

import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase							:  int            =						logging.getLogger(__name__)



@dataclass
class   __lowercase    :




			"""simple docstring"""







			_UpperCAmelCase       :							str      							=					field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
			_UpperCAmelCase       :							Optional[str]      							=					field(
			    default=UpperCAmelCase_		,	metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
			_UpperCAmelCase       :							Optional[str]      							=					field(
			    default='''NER'''		,	metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}     )
			_UpperCAmelCase       :							Optional[str]      							=					field(
			    default=UpperCAmelCase_		,	metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
			_UpperCAmelCase       :							bool      							=					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.
			_UpperCAmelCase       :							Optional[str]      							=					field(
			    default=UpperCAmelCase_		,	metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,	)







@dataclass
class   __lowercase    :




			"""simple docstring"""







			_UpperCAmelCase       :							str      							=					field(
			    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}     )
			_UpperCAmelCase       :							Optional[str]      							=					field(
			    default=UpperCAmelCase_		,	metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		,	)
			_UpperCAmelCase       :							int      							=					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.'''
			        )
			    }		,	)
			_UpperCAmelCase       :							bool      							=					field(
			    default=UpperCAmelCase_		,	metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )







def       A_				(   ):
						# 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.

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
						if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
						else:
												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Dict  		=	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."   )

						SCREAMING_SNAKE_CASE_:			Any  		=	import_module("tasks"   )
						try:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	getattr(_UpperCAmelCase    ,     model_args.task_type   )
												SCREAMING_SNAKE_CASE_:			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 if training_args.local_rank in [-1, 0] else logging.WARN    ,     )
						logger.warning(
						    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,     training_args.local_rank    ,     training_args.device    ,     training_args.n_gpu    ,     bool(training_args.local_rank != -1   )    ,     training_args.fpaa    ,     )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank   ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info("Training/evaluation parameters %s"    ,     _UpperCAmelCase   )

						# Set seed
						set_seed(training_args.seed   )

						# Prepare CONLL-2003 task
						SCREAMING_SNAKE_CASE_:			List[str]  		=	token_classification_task.get_labels(data_args.labels   )
						SCREAMING_SNAKE_CASE_:			Dict[int, str]  		=	dict(enumerate(_UpperCAmelCase   )   )
						SCREAMING_SNAKE_CASE_:			Any  		=	len(_UpperCAmelCase   )

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

						SCREAMING_SNAKE_CASE_:			Tuple  		=	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    ,     )
						SCREAMING_SNAKE_CASE_:			Union[str, 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    ,     )
						SCREAMING_SNAKE_CASE_:			Any  		=	AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path    ,     from_tf=bool(".ckpt" in model_args.model_name_or_path   )    ,     config=_UpperCAmelCase    ,     cache_dir=model_args.cache_dir    ,     )

						# Get datasets
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	(
						    TokenClassificationDataset(
						        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
						)
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	(
						    TokenClassificationDataset(
						        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    ,     _UpperCAmelCase   ) -> Tuple[List[int], List[int]]:
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.argmax(_UpperCAmelCase    ,     axis=2   )

												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			str  		=	preds.shape

												SCREAMING_SNAKE_CASE_:			List[Any]  		=	[[] for _ in range(_UpperCAmelCase   )]
												SCREAMING_SNAKE_CASE_:			int  		=	[[] for _ in range(_UpperCAmelCase   )]

												for i in range(_UpperCAmelCase   ):
																		for j in range(_UpperCAmelCase   ):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														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   ) -> Dict:
												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	align_predictions(p.predictions    ,     p.label_ids   )
												return {
												    "accuracy_score": accuracy_score(_UpperCAmelCase    ,     _UpperCAmelCase   ),
												    "precision": precision_score(_UpperCAmelCase    ,     _UpperCAmelCase   ),
												    "recall": recall_score(_UpperCAmelCase    ,     _UpperCAmelCase   ),
												    "f1": fa_score(_UpperCAmelCase    ,     _UpperCAmelCase   ),
												}

						# Data collator
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	DataCollatorWithPadding(_UpperCAmelCase    ,     pad_to_multiple_of=8   ) if training_args.fpaa else None

						# Initialize our Trainer
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	Trainer(
						    model=_UpperCAmelCase    ,     args=_UpperCAmelCase    ,     train_dataset=_UpperCAmelCase    ,     eval_dataset=_UpperCAmelCase    ,     compute_metrics=_UpperCAmelCase    ,     data_collator=_UpperCAmelCase    ,     )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
						SCREAMING_SNAKE_CASE_:			Dict  		=	{}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"   )

												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	trainer.evaluate()

												SCREAMING_SNAKE_CASE_:			Tuple  		=	os.path.join(training_args.output_dir    ,     "eval_results.txt"   )
												if trainer.is_world_process_zero():
																		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:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	TokenClassificationDataset(
												    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    ,     )

												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	trainer.predict(_UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[Any]  		=	align_predictions(_UpperCAmelCase    ,     _UpperCAmelCase   )

												SCREAMING_SNAKE_CASE_:			List[Any]  		=	os.path.join(training_args.output_dir    ,     "test_results.txt"   )
												if trainer.is_world_process_zero():
																		with open(_UpperCAmelCase    ,     "w"   ) as writer:
																								for key, value in metrics.items():
																														logger.info("  %s = %s"    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
																														writer.write("%s = %s\n" % (key, value)   )

        # Save predictions
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	os.path.join(training_args.output_dir    ,     "test_predictions.txt"   )
												if trainer.is_world_process_zero():
																		with open(_UpperCAmelCase    ,     "w"   ) as writer:
																								with open(os.path.join(data_args.data_dir    ,     "test.txt"   )    ,     "r"   ) as f:
																														token_classification_task.write_predictions_to_file(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )

						return results







def       A_				(   _UpperCAmelCase   ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()









# Copyright 2022 The HuggingFace 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.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase							:  List[str]            =						"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""







def       A_				(   _UpperCAmelCase=None   ):
						if subparsers is not None:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	subparsers.add_parser("tpu-config"    ,     description=_description   )
						else:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	argparse.ArgumentParser("Accelerate tpu-config command"    ,     description=_description   )
						# Core arguments
						SCREAMING_SNAKE_CASE_:			Any  		=	parser.add_argument_group(
						    "Config Arguments"    ,     "Arguments that can be configured through `accelerate config`."   )
						config_args.add_argument(
						    "--config_file"    ,     type=_UpperCAmelCase    ,     default=_UpperCAmelCase    ,     help="Path to the config file to use for accelerate."    ,     )
						config_args.add_argument(
						    "--tpu_name"    ,     default=_UpperCAmelCase    ,     help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."    ,     )
						config_args.add_argument(
						    "--tpu_zone"    ,     default=_UpperCAmelCase    ,     help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."    ,     )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	parser.add_argument_group("TPU Arguments"    ,     "Arguments for options ran inside the TPU."   )
						pod_args.add_argument(
						    "--use_alpha"    ,     action="store_true"    ,     help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."    ,     )
						pod_args.add_argument(
						    "--command_file"    ,     default=_UpperCAmelCase    ,     help="The path to the file containing the commands to run on the pod on startup."    ,     )
						pod_args.add_argument(
						    "--command"    ,     action="append"    ,     nargs="+"    ,     help="A command to run on the pod. Can be passed multiple times."    ,     )
						pod_args.add_argument(
						    "--install_accelerate"    ,     action="store_true"    ,     help="Whether to install accelerate on the pod. Defaults to False."    ,     )
						pod_args.add_argument(
						    "--accelerate_version"    ,     default="latest"    ,     help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."    ,     )
						pod_args.add_argument(
						    "--debug"    ,     action="store_true"    ,     help="If set, will print the command that would be run instead of running it."   )

						if subparsers is not None:
												parser.set_defaults(func=_UpperCAmelCase   )
						return parser







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	None

						# Get the default from the config file if it exists.
						if args.config_file is not None or os.path.isfile(_UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_config_from_file(args.config_file   )
												if not args.command_file and defaults.command_file is not None and not args.command:
																		SCREAMING_SNAKE_CASE_:			Any  		=	defaults.command_file
												if not args.command and defaults.commands is not None:
																		SCREAMING_SNAKE_CASE_:			int  		=	defaults.commands
												if not args.tpu_name:
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	defaults.tpu_name
												if not args.tpu_zone:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	defaults.tpu_zone
						if args.accelerate_version == "dev":
												SCREAMING_SNAKE_CASE_:			Tuple  		=	"git+https://github.com/huggingface/accelerate.git"
						elif args.accelerate_version == "latest":
												SCREAMING_SNAKE_CASE_:			str  		=	"accelerate -U"
						elif isinstance(parse(args.accelerate_version   )    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	f"accelerate=={args.accelerate_version}"

						if not args.command_file and not args.command:
												raise ValueError("You must specify either a command file or a command to run on the pod."   )

						if args.command_file:
												with open(args.command_file    ,     "r"   ) as f:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	[f.read().splitlines()]

    # To turn list of lists into list of strings
						if isinstance(args.command[0]    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	[line for cmd in args.command for line in cmd]
						# Default to the shared folder and install accelerate
						SCREAMING_SNAKE_CASE_:			Dict  		=	["cd /usr/share"]
						if args.install_accelerate:
												new_cmd += [f"pip install {args.accelerate_version}"]
						new_cmd += args.command
						SCREAMING_SNAKE_CASE_:			List[str]  		=	"; ".join(_UpperCAmelCase   )

						# Then send it to gcloud
						# Eventually try to use google-api-core to do this instead of subprocess
						SCREAMING_SNAKE_CASE_:			int  		=	["gcloud"]
						if args.use_alpha:
												cmd += ["alpha"]
						cmd += [
						    "compute",
						    "tpus",
						    "tpu-vm",
						    "ssh",
						    args.tpu_name,
						    "--zone",
						    args.tpu_zone,
						    "--command",
						    args.command,
						    "--worker",
						    "all",
						]
						if args.debug:
												print(f"Running {' '.join(_UpperCAmelCase   )}"   )
												return
						subprocess.run(_UpperCAmelCase   )
						print("Successfully setup pod."   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	tpu_command_parser()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	parser.parse_args()

						tpu_command_launcher(_UpperCAmelCase   )






