




"""simple docstring"""

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCamelCase__			:     int			       =				logging.get_logger(__name__)

UpperCamelCase__			:     List[Any]			       =				{
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class      A__   (   lowercase_  ):
	A_				:					int      =   """blenderbot"""
	A_				:					Optional[int]      =   ["""past_key_values"""]
	A_				:					int      =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__( self	:						List[Any]   ,     lowercase	:						Any=8_008   ,     lowercase	:						Optional[Any]=128   ,     lowercase	:						Dict=2   ,     lowercase	:						List[str]=10_240   ,     lowercase	:						int=32   ,     lowercase	:						Any=24   ,     lowercase	:						Dict=10_240   ,     lowercase	:						str=32   ,     lowercase	:						Any=0.0   ,     lowercase	:						List[Any]=0.0   ,     lowercase	:						Tuple=True   ,     lowercase	:						Tuple=True   ,     lowercase	:						str="gelu"   ,     lowercase	:						int=2_560   ,     lowercase	:						Optional[int]=0.1   ,     lowercase	:						Optional[int]=0.0   ,     lowercase	:						Tuple=0.0   ,     lowercase	:						Union[str, Any]=0.02   ,     lowercase	:						List[Any]=1   ,     lowercase	:						Any=False   ,     lowercase	:						Union[str, Any]=0   ,     lowercase	:						int=1   ,     lowercase	:						int=2   ,     lowercase	:						Dict=3   ,     lowercase	:						Tuple=2   ,     **lowercase	:						Optional[Any]   ,     )       ->			List[Any]:
						__snake_case        =						vocab_size
						__snake_case        =						max_position_embeddings
						__snake_case        =						d_model
						__snake_case        =						encoder_ffn_dim
						__snake_case        =						encoder_layers
						__snake_case        =						encoder_attention_heads
						__snake_case        =						decoder_ffn_dim
						__snake_case        =						decoder_layers
						__snake_case        =						decoder_attention_heads
						__snake_case        =						dropout
						__snake_case        =						attention_dropout
						__snake_case        =						activation_dropout
						__snake_case        =						activation_function
						__snake_case        =						init_std
						__snake_case        =						encoder_layerdrop
						__snake_case        =						decoder_layerdrop
						__snake_case        =						use_cache
						__snake_case        =						encoder_layers
						__snake_case        =						scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     is_encoder_decoder=lowercase   ,     decoder_start_token_id=lowercase   ,     encoder_no_repeat_ngram_size=lowercase   ,     forced_eos_token_id=lowercase   ,     **lowercase   ,     )

class      A__   (   lowercase_  ):




	@property
	def 				a_		( self	:						Optional[Any]						)       ->			Mapping[str, Mapping[int, str]]:
						if self.task in ["default", "seq2seq-lm"]:
											__snake_case        =						OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]						)
											if self.use_past:
																__snake_case        =						{0: """batch"""}
																__snake_case        =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
																__snake_case        =						{0: """batch""", 1: """decoder_sequence"""}
																__snake_case        =						{0: """batch""", 1: """decoder_sequence"""}
											if self.use_past:
																self.fill_with_past_key_values_(lowercase   ,     direction="""inputs"""						)
						elif self.task == "causal-lm":
											__snake_case        =						OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											    ]						)
											if self.use_past:
																__snake_case   ,   __snake_case        =						self.num_layers
																for i in range(lowercase						):
																					__snake_case        =						{0: """batch""", 2: """past_sequence + sequence"""}
																					__snake_case        =						{0: """batch""", 2: """past_sequence + sequence"""}
						else:
											__snake_case        =						OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
											        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
											        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
											    ]						)

						return common_inputs




	@property
	# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
	def 				a_		( self	:						str						)       ->			Mapping[str, Mapping[int, str]]:
						if self.task in ["default", "seq2seq-lm"]:
											__snake_case        =						super().outputs
						else:
											__snake_case        =						super(lowercase   ,     self						).outputs
											if self.use_past:
																__snake_case   ,   __snake_case        =						self.num_layers
																for i in range(lowercase						):
																					__snake_case        =						{0: """batch""", 2: """past_sequence + sequence"""}
																					__snake_case        =						{0: """batch""", 2: """past_sequence + sequence"""}
						return common_outputs




	def 				a_		( self	:						Any   ,     lowercase	:						PreTrainedTokenizer   ,     lowercase	:						int = -1   ,     lowercase	:						int = -1   ,     lowercase	:						bool = False   ,     lowercase	:						Optional[TensorType] = None   ,     )       ->			Mapping[str, Any]:
						__snake_case        =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowercase   ,     lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)
						# Generate decoder inputs
						__snake_case        =						seq_length if not self.use_past else 1
						__snake_case        =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowercase   ,     lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)
						__snake_case        =						{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						__snake_case        =						dict(**lowercase   ,     **lowercase						)

						if self.use_past:
											if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
											else:
																import torch
											__snake_case   ,   __snake_case        =						common_inputs["""input_ids"""].shape
											__snake_case        =						common_inputs["""decoder_input_ids"""].shape[1]
											__snake_case   ,   __snake_case        =						self.num_attention_heads
											__snake_case        =						(
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											__snake_case        =						decoder_seq_length
											__snake_case        =						(
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)
											__snake_case        =						torch.cat(
											    [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase   ,     lowercase						)]   ,     dim=1						)
											__snake_case        =						[]
											__snake_case   ,   __snake_case        =						self.num_layers

											for _ in range(lowercase						):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(lowercase						),
																        torch.zeros(lowercase						),
																        torch.zeros(lowercase						),
																        torch.zeros(lowercase						),
																    )						)
						return common_inputs




	def 				a_		( self	:						str   ,     lowercase	:						PreTrainedTokenizer   ,     lowercase	:						int = -1   ,     lowercase	:						int = -1   ,     lowercase	:						bool = False   ,     lowercase	:						Optional[TensorType] = None   ,     )       ->			Mapping[str, Any]:
						__snake_case        =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowercase   ,     lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)

						if self.use_past:
											if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
											else:
																import torch
											__snake_case   ,   __snake_case        =						common_inputs["""input_ids"""].shape
											__snake_case        =						seqlen
											__snake_case   ,   __snake_case        =						self.num_layers
											__snake_case   ,   __snake_case        =						self.num_attention_heads
											__snake_case        =						(
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											__snake_case        =						common_inputs["""attention_mask"""].dtype
											__snake_case        =						torch.cat(
											    [common_inputs["""attention_mask"""], torch.ones(lowercase   ,     lowercase   ,     dtype=lowercase						)]   ,     dim=1						)
											__snake_case        =						[
											    (torch.zeros(lowercase						), torch.zeros(lowercase						)) for _ in range(lowercase						)
											]
						return common_inputs




	def 				a_		( self	:						Optional[int]   ,     lowercase	:						PreTrainedTokenizer   ,     lowercase	:						int = -1   ,     lowercase	:						int = -1   ,     lowercase	:						bool = False   ,     lowercase	:						Optional[TensorType] = None   ,     )       ->			Mapping[str, Any]:
						# Copied from OnnxConfig.generate_dummy_inputs
						# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__snake_case        =						compute_effective_axis_dimension(
						    lowercase   ,     fixed_dimension=OnnxConfig.default_fixed_batch   ,     num_token_to_add=0						)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__snake_case        =						tokenizer.num_special_tokens_to_add(lowercase						)
						__snake_case        =						compute_effective_axis_dimension(
						    lowercase   ,     fixed_dimension=OnnxConfig.default_fixed_sequence   ,     num_token_to_add=lowercase						)

						# Generate dummy inputs according to compute batch and sequence
						__snake_case        =						[""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
						__snake_case        =						dict(tokenizer(lowercase   ,     return_tensors=lowercase						)						)
						return common_inputs




	def 				a_		( self	:						Tuple   ,     lowercase	:						PreTrainedTokenizer   ,     lowercase	:						int = -1   ,     lowercase	:						int = -1   ,     lowercase	:						bool = False   ,     lowercase	:						Optional[TensorType] = None   ,     )       ->			Mapping[str, Any]:
						if self.task in ["default", "seq2seq-lm"]:
											__snake_case        =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    lowercase   ,     batch_size=lowercase   ,     seq_length=lowercase   ,     is_pair=lowercase   ,     framework=lowercase						)

						elif self.task == "causal-lm":
											__snake_case        =						self._generate_dummy_inputs_for_causal_lm(
											    lowercase   ,     batch_size=lowercase   ,     seq_length=lowercase   ,     is_pair=lowercase   ,     framework=lowercase						)
						else:
											__snake_case        =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    lowercase   ,     batch_size=lowercase   ,     seq_length=lowercase   ,     is_pair=lowercase   ,     framework=lowercase						)

						return common_inputs




	def 				a_		( self	:						Optional[int]   ,     lowercase	:						Tuple   ,     lowercase	:						Any   ,     lowercase	:						List[Any]   ,     lowercase	:						Optional[Any]						)       ->			List[Any]:
						if self.task in ["default", "seq2seq-lm"]:
											__snake_case        =						super()._flatten_past_key_values_(lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)
						else:
											__snake_case        =						super(lowercase   ,     self						)._flatten_past_key_values_(
											    lowercase   ,     lowercase   ,     lowercase   ,     lowercase						)




	def 				a_		( self	:						Any   ,     lowercase	:						Mapping[str, Mapping[int, str]]   ,     lowercase	:						str						)       ->			str:
						if direction not in ["inputs", "outputs"]:
											raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''						)

						__snake_case        =						"""past_key_values""" if direction == """inputs""" else """present"""
						__snake_case   ,   __snake_case        =						self.num_layers

						__snake_case        =						"""past_encoder_sequence"""
						__snake_case        =						"""past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

						for i in range(lowercase						):
											__snake_case        =						{0: """batch""", 2: decoder_sequence}
											__snake_case        =						{0: """batch""", 2: decoder_sequence}
											__snake_case        =						{0: """batch""", 2: encoder_sequence}
											__snake_case        =						{0: """batch""", 2: encoder_sequence}








"""simple docstring"""

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class      A__   (   utils.Parser  ):
	A_				:					str      =   "halfcheetah-medium-expert-v2"
	A_				:					str      =   "config.offline"




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]		):
					__snake_case   ,   __snake_case        =						utils.load_model(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       epoch=lowerCAmelCase					,       device=lowerCAmelCase		)
					__snake_case        =						TrajectoryTransformerModel(gpt.config		)

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


