'''simple docstring'''

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 		SCREAMING_SNAKE_CASE_					(utils.Parser      ):



			"""simple docstring"""






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







def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[Any],      _lowerCAmelCase    :			int,      _lowerCAmelCase    :			int,      _lowerCAmelCase    :			Optional[int],      _lowerCAmelCase    :			List[Any])    ->		Any:



						"""simple docstring"""

						snake_case				,					snake_case :       Optional[int]				=    utils.load_model(_lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase,      epoch=_lowerCAmelCase,      device=_lowerCAmelCase)
						snake_case :       List[str]				=    TrajectoryTransformerModel(gpt.config)

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict())
						snake_case :       int				=    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_	   = 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'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A_	   = {
    "configuration_convnextv2": [
        "CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConvNextV2Config",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_	   = [
						    "CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvNextV2ForImageClassification",
						    "ConvNextV2Model",
						    "ConvNextV2PreTrainedModel",
						    "ConvNextV2Backbone",
						]


if TYPE_CHECKING:
						from .configuration_convnextva import (
						    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ConvNextVaConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convnextva import (
												    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvNextVaBackbone,
												    ConvNextVaForImageClassification,
												    ConvNextVaModel,
												    ConvNextVaPreTrainedModel,
												)

else:
						import sys

						A_	   = _LazyModule(__name__, globals()["__file__"], _import_structure)





