



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


A__  :  List[str]       = logging.get_logger(__name__)

A__  :  Optional[Any]       = {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			'''longt5'''
							_a      				=			['''past_key_values''']
							_a      				=			{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




							def __init__(	self					:	int    ,    A_					:	Union[str, Any]=3_2_1_2_8    ,    A_					:	List[Any]=5_1_2    ,    A_					:	Dict=6_4    ,    A_					:	Union[str, Any]=2_0_4_8    ,    A_					:	Union[str, Any]=6    ,    A_					:	Union[str, Any]=None    ,    A_					:	Optional[Any]=8    ,    A_					:	Optional[Any]=1_2_7    ,    A_					:	str=1_6    ,    A_					:	List[Any]=3_2    ,    A_					:	Optional[Any]=1_2_8    ,    A_					:	str=0.1    ,    A_					:	str=1e-6    ,    A_					:	int=1.0    ,    A_					:	Any="relu"    ,    A_					:	Optional[int]=True    ,    A_					:	Union[str, Any]="local"    ,    A_					:	Optional[int]=True    ,    A_					:	List[Any]=0    ,    A_					:	Union[str, Any]=1    ,    **A_					:	Dict    ,    ):
									lowerCAmelCase_      :					Tuple     =			vocab_size
									lowerCAmelCase_      :					int     =			d_model
									lowerCAmelCase_      :					Optional[Any]     =			d_kv
									lowerCAmelCase_      :					Any     =			d_ff
									lowerCAmelCase_      :					Union[str, Any]     =			num_layers
									# default = symmetry
									lowerCAmelCase_      :					Optional[Any]     =			num_decoder_layers if num_decoder_layers is not None else self.num_layers
									lowerCAmelCase_      :					int     =			num_heads
									lowerCAmelCase_      :					Tuple     =			local_radius
									lowerCAmelCase_      :					List[Any]     =			global_block_size
									lowerCAmelCase_      :					Optional[int]     =			relative_attention_num_buckets
									lowerCAmelCase_      :					str     =			relative_attention_max_distance
									lowerCAmelCase_      :					List[Any]     =			dropout_rate
									lowerCAmelCase_      :					str     =			layer_norm_epsilon
									lowerCAmelCase_      :					List[str]     =			initializer_factor
									lowerCAmelCase_      :					Optional[int]     =			feed_forward_proj
									lowerCAmelCase_      :					Union[str, Any]     =			encoder_attention_type
									lowerCAmelCase_      :					Optional[Any]     =			use_cache

									lowerCAmelCase_      :					List[Any]     =			self.feed_forward_proj.split('''-''')
									lowerCAmelCase_      :					Optional[int]     =			act_info[-1]
									lowerCAmelCase_      :					Any     =			act_info[0] == '''gated'''

									if len(A_) > 1 and act_info[0] != "gated" or len(A_) > 2:
											raise ValueError(
											    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
											    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
											    '''\'gated-gelu\' or \'relu\'''')

									# for backwards compatibility
									if feed_forward_proj == "gated-gelu":
											lowerCAmelCase_      :					Optional[Any]     =			'''gelu_new'''

									super().__init__(
									    pad_token_id=A_    ,    eos_token_id=A_    ,    is_encoder_decoder=A_    ,    **A_    ,    )





class     __snake_case						(					UpperCamelCase_						):




							@property
							def        UpperCAmelCase__      (	self					:	str):
									lowerCAmelCase_      :					List[Any]     =			{
									    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
									    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
									}
									if self.use_past:
											lowerCAmelCase_      :					Dict     =			'''past_encoder_sequence + sequence'''
											lowerCAmelCase_      :					Tuple     =			{0: '''batch'''}
											lowerCAmelCase_      :					List[str]     =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
									else:
											lowerCAmelCase_      :					Union[str, Any]     =			{0: '''batch''', 1: '''decoder_sequence'''}
											lowerCAmelCase_      :					List[Any]     =			{0: '''batch''', 1: '''decoder_sequence'''}

									if self.use_past:
											self.fill_with_past_key_values_(A_    ,    direction='''inputs''')

									return common_inputs







							@property
							def        UpperCAmelCase__      (	self					:	Dict):
									return 1_3










import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



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





def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		Any					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		List[Any]			):
		lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[Any]     =			utils.load_model(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase					,epoch=__UpperCamelCase					,device=__UpperCamelCase			)
		lowerCAmelCase_      :					List[str]     =			TrajectoryTransformerModel(gpt.config			)

		trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
		lowerCAmelCase_      :					str     =			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__  :  Any       = Parser().parse_args('''plan''')
						convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
						    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
						)





