=self.image_processing_class.from_dict(self.image_processor_dict					,						size=42					,						reduce_labels=UpperCamelCase_      )
			self.assertEqual(image_processor.size					,						{'''height''': 42, '''width''': 42}      )
			self.assertEqual(image_processor.do_reduce_labels					,						UpperCamelCase_      )




	def 	__A      (						self				: Dict      ):
			pass




	def 	__A      (						self				: Any      ):
			# Initialize image_processing
			lowerCAmelCase_   :       List[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PIL images
			lowerCAmelCase_   :       Dict    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_      )
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						Image.Image      )

			# Test not batched input
			lowerCAmelCase_   :       int    							=image_processing(image_inputs[0]					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)




	def 	__A      (						self				: str      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Union[str, Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random numpy tensors
			lowerCAmelCase_   :       List[str]    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						numpify=UpperCamelCase_      )
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						np.ndarray      )

			# Test not batched input
			lowerCAmelCase_   :       Any    							=image_processing(image_inputs[0]					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)




	def 	__A      (						self				: List[str]      ):
			# Initialize image_processing
			lowerCAmelCase_   :       List[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			lowerCAmelCase_   :       str    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						torchify=UpperCamelCase_      )
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						torch.Tensor      )

			# Test not batched input
			lowerCAmelCase_   :       Optional[int]    							=image_processing(image_inputs[0]					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       str    							=image_processing(UpperCamelCase_					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)




	def 	__A      (						self				: str      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Any    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			lowerCAmelCase_   :       Optional[Any]    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						torchify=UpperCamelCase_      )
			lowerCAmelCase_   :       Tuple    							=[]
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						torch.Tensor      )
					maps.append(torch.zeros(image.shape[-2:]      ).long()      )

			# Test not batched input
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(image_inputs[0]					,						maps[0]					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        1,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )

			# Test batched
			lowerCAmelCase_   :       Union[str, Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )

			# Test not batched input (PIL images)
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=prepare_semantic_single_inputs()

			lowerCAmelCase_   :       Union[str, Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        1,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )

			# Test batched input (PIL images)
			lowerCAmelCase_							,		lowerCAmelCase_   :       Any    							=prepare_semantic_batch_inputs()

			lowerCAmelCase_   :       Union[str, Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        2,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        2,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )





	def 	__A      (						self				: List[str]      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Any    							=self.image_processing_class(**self.image_processor_dict      )

			# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=prepare_semantic_single_inputs()
			lowerCAmelCase_   :       Dict    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 150      )

			lowerCAmelCase_   :       Optional[Any]    							=True
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )






