



"""simple docstring"""
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


__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      logging.getLogger(__name__)


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



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


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



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



def        lowerCAmelCase_(   )	->       Optional[Any]:
				# 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        =				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.
								_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
				else:
								_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				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        =				import_module('''tasks'''							)
				try:
								_lowerCamelCase        =				getattr(lowercase_						,   model_args.task_type							)
								_lowerCamelCase        =				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'''						,   lowercase_							)

				# Set seed
				set_seed(training_args.seed							)

				# Prepare CONLL-2003 task
				_lowerCamelCase        =				token_classification_task.get_labels(data_args.labels							)
				_lowerCamelCase        =				dict(enumerate(lowercase_							)							)
				_lowerCamelCase        =				len(lowercase_							)

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

				_lowerCamelCase        =				AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,   num_labels=lowercase_						,   idalabel=lowercase_						,   labelaid={label: i for i, label in enumerate(lowercase_							)}						,   cache_dir=model_args.cache_dir						,   )
				_lowerCamelCase        =				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						,   )
				_lowerCamelCase        =				AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path						,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)						,   config=lowercase_						,   cache_dir=model_args.cache_dir						,   )

				# Get datasets
				_lowerCamelCase        =				(
				    TokenClassificationDataset(
				        token_classification_task=lowercase_						,   data_dir=data_args.data_dir						,   tokenizer=lowercase_						,   labels=lowercase_						,   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        =				(
				    TokenClassificationDataset(
				        token_classification_task=lowercase_						,   data_dir=data_args.data_dir						,   tokenizer=lowercase_						,   labels=lowercase_						,   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(lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray							) -> Tuple[List[int], List[int]]:
								_lowerCamelCase        =				np.argmax(lowercase_						,   axis=2							)

								_lowerCamelCase  ,     _lowerCamelCase        =				preds.shape

								_lowerCamelCase        =				[[] for _ in range(lowercase_							)]
								_lowerCamelCase        =				[[] for _ in range(lowercase_							)]

								for i in range(lowercase_							):
												for j in range(lowercase_							):
																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(lowercase_     :							EvalPrediction							) -> Dict:
								_lowerCamelCase  ,     _lowerCamelCase        =				align_predictions(p.predictions						,   p.label_ids							)
								return {
								    "accuracy_score": accuracy_score(lowercase_						,   lowercase_							),
								    "precision": precision_score(lowercase_						,   lowercase_							),
								    "recall": recall_score(lowercase_						,   lowercase_							),
								    "f1": fa_score(lowercase_						,   lowercase_							),
								}

				# Data collator
				_lowerCamelCase        =				DataCollatorWithPadding(lowercase_						,   pad_to_multiple_of=8							) if training_args.fpaa else None

				# Initialize our Trainer
				_lowerCamelCase        =				Trainer(
				    model=lowercase_						,   args=lowercase_						,   train_dataset=lowercase_						,   eval_dataset=lowercase_						,   compute_metrics=lowercase_						,   data_collator=lowercase_						,   )

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

								_lowerCamelCase        =				trainer.evaluate()

								_lowerCamelCase        =				os.path.join(training_args.output_dir						,   '''eval_results.txt'''							)
								if trainer.is_world_process_zero():
												with open(lowercase_						,   '''w'''							) as writer:
																logger.info('''***** Eval results *****'''							)
																for key, value in result.items():
																				logger.info('''  %s = %s'''						,   lowercase_						,   lowercase_							)
																				writer.write('''%s = %s\n''' % (key, value)							)

												results.update(lowercase_							)

    # Predict
				if training_args.do_predict:
								_lowerCamelCase        =				TokenClassificationDataset(
								    token_classification_task=lowercase_						,   data_dir=data_args.data_dir						,   tokenizer=lowercase_						,   labels=lowercase_						,   model_type=config.model_type						,   max_seq_length=data_args.max_seq_length						,   overwrite_cache=data_args.overwrite_cache						,   mode=Split.test						,   )

								_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				trainer.predict(lowercase_							)
								_lowerCamelCase  ,     _lowerCamelCase        =				align_predictions(lowercase_						,   lowercase_							)

								_lowerCamelCase        =				os.path.join(training_args.output_dir						,   '''test_results.txt'''							)
								if trainer.is_world_process_zero():
												with open(lowercase_						,   '''w'''							) as writer:
																for key, value in metrics.items():
																				logger.info('''  %s = %s'''						,   lowercase_						,   lowercase_							)
																				writer.write('''%s = %s\n''' % (key, value)							)

        # Save predictions
								_lowerCamelCase        =				os.path.join(training_args.output_dir						,   '''test_predictions.txt'''							)
								if trainer.is_world_process_zero():
												with open(lowercase_						,   '''w'''							) as writer:
																with open(os.path.join(data_args.data_dir						,   '''test.txt'''							)						,   '''r'''							) as f:
																				token_classification_task.write_predictions_to_file(lowercase_						,   lowercase_						,   lowercase_							)

				return results



def        lowerCAmelCase_(   lowercase_     :							Dict							)	->       str:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Tuple    							=      ['''SpeechT5Tokenizer''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Any    							=      [
			    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SpeechT5ForSpeechToText''',
			    '''SpeechT5ForSpeechToSpeech''',
			    '''SpeechT5ForTextToSpeech''',
			    '''SpeechT5Model''',
			    '''SpeechT5PreTrainedModel''',
			    '''SpeechT5HifiGan''',
			]

if TYPE_CHECKING:
			from .configuration_speechta import (
			    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
			    SpeechTaConfig,
			    SpeechTaHifiGanConfig,
			)
			from .feature_extraction_speechta import SpeechTaFeatureExtractor
			from .processing_speechta import SpeechTaProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speechta import SpeechTaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speechta import (
						    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechTaForSpeechToSpeech,
						    SpeechTaForSpeechToText,
						    SpeechTaForTextToSpeech,
						    SpeechTaHifiGan,
						    SpeechTaModel,
						    SpeechTaPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE  :						int    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
