



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()


UpperCamelCase_	       =   logging.getLogger(__name__)







@dataclass
class 							a_  :
						UpperCamelCase__      :						str				       =field(
						    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}				)
						UpperCamelCase__      :						Optional[str]				       =field(
						    default=_snake_case					, 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=_snake_case					, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
						UpperCamelCase__      :						bool				       =field(default=_snake_case					, 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=_snake_case					, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					, )







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







def 				A			(		)       ->       List[str]:



						'''simple docstring'''






						UpperCAmelCase_     							=     HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
						UpperCAmelCase_							,      UpperCAmelCase_							,      UpperCAmelCase_     							=     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.'''	)

						UpperCAmelCase_     							=     import_module('''tasks'''	)

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

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

						UpperCAmelCase_     							=     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							,					)
						UpperCAmelCase_     							=     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():
												UpperCAmelCase_     							=     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
						UpperCAmelCase_     							=     (
						    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
						)
						UpperCAmelCase_     							=     (
						    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]]:
												UpperCAmelCase_     							=     np.argmax(__UpperCAmelCase							,					axis=2	)
												UpperCAmelCase_							,      UpperCAmelCase_     							=     preds.shape
												UpperCAmelCase_     							=     [[] for _ in range(__UpperCAmelCase	)]
												UpperCAmelCase_     							=     [[] 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	) -> Dict:
												UpperCAmelCase_							,      UpperCAmelCase_     							=     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
						UpperCAmelCase_     							=     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
						UpperCAmelCase_     							=     {}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''	)

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

												UpperCAmelCase_							,      UpperCAmelCase_							,      UpperCAmelCase_     							=     trainer.predict(test_dataset.get_dataset()	)
												UpperCAmelCase_							,      UpperCAmelCase_     							=     align_predictions(__UpperCAmelCase							,					__UpperCAmelCase	)
												UpperCAmelCase_     							=     classification_report(__UpperCAmelCase							,					__UpperCAmelCase	)

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

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

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

												# Save predictions
												UpperCAmelCase_     							=     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:
																								UpperCAmelCase_     							=     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]:
																																				UpperCAmelCase_     							=     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()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_	       =   {
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "EsmForMaskedLM",
							    "EsmForSequenceClassification",
							    "EsmForTokenClassification",
							    "EsmModel",
							    "EsmPreTrainedModel",
							]
							UpperCamelCase_	       =   ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFEsmForMaskedLM",
							    "TFEsmForSequenceClassification",
							    "TFEsmForTokenClassification",
							    "TFEsmModel",
							    "TFEsmPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
							from .tokenization_esm import EsmTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_esm import (
														    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EsmForMaskedLM,
														    EsmForSequenceClassification,
														    EsmForTokenClassification,
														    EsmModel,
														    EsmPreTrainedModel,
														)
														from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_esm import (
														    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFEsmForMaskedLM,
														    TFEsmForSequenceClassification,
														    TFEsmForTokenClassification,
														    TFEsmModel,
														    TFEsmPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure)





