



# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


UpperCAmelCase								=   """Here is a text containing a lot of information: '''{text}'''.

Can you answer this question about the text: '{question}'"""

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: List[Any]             =   """google/flan-t5-base"""
							UpperCamelCase__			: Optional[Any]             =   (
							    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
							    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
							)
							UpperCamelCase__			: Any             =   """text_qa"""
							UpperCamelCase__			: str             =   AutoTokenizer
							UpperCamelCase__			: Union[str, Any]             =   AutoModelForSeqaSeqLM

							UpperCamelCase__			: Union[str, Any]             =   ["""text""", """text"""]
							UpperCamelCase__			: int             =   ["""text"""]


							def _a (							self					,					a_					,					a_						):
											a__			 =						QA_PROMPT.format(text=a_					,					question=a_						)
											return self.pre_processor(a_					,					return_tensors="""pt"""						)


							def _a (							self					,					a_						):
											a__			 =						self.model.generate(**a_						)

											a__ ,     a__			 =						inputs["""input_ids"""].shape
											a__			 =						output_ids.shape[0]

											return output_ids.reshape(a_					,					out_b // in_b					,					*output_ids.shape[1:]						)[0][0]







							def _a (							self					,					a_						):
											return self.pre_processor.decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)






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
	)

