


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



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






def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Union[str, Any]   ,    lowercase__	:int   ,    lowercase__	:Dict   ,    lowercase__	:str   ,    lowercase__	:Optional[int]					)							->	Union[str, Any]:
						__magic_name__					:				Union[str, Any]				=   utils.load_model(lowerCAmelCase__   ,    lowerCAmelCase__   ,    lowerCAmelCase__   ,    epoch=lowerCAmelCase__   ,    device=lowerCAmelCase__					)
						__magic_name__					:				Dict				=   TrajectoryTransformerModel(gpt.config					)

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()					)
						__magic_name__					:				Union[str, 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__":
			snake_case__  				=			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'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase									=      {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					_UpperCamelCase									=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




