
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()


SCREAMING_SNAKE_CASE   :		Any					 =       logging.getLogger(__name__)






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






@dataclass
class 					__lowercase		:
					__magic_name__						:							str     	=	field(
					    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}       )
					__magic_name__						:							Optional[str]     	=	field(
					    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}       )
					__magic_name__						:							int     	=	field(
					    default=1_28							,       metadata={
					        '''help''': (
					            '''The maximum total input sequence length after tokenization. Sequences longer '''
					            '''than this will be truncated, sequences shorter will be padded.'''
					        )
					    }							,       )
					__magic_name__						:							bool     	=	field(
					    default=A							,       metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )



def        lowerCamelCase_  (   ):
				# 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.
				A_       	=  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
				A_	,					A_	,					A_       	=  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.'''     )

				A_       	=  import_module('''tasks'''     )

				try:
								A_       	=  getattr(__UpperCamelCase ,						model_args.task_type     )
								A_       	=  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
				A_       	=  token_classification_task.get_labels(data_args.labels     )
				A_       	=  dict(enumerate(__UpperCamelCase     )     )
				A_       	=  len(__UpperCamelCase     )

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

				A_       	=  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 ,						)
				A_       	=  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():
								A_       	=  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
				A_       	=  (
				    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
				)
				A_       	=  (
				    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 ,						__UpperCamelCase     ) -> Tuple[List[int], List[int]]:
								A_       	=  np.argmax(__UpperCamelCase ,						axis=2     )
								A_	,					A_       	=  preds.shape
								A_       	=  [[] for _ in range(__UpperCamelCase     )]
								A_       	=  [[] for _ in range(__UpperCamelCase     )]

								for i in range(__UpperCamelCase     ):
												for j in range(__UpperCamelCase     ):
																if label_ids[i, j] != -1_00:
																				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:
								A_	,					A_       	=  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
				A_       	=  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
				A_       	=  {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''     )

								A_       	=  trainer.evaluate()
								A_       	=  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:
								A_       	=  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 ,						)

								A_	,					A_	,					A_       	=  trainer.predict(test_dataset.get_dataset()     )
								A_	,					A_       	=  align_predictions(__UpperCamelCase ,						__UpperCamelCase     )
								A_       	=  classification_report(__UpperCamelCase ,						__UpperCamelCase     )

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

								A_       	=  os.path.join(training_args.output_dir ,						'''test_results.txt'''     )

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

								# Save predictions
								A_       	=  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:
																A_       	=  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]:
																								A_       	=  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 importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
						import torch

SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       logging.get_logger(__name__)



def        lowerCamelCase_  (   ):
				return importlib.util.find_spec('''deepspeed'''     ) is not None


if is_accelerate_available() and is_deepspeed_available():
						from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
						# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
						# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
						from builtins import object as DeepSpeedConfig






class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							Dict:





									'''simple docstring'''
									# set global weakref object
									set_hf_deepspeed_config(self						)
									dep_version_check('''accelerate'''						)
									dep_version_check('''deepspeed'''						)
									super().__init__(a__						)






class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							List[Any]:





									'''simple docstring'''
									super().__init__(a__						)
									A_       	=  None
									A_       	=  []


					def        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									if self._dtype is None:
													raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''						)
									return self._dtype


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[int]:





									'''simple docstring'''
									A_       	=  self.get_value(a__						)
									if val is None:
													return False
									else:
													return val == "auto"


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__=None       ,       a__=True						)		->							List[str]:





									'''simple docstring'''
									A_	,					A_       	=  self.find_config_node(a__						)
									if config is None:
													return

									if config.get(a__						) == "auto":
													A_       	=  hf_val
													return

									if not must_match:
													return

									A_       	=  config.get(a__						)
									if ds_val is not None and ds_val != hf_val:
													self.mismatches.append(F"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"						)

					__magic_name__						:							str     	=	partialmethod(A							,       must_match=A       )


					def        lowerCAmelCase_    (	self       ,       a__						)		->							str:





									'''simple docstring'''
									A_       	=  args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
									self.fill_match(
									    '''train_micro_batch_size_per_gpu'''       ,       args.per_device_train_batch_size       ,       '''per_device_train_batch_size'''						)
									self.fill_match('''gradient_accumulation_steps'''       ,       args.gradient_accumulation_steps       ,       '''gradient_accumulation_steps'''						)
									self.fill_match('''train_batch_size'''       ,       a__       ,       '''train_batch_size (calculated)'''						)
									self.fill_match('''gradient_clipping'''       ,       args.max_grad_norm       ,       '''max_grad_norm'''						)

									self.fill_match('''optimizer.params.lr'''       ,       args.learning_rate       ,       '''learning_rate'''						)
									self.fill_match('''optimizer.params.betas'''       ,       [args.adam_betaa, args.adam_betaa]       ,       '''adam_beta1+adam_beta2'''						)
									self.fill_match('''optimizer.params.eps'''       ,       args.adam_epsilon       ,       '''adam_epsilon'''						)
									self.fill_match('''optimizer.params.weight_decay'''       ,       args.weight_decay       ,       '''weight_decay'''						)

									self.fill_only('''scheduler.params.warmup_min_lr'''       ,       0						)  # not a trainer arg
									self.fill_match('''scheduler.params.warmup_max_lr'''       ,       args.learning_rate       ,       '''learning_rate'''						)
									# total_num_steps - will get set in trainer_config_finalize

									# fp16
									if args.fpaa or args.fpaa_full_eval:
													A_       	=  '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
									else:
													A_       	=  None

									if args.save_on_each_node:
													# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
													A_       	=  self.config.get('''checkpoint'''       ,       {}						)
													A_       	=  args.save_on_each_node

									# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
									# any here unless the user did the work
									self.fill_match(
									    '''fp16.enabled'''       ,       ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')       ,       '''fp16|fp16_full_eval+fp16_backend(amp)'''       ,       )

									# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
									# ZeRO features
									self.fill_match('''amp.enabled'''       ,       fpaa_backend == '''apex'''       ,       '''fp16+fp16_backend(apex)'''						)
									self.fill_match('''amp.opt_level'''       ,       args.fpaa_opt_level       ,       '''fp16_opt_level'''						)

									self.fill_match('''bf16.enabled'''       ,       (args.bfaa or args.bfaa_full_eval)       ,       '''bf16|bf16_full_eval'''						)

									# deepspeed's default mode is fp16 unless there is a config that says differently
									if self.is_true('''bf16.enabled'''						):
													A_       	=  torch.bfloataa
									elif self.is_false('''fp16.enabled'''						):
													A_       	=  torch.floataa
									else:
													A_       	=  torch.floataa


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__						)		->							Tuple:





									'''simple docstring'''
									A_       	=  [
									    '''zero_optimization.reduce_bucket_size''',
									    '''zero_optimization.stage3_prefetch_bucket_size''',
									    '''zero_optimization.stage3_param_persistence_threshold''',
									]
									A_       	=  [x for x in hidden_size_based_keys if self.is_auto(a__						)]

									if len(a__						) > 0:
													if hasattr(model.config       ,       '''hidden_size'''						):
																	A_       	=  model.config.hidden_size
													elif hasattr(model.config       ,       '''hidden_sizes'''						):
																	# if there are many hidden sizes pick the largest one
																	A_       	=  max(model.config.hidden_sizes						)
													else:
																	raise ValueError(
																	    '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
																	    '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
																	    F"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
																	    '''`auto` values for these keys with an integer value of your choice.'''						)

													self.fill_only('''zero_optimization.reduce_bucket_size'''       ,       hidden_size * hidden_size						)
													if self.is_zeroa():
																	# automatically assign the optimal config values based on model config
																	self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''       ,       0.9 * hidden_size * hidden_size						)
																	self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''       ,       1_0 * hidden_size						)

        # scheduler
									self.fill_match('''scheduler.params.total_num_steps'''       ,       a__       ,       '''num_training_steps (calculated)'''						)
									self.fill_match('''scheduler.params.warmup_num_steps'''       ,       args.get_warmup_steps(a__						)       ,       '''warmup_steps'''						)

									if len(self.mismatches						) > 0:
													A_       	=  '''\n'''.join(self.mismatches						)
													raise ValueError(
													    '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
													    F" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."						)
SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       None



def        lowerCamelCase_  (   __UpperCamelCase     ):
				# this is a special weakref global object to allow us to get to Deepspeed config from APIs
				# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
				global _hf_deepspeed_config_weak_ref
				# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
				A_       	=  weakref.ref(__UpperCamelCase     )



def        lowerCamelCase_  (   ):
				# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
				global _hf_deepspeed_config_weak_ref
				A_       	=  None



def        lowerCamelCase_  (   ):
				if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
								return _hf_deepspeed_config_weak_ref().is_zeroa()
				else:
								return False



def        lowerCamelCase_  (   ):
				if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
								return _hf_deepspeed_config_weak_ref().config
				else:
								return None



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				from accelerate.utils import DummyOptim, DummyScheduler

				A_       	=  hf_deepspeed_config.config

				# Optimizer + Scheduler
				# Currently supported combos:
				# 1. DS scheduler + DS optimizer: Yes
				# 2. HF scheduler + HF optimizer: Yes
				# 3. DS scheduler + HF optimizer: Yes
				# 4. HF scheduler + DS optimizer: No
				#
				# Unless Offload is enabled in which case it's:
				# 1. DS scheduler + DS optimizer: Yes
				# 2. HF scheduler + HF optimizer: Mostly*
				# 3. DS scheduler + HF optimizer: Mostly*
				# 4. HF scheduler + DS optimizer: No
				#
				# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

				A_       	=  None
				if "optimizer" in config:
								if args.adafactor:
												raise ValueError(
												    '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
												    '''Only one optimizer can be configured.'''     )
								A_       	=  DummyOptim(params=__UpperCamelCase     )
				else:
								if hf_deepspeed_config.is_offload():
												logger.info(
												    '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
												    ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''     )

								# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
								# But trainer uses AdamW by default.
								A_       	=  trainer.create_optimizer()
								# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
								A_       	=  True

				A_       	=  None
				if "scheduler" in config:
								A_       	=  DummyScheduler(__UpperCamelCase     )
				else:
								if isinstance(__UpperCamelCase ,						__UpperCamelCase     ):
												raise ValueError(
												    '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
												    '''Please configure a scheduler in the DeepSpeed config.'''     )
								A_       	=  trainer.create_scheduler(num_training_steps=__UpperCamelCase ,						optimizer=__UpperCamelCase     )

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase=False     ):
				from deepspeed.utils import logger as ds_logger

				A_       	=  trainer.model
				A_       	=  trainer.args

				A_       	=  trainer.accelerator.state.deepspeed_plugin.hf_ds_config

				# resume config update - some bits like `model` and `num_training_steps` only become available during train
				hf_deepspeed_config.trainer_config_finalize(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

				# set the Deepspeed log level consistent with the Trainer
				ds_logger.setLevel(args.get_process_log_level()     )

				if inference:
								# only Z3 makes sense for the inference
								if not hf_deepspeed_config.is_zeroa():
												raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''     )

								# in case the training config is re-used for inference
								hf_deepspeed_config.del_config_sub_tree('''optimizer'''     )
								hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''     )
								A_	,					A_       	=  None, None
								A_       	=  None
				else:
								A_       	=  None  # important for when deepspeed_init is used as re-init
								A_       	=  list(filter(lambda __UpperCamelCase     : p.requires_grad ,						model.parameters()     )     )
								A_	,					A_       	=  deepspeed_optim_sched(
								    __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

				# keep for quick debug:
				# from pprint import pprint; pprint(config)

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# it's possible that the user is trying to resume from model_path, which doesn't necessarily
				# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
				# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
				# path contains what looks like a deepspeed checkpoint
				import glob

				A_       	=  sorted(glob.glob(F"{checkpoint_path}/global_step*"     )     )

				if len(__UpperCamelCase     ) > 0:
								logger.info(F"Attempting to resume from {checkpoint_path}"     )
								# this magically updates self.optimizer and self.lr_scheduler
								A_	,					A_       	=  deepspeed_engine.load_checkpoint(
								    __UpperCamelCase ,						load_optimizer_states=__UpperCamelCase ,						load_lr_scheduler_states=__UpperCamelCase     )
								if load_path is None:
												raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"     )
				else:
								raise ValueError(F"Can't find a valid checkpoint at {checkpoint_path}"     )