



"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


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



@dataclass
class     __snake_case						:
							_a      				=			field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
							_a      				=			field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
							_a      				=			field(
							    default='''NER'''						,metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}						)
							_a      				=			field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
							_a      				=			field(default=UpperCamelCase_						,metadata={'''help''': '''Set this flag to use fast tokenization.'''}						)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							_a      				=			field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,)





@dataclass
class     __snake_case						:
							_a      				=			field(
							    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}						)
							_a      				=			field(
							    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}						)
							_a      				=			field(
							    default=128						,metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }						,)
							_a      				=			field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)





def       UpperCamelCase(  ):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.
		lowerCAmelCase_      :					Dict     =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
		lowerCAmelCase_      :					Any     =			parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir			)
		    and os.listdir(training_args.output_dir			)
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
				    ''' --overwrite_output_dir to overcome.'''			)

		lowerCAmelCase_      :					Dict     =			import_module('''tasks'''			)

		try:
				lowerCAmelCase_      :					Optional[Any]     =			getattr(__UpperCamelCase					,model_args.task_type			)
				lowerCAmelCase_      :					TokenClassificationTask     =			token_classification_task_clazz()
		except AttributeError:
				raise ValueError(
				    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
				    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""			)

		# Setup logging
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,datefmt='''%m/%d/%Y %H:%M:%S'''					,level=logging.INFO					,)
		logger.info(
		    '''n_replicas: %s, distributed training: %s, 16-bits training: %s'''					,training_args.n_replicas					,bool(training_args.n_replicas > 1			)					,training_args.fpaa					,)
		logger.info('''Training/evaluation parameters %s'''					,__UpperCamelCase			)

		# Prepare Token Classification task
		lowerCAmelCase_      :					int     =			token_classification_task.get_labels(data_args.labels			)
		lowerCAmelCase_      :					Dict[int, str]     =			dict(enumerate(__UpperCamelCase			)			)
		lowerCAmelCase_      :					str     =			len(__UpperCamelCase			)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		lowerCAmelCase_      :					str     =			AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,num_labels=__UpperCamelCase					,idalabel=__UpperCamelCase					,labelaid={label: i for i, label in enumerate(__UpperCamelCase			)}					,cache_dir=model_args.cache_dir					,)
		lowerCAmelCase_      :					List[Any]     =			AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,cache_dir=model_args.cache_dir					,use_fast=model_args.use_fast					,)

		with training_args.strategy.scope():
				lowerCAmelCase_      :					Union[str, Any]     =			TFAutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path					,from_pt=bool('''.bin''' in model_args.model_name_or_path			)					,config=__UpperCamelCase					,cache_dir=model_args.cache_dir					,)

		# Get datasets
		lowerCAmelCase_      :					List[Any]     =			(
		    TFTokenClassificationDataset(
		        token_classification_task=__UpperCamelCase					,data_dir=data_args.data_dir					,tokenizer=__UpperCamelCase					,labels=__UpperCamelCase					,model_type=config.model_type					,max_seq_length=data_args.max_seq_length					,overwrite_cache=data_args.overwrite_cache					,mode=Split.train					,)
		    if training_args.do_train
		    else None
		)
		lowerCAmelCase_      :					List[Any]     =			(
		    TFTokenClassificationDataset(
		        token_classification_task=__UpperCamelCase					,data_dir=data_args.data_dir					,tokenizer=__UpperCamelCase					,labels=__UpperCamelCase					,model_type=config.model_type					,max_seq_length=data_args.max_seq_length					,overwrite_cache=data_args.overwrite_cache					,mode=Split.dev					,)
		    if training_args.do_eval
		    else None
		)

		def align_predictions(__UpperCamelCase							:		np.ndarray					,__UpperCamelCase							:		np.ndarray			) -> Tuple[List[int], List[int]]:
				lowerCAmelCase_      :					str     =			np.argmax(__UpperCamelCase					,axis=2			)
				lowerCAmelCase_      :					List[str]     =			preds.shape
				lowerCAmelCase_      :					Dict     =			[[] for _ in range(__UpperCamelCase			)]
				lowerCAmelCase_      :					int     =			[[] for _ in range(__UpperCamelCase			)]

				for i in range(__UpperCamelCase			):
						for j in range(__UpperCamelCase			):
								if label_ids[i, j] != -100:
										out_label_list[i].append(label_map[label_ids[i][j]]			)
										preds_list[i].append(label_map[preds[i][j]]			)

				return preds_list, out_label_list

		def compute_metrics(__UpperCamelCase							:		EvalPrediction			) -> Dict:
				lowerCAmelCase_      :					Optional[int]     =			align_predictions(p.predictions					,p.label_ids			)

				return {
				    "precision": precision_score(__UpperCamelCase					,__UpperCamelCase			),
				    "recall": recall_score(__UpperCamelCase					,__UpperCamelCase			),
				    "f1": fa_score(__UpperCamelCase					,__UpperCamelCase			),
				}

		# Initialize our Trainer
		lowerCAmelCase_      :					Union[str, Any]     =			TFTrainer(
		    model=__UpperCamelCase					,args=__UpperCamelCase					,train_dataset=train_dataset.get_dataset() if train_dataset else None					,eval_dataset=eval_dataset.get_dataset() if eval_dataset else None					,compute_metrics=__UpperCamelCase					,)

		# Training
		if training_args.do_train:
				trainer.train()
				trainer.save_model()
				tokenizer.save_pretrained(training_args.output_dir			)

		# Evaluation
		lowerCAmelCase_      :					Any     =			{}
		if training_args.do_eval:
				logger.info('''*** Evaluate ***'''			)

				lowerCAmelCase_      :					int     =			trainer.evaluate()
				lowerCAmelCase_      :					Optional[Any]     =			os.path.join(training_args.output_dir					,'''eval_results.txt'''			)

				with open(__UpperCamelCase					,'''w'''			) as writer:
						logger.info('''***** Eval results *****'''			)

						for key, value in result.items():
								logger.info('''  %s = %s'''					,__UpperCamelCase					,__UpperCamelCase			)
								writer.write('''%s = %s\n''' % (key, value)			)

						results.update(__UpperCamelCase			)

    # Predict
		if training_args.do_predict:
				lowerCAmelCase_      :					Optional[Any]     =			TFTokenClassificationDataset(
				    token_classification_task=__UpperCamelCase					,data_dir=data_args.data_dir					,tokenizer=__UpperCamelCase					,labels=__UpperCamelCase					,model_type=config.model_type					,max_seq_length=data_args.max_seq_length					,overwrite_cache=data_args.overwrite_cache					,mode=Split.test					,)

				lowerCAmelCase_      :					Optional[Any]     =			trainer.predict(test_dataset.get_dataset()			)
				lowerCAmelCase_      :					int     =			align_predictions(__UpperCamelCase					,__UpperCamelCase			)
				lowerCAmelCase_      :					Tuple     =			classification_report(__UpperCamelCase					,__UpperCamelCase			)

				logger.info('''\n%s'''					,__UpperCamelCase			)

				lowerCAmelCase_      :					Optional[Any]     =			os.path.join(training_args.output_dir					,'''test_results.txt'''			)

				with open(__UpperCamelCase					,'''w'''			) as writer:
						writer.write('''%s\n''' % report			)

				# Save predictions
				lowerCAmelCase_      :					Union[str, Any]     =			os.path.join(training_args.output_dir					,'''test_predictions.txt'''			)

				with open(__UpperCamelCase					,'''w'''			) as writer:
						with open(os.path.join(data_args.data_dir					,'''test.txt'''			)					,'''r'''			) as f:
								lowerCAmelCase_      :					Tuple     =			0

								for line in f:
										if line.startswith('''-DOCSTART-'''			) or line == "" or line == "\n":
												writer.write(__UpperCamelCase			)

												if not preds_list[example_id]:
														example_id += 1
										elif preds_list[example_id]:
												lowerCAmelCase_      :					List[str]     =			line.split()[0] + ''' ''' + preds_list[example_id].pop(0			) + '''\n'''

												writer.write(__UpperCamelCase			)
										else:
												logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''					,line.split()[0]			)

		return results


if __name__ == "__main__":
						main()










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
						)





