






import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
					from transformers import WhisperFeatureExtractor

if is_torch_available():
					import torch

__snake_case    :    Union[str, Any]         =     random.Random()







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Dict=1.0    ,       UpperCamelCase_       :					List[Any]=None    ,       UpperCamelCase_       :					Union[str, Any]=None		)  ->   Tuple:

						"""simple docstring"""



						if rng is None:
												lowerCAmelCase__    						=						global_rng

						lowerCAmelCase__    						=						[]
						for batch_idx in range(shape[0]		):
												values.append([]		)
												for _ in range(shape[1]		):
																		values[-1].append(rng.random() * scale		)

						return values


@require_torch
@require_torchaudio
class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase=7     ,						_UpperCamelCase=4_00     ,						_UpperCamelCase=20_00     ,						_UpperCamelCase=10     ,						_UpperCamelCase=1_60     ,						_UpperCamelCase=8     ,						_UpperCamelCase=0.0     ,						_UpperCamelCase=40_00     ,						_UpperCamelCase=False     ,						_UpperCamelCase=True     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						parent
												lowerCAmelCase__    						=						batch_size
												lowerCAmelCase__    						=						min_seq_length
												lowerCAmelCase__    						=						max_seq_length
												lowerCAmelCase__    						=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												lowerCAmelCase__    						=						padding_value
												lowerCAmelCase__    						=						sampling_rate
												lowerCAmelCase__    						=						return_attention_mask
												lowerCAmelCase__    						=						do_normalize
												lowerCAmelCase__    						=						feature_size
												lowerCAmelCase__    						=						chunk_length
												lowerCAmelCase__    						=						hop_length






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return {
												    "feature_size": self.feature_size,
												    "hop_length": self.hop_length,
												    "chunk_length": self.chunk_length,
												    "padding_value": self.padding_value,
												    "sampling_rate": self.sampling_rate,
												    "return_attention_mask": self.return_attention_mask,
												    "do_normalize": self.do_normalize,
												}







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=False     ,						_UpperCamelCase=False   ):






												"""simple docstring"""
												def _flatten(_UpperCamelCase   ):
																		return list(itertools.chain(*_UpperCamelCase   )   )

												if equal_length:
																		lowerCAmelCase__    						=						[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
												else:
																		# make sure that inputs increase in size
																		lowerCAmelCase__    						=						[
																		    floats_list((x, self.feature_size)   )
																		    for x in range(self.min_seq_length     ,						self.max_seq_length     ,						self.seq_length_diff   )
																		]
												if numpify:
																		lowerCAmelCase__    						=						[np.asarray(_UpperCamelCase   ) for x in speech_inputs]
												return speech_inputs




@require_torch
@require_torchaudio
class     __SCREAMING_SNAKE_CASE					(   __lowercase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       List[str]   =					WhisperFeatureExtractor if is_speech_available() else None






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						WhisperFeatureExtractionTester(self   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.feature_extraction_class(**self.feat_extract_dict   )

												with tempfile.TemporaryDirectory() as tmpdirname:
																		lowerCAmelCase__    						=						feat_extract_first.save_pretrained(_UpperCamelCase   )[0]
																		check_json_file_has_correct_format(_UpperCamelCase   )
																		lowerCAmelCase__    						=						self.feature_extraction_class.from_pretrained(_UpperCamelCase   )

												lowerCAmelCase__    						=						feat_extract_first.to_dict()
												lowerCAmelCase__    						=						feat_extract_second.to_dict()
												lowerCAmelCase__    						=						feat_extract_first.mel_filters
												lowerCAmelCase__    						=						feat_extract_second.mel_filters
												self.assertTrue(np.allclose(_UpperCamelCase     ,						_UpperCamelCase   )   )
												self.assertEqual(_UpperCamelCase     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.feature_extraction_class(**self.feat_extract_dict   )

												with tempfile.TemporaryDirectory() as tmpdirname:
																		lowerCAmelCase__    						=						os.path.join(_UpperCamelCase     ,						'feat_extract.json'   )
																		feat_extract_first.to_json_file(_UpperCamelCase   )
																		lowerCAmelCase__    						=						self.feature_extraction_class.from_json_file(_UpperCamelCase   )

												lowerCAmelCase__    						=						feat_extract_first.to_dict()
												lowerCAmelCase__    						=						feat_extract_second.to_dict()
												lowerCAmelCase__    						=						feat_extract_first.mel_filters
												lowerCAmelCase__    						=						feat_extract_second.mel_filters
												self.assertTrue(np.allclose(_UpperCamelCase     ,						_UpperCamelCase   )   )
												self.assertEqual(_UpperCamelCase     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# Tests that all call wrap to encode_plus and batch_encode_plus
												lowerCAmelCase__    						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												# create three inputs of length 800, 1000, and 1200
												lowerCAmelCase__    						=						[floats_list((1, x)   )[0] for x in range(8_00     ,						14_00     ,						2_00   )]
												lowerCAmelCase__    						=						[np.asarray(_UpperCamelCase   ) for speech_input in speech_inputs]

												# Test feature size
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						padding='max_length'     ,						return_tensors='np'   ).input_features
												self.assertTrue(input_features.ndim == 3   )
												self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames   )
												self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size   )

												# Test not batched input
												lowerCAmelCase__    						=						feature_extractor(speech_inputs[0]     ,						return_tensors='np'   ).input_features
												lowerCAmelCase__    						=						feature_extractor(np_speech_inputs[0]     ,						return_tensors='np'   ).input_features
												self.assertTrue(np.allclose(_UpperCamelCase     ,						_UpperCamelCase     ,						atol=1E-3   )   )

												# Test batched
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='np'   ).input_features
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='np'   ).input_features
												for enc_seq_a, enc_seq_a in zip(_UpperCamelCase     ,						_UpperCamelCase   ):
																		self.assertTrue(np.allclose(_UpperCamelCase     ,						_UpperCamelCase     ,						atol=1E-3   )   )

												# Test 2-D numpy arrays are batched.
												lowerCAmelCase__    						=						[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
												lowerCAmelCase__    						=						np.asarray(_UpperCamelCase   )
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='np'   ).input_features
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='np'   ).input_features
												for enc_seq_a, enc_seq_a in zip(_UpperCamelCase     ,						_UpperCamelCase   ):
																		self.assertTrue(np.allclose(_UpperCamelCase     ,						_UpperCamelCase     ,						atol=1E-3   )   )

												# Test truncation required
												lowerCAmelCase__    						=						[floats_list((1, x)   )[0] for x in range(2_00     ,						(feature_extractor.n_samples + 5_00)     ,						2_00   )]
												lowerCAmelCase__    						=						[np.asarray(_UpperCamelCase   ) for speech_input in speech_inputs]

												lowerCAmelCase__    						=						[x[: feature_extractor.n_samples] for x in speech_inputs]
												lowerCAmelCase__    						=						[np.asarray(_UpperCamelCase   ) for speech_input in speech_inputs_truncated]

												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='np'   ).input_features
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='np'   ).input_features
												for enc_seq_a, enc_seq_a in zip(_UpperCamelCase     ,						_UpperCamelCase   ):
																		self.assertTrue(np.allclose(_UpperCamelCase     ,						_UpperCamelCase     ,						atol=1E-3   )   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												import torch

												lowerCAmelCase__    						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCAmelCase__    						=						np.random.rand(1_00     ,						32   ).astype(np.floataa   )
												lowerCAmelCase__    						=						np_speech_inputs.tolist()

												for inputs in [py_speech_inputs, np_speech_inputs]:
																		lowerCAmelCase__    						=						feature_extractor.pad([{'input_features': inputs}]     ,						return_tensors='np'   )
																		self.assertTrue(np_processed.input_features.dtype == np.floataa   )
																		lowerCAmelCase__    						=						feature_extractor.pad([{'input_features': inputs}]     ,						return_tensors='pt'   )
																		self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,						'clean'     ,						split='validation'   )
												# automatic decoding with librispeech
												lowerCAmelCase__    						=						ds.sort('id'   ).select(range(_UpperCamelCase   )   )[:num_samples]['audio']

												return [x["array"] for x in speech_samples]






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												# fmt: off
												lowerCAmelCase__    						=						torch.tensor(
												    [
												        0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
												        0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
												        0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
												        -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
												    ]   )
												# fmt: on

												lowerCAmelCase__    						=						self._load_datasamples(1   )
												lowerCAmelCase__    						=						WhisperFeatureExtractor()
												lowerCAmelCase__    						=						feature_extractor(_UpperCamelCase     ,						return_tensors='pt'   ).input_features
												self.assertEqual(input_features.shape     ,						(1, 80, 30_00)   )
												self.assertTrue(torch.allclose(input_features[0, 0, :30]     ,						_UpperCamelCase     ,						atol=1E-4   )   )







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCAmelCase__    						=						self._load_datasamples(1   )[0]
												lowerCAmelCase__    						=						((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
												lowerCAmelCase__    						=						feat_extract.zero_mean_unit_var_norm([audio]     ,						attention_mask=_UpperCamelCase   )[0]

												self.assertTrue(np.all(np.mean(_UpperCamelCase   ) < 1E-3   )   )
												self.assertTrue(np.all(np.abs(np.var(_UpperCamelCase   ) - 1   ) < 1E-3   )   )













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


__snake_case    :    List[Any]         =     logging.getLogger(__name__)


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




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







def 	_UpperCamelCase   (    )  ->   List[str]:

						"""simple docstring"""



						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(UpperCamelCase_    ,       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'    ,       UpperCamelCase_		)

						# Set seed
						set_seed(training_args.seed		)

						# Prepare CONLL-2003 task
						lowerCAmelCase__    						=						token_classification_task.get_labels(data_args.labels		)
						lowerCAmelCase__    						=						dict(enumerate(UpperCamelCase_		)		)
						lowerCAmelCase__    						=						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__    						=						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__    						=						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=UpperCamelCase_    ,       cache_dir=model_args.cache_dir    ,       )

						# Get datasets
						lowerCAmelCase__    						=						(
						    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
						)
						lowerCAmelCase__    						=						(
						    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_       :					np.ndarray    ,       UpperCamelCase_       :					np.ndarray		) -> Tuple[List[int], List[int]]:
												lowerCAmelCase__    						=						np.argmax(UpperCamelCase_    ,       axis=2		)

												lowerCAmelCase__						,       lowerCAmelCase__    						=						preds.shape

												lowerCAmelCase__    						=						[[] for _ in range(UpperCamelCase_		)]
												lowerCAmelCase__    						=						[[] 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_       :					EvalPrediction		) -> Dict:
												lowerCAmelCase__						,       lowerCAmelCase__    						=						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
						lowerCAmelCase__    						=						DataCollatorWithPadding(UpperCamelCase_    ,       pad_to_multiple_of=8		) if training_args.fpaa else None

						# Initialize our Trainer
						lowerCAmelCase__    						=						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
						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(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__    						=						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    ,       )

												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						trainer.predict(UpperCamelCase_		)
												lowerCAmelCase__						,       lowerCAmelCase__    						=						align_predictions(UpperCamelCase_    ,       UpperCamelCase_		)

												lowerCAmelCase__    						=						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
												lowerCAmelCase__    						=						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 	_UpperCamelCase   (    UpperCamelCase_       :					Dict		)  ->   Union[str, Any]:

						"""simple docstring"""



						main()


if __name__ == "__main__":
					main()





