



import unittest

import numpy as np

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 ConvNextImageProcessor

class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_=7					,					a_=3					,					a_=18					,					a_=30					,					a_=400					,					a_=True					,					a_=None					,					a_=0.875					,					a_=True					,					a_=[0.5, 0.5, 0.5]					,					a_=[0.5, 0.5, 0.5]					,					):
											a__			 =						size if size is not None else {"""shortest_edge""": 20}
											a__			 =						parent
											a__			 =						batch_size
											a__			 =						num_channels
											a__			 =						image_size
											a__			 =						min_resolution
											a__			 =						max_resolution
											a__			 =						do_resize
											a__			 =						size
											a__			 =						crop_pct
											a__			 =						do_normalize
											a__			 =						image_mean
											a__			 =						image_std







							def _a (							self						):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "crop_pct": self.crop_pct,
											}




@require_torch
@require_vision
class 	__snake_case (				_a     ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__			: Dict             =   ConvNextImageProcessor if is_vision_available() else None


							def _a (							self						):
											a__			 =						ConvNextImageProcessingTester(self						)


							@property
							def _a (							self						):
											return self.image_processor_tester.prepare_image_processor_dict()


							def _a (							self						):
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(snake_case_					,					"""do_resize"""						)						)
											self.assertTrue(hasattr(snake_case_					,					"""size"""						)						)
											self.assertTrue(hasattr(snake_case_					,					"""crop_pct"""						)						)
											self.assertTrue(hasattr(snake_case_					,					"""do_normalize"""						)						)
											self.assertTrue(hasattr(snake_case_					,					"""image_mean"""						)						)
											self.assertTrue(hasattr(snake_case_					,					"""image_std"""						)						)


							def _a (							self						):
											a__			 =						self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size					,					{"""shortest_edge""": 20}						)

											a__			 =						self.image_processing_class.from_dict(self.image_processor_dict					,					size=42						)
											self.assertEqual(image_processor.size					,					{"""shortest_edge""": 42}						)


							def _a (							self						):
											pass


							def _a (							self						):
											# Initialize image_processing
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											a__			 =						prepare_image_inputs(self.image_processor_tester					,					equal_resolution=snake_case_						)
											for image in image_inputs:
															self.assertIsInstance(snake_case_					,					Image.Image						)

											# Test not batched input
											a__			 =						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["""shortest_edge"""],
											        self.image_processor_tester.size["""shortest_edge"""],
											    )					,					)

											# Test batched
											a__			 =						image_processing(snake_case_					,					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["""shortest_edge"""],
											        self.image_processor_tester.size["""shortest_edge"""],
											    )					,					)


							def _a (							self						):
											# Initialize image_processing
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											a__			 =						prepare_image_inputs(self.image_processor_tester					,					equal_resolution=snake_case_					,					numpify=snake_case_						)
											for image in image_inputs:
															self.assertIsInstance(snake_case_					,					np.ndarray						)

											# Test not batched input
											a__			 =						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["""shortest_edge"""],
											        self.image_processor_tester.size["""shortest_edge"""],
											    )					,					)

											# Test batched
											a__			 =						image_processing(snake_case_					,					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["""shortest_edge"""],
											        self.image_processor_tester.size["""shortest_edge"""],
											    )					,					)







							def _a (							self						):
											# Initialize image_processing
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											a__			 =						prepare_image_inputs(self.image_processor_tester					,					equal_resolution=snake_case_					,					torchify=snake_case_						)
											for image in image_inputs:
															self.assertIsInstance(snake_case_					,					torch.Tensor						)

											# Test not batched input
											a__			 =						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["""shortest_edge"""],
											        self.image_processor_tester.size["""shortest_edge"""],
											    )					,					)

											# Test batched
											a__			 =						image_processing(snake_case_					,					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["""shortest_edge"""],
											        self.image_processor_tester.size["""shortest_edge"""],
											    )					,					)






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      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

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

