



import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__     = get_tests_dir("fixtures/test_sentencepiece.model")

if is_sentencepiece_available():
							import sentencepiece as sp


UpperCAmelCase__     = 5
UpperCAmelCase__     = 10







@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  SpeechaTextTokenizer
			UpperCamelCase           =  False
			UpperCamelCase           =  True



			def 					_lowerCamelCase (		self     :						Tuple)     ->					List[str]:



						"""simple docstring"""






						super().setUp()

						_UpperCAmelCase	 =     sp.SentencePieceProcessor()
						spm_model.Load(A)
						_UpperCAmelCase	 =     ['<s>', '<pad>', '</s>', '<unk>']

						vocab += [spm_model.IdToPiece(id_) for id_ in range(len(A))]
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))

						_UpperCAmelCase	 =     Path(self.tmpdirname)
						save_json(A				,	save_dir / VOCAB_FILES_NAMES['vocab_file'])
						if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
									copyfile(A				,	save_dir / VOCAB_FILES_NAMES['spm_file'])

						_UpperCAmelCase	 =     SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
						tokenizer.save_pretrained(self.tmpdirname)



			def 					_lowerCamelCase (		self     :						Tuple)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     '<pad>'
						_UpperCAmelCase	 =     1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(A)				,	A)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(A)				,	A)



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]				,	'<s>')
						self.assertEqual(vocab_keys[1]				,	'<pad>')
						self.assertEqual(vocab_keys[-1]				,	'j')
						self.assertEqual(len(A)				,	10_01)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size				,	10_01)



			def 					_lowerCamelCase (		self     :						str)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     SpeechaTextTokenizer.from_pretrained(self.tmpdirname)

						_UpperCAmelCase	 =     tokenizer.tokenize('This is a test')
						self.assertListEqual(A				,	['▁This', '▁is', '▁a', '▁t', 'est'])

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(A)				,	[2_89, 50, 14, 1_74, 3_86]				,	)

						_UpperCAmelCase	 =     tokenizer.tokenize('I was born in 92000, and this is falsé.')
						self.assertListEqual(
						    A				,	[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']				,	)
						_UpperCAmelCase	 =     tokenizer.convert_tokens_to_ids(A)
						self.assertListEqual(A				,	[12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8])

						_UpperCAmelCase	 =     tokenizer.convert_ids_to_tokens(A)
						self.assertListEqual(
						    A				,	[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']				,	)



			@slow
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     {'input_ids': [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=A				,	model_name='facebook/s2t-small-mustc-en-de-st'				,	revision='a14f04cf0776c02f62a8cb800cf7909e15ea23ad'				,	)







@require_sentencepiece
class     __lowerCAmelCase					(    unittest.TestCase		):
			UpperCamelCase           =  '''valhalla/s2t_mustc_multilinguial_medium'''

			UpperCamelCase           =  '''C\'est trop cool'''
			UpperCamelCase           =  '''Esto es genial'''



			@classmethod
			def 					_lowerCamelCase (		cls     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name)
						return cls



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Dict:



						"""simple docstring"""






						self.assertEqual(self.tokenizer.lang_code_to_id['pt']				,	4)
						self.assertEqual(self.tokenizer.lang_code_to_id['ru']				,	6)
						self.assertEqual(self.tokenizer.lang_code_to_id['it']				,	9)
						self.assertEqual(self.tokenizer.lang_code_to_id['de']				,	11)



			def 					_lowerCamelCase (		self     :						Dict)     ->					Optional[Any]:



						"""simple docstring"""






						self.assertEqual(self.tokenizer.vocab_size				,	1_00_00)



			def 					_lowerCamelCase (		self     :						str)     ->					Tuple:



						"""simple docstring"""






						self.assertIn(A				,	self.tokenizer.all_special_ids)
						_UpperCAmelCase	 =     [ES_CODE, 4, 16_01, 47, 76_47, 2]
						_UpperCAmelCase	 =     self.tokenizer.decode(A				,	skip_special_tokens=A)
						_UpperCAmelCase	 =     self.tokenizer.decode(generated_ids[1:]				,	skip_special_tokens=A)
						self.assertEqual(A				,	A)
						self.assertNotIn(self.tokenizer.eos_token				,	A)



			def 					_lowerCamelCase (		self     :						str)     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     'fr'
						_UpperCAmelCase	 =     self.tokenizer(self.french_text).input_ids
						self.assertEqual(encoded[0]				,	A)
						self.assertEqual(encoded[-1]				,	self.tokenizer.eos_token_id)



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     'fr'
						self.assertListEqual(self.tokenizer.prefix_tokens				,	[FR_CODE])

						_UpperCAmelCase	 =     'es'
						self.assertListEqual(self.tokenizer.prefix_tokens				,	[ES_CODE])





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase__     = logging.getLogger(__name__)
UpperCAmelCase__     = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase__     = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A		)}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''Override some existing default config settings when a model is trained from scratch. Example: '''
			            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}       ,		)
			UpperCamelCase           =  field(
			    default='''main'''       ,		metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }       ,		)



			def 					_lowerCamelCase (		self     :						Any)     ->					Dict:



						"""simple docstring"""






						if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
									raise ValueError(
									    '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path')







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}		)
			UpperCamelCase           =  field(default=A       ,		metadata={'''help''': '''The input training data file (a text file).'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)
			UpperCamelCase           =  field(
			    default=5       ,		metadata={
			        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated. Default to the max input length of the model.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The number of processes to use for the preprocessing.'''}       ,		)
			UpperCamelCase           =  field(
			    default=0.15       ,		metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''Whether to pad all samples to `max_seq_length`. '''
			            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
			        )
			    }       ,		)



			def 					_lowerCamelCase (		self     :						Dict)     ->					Union[str, Any]:



						"""simple docstring"""






						if self.train_file is not None:
									_UpperCAmelCase	 =     self.train_file.split('.')[-1]
									assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
						if self.validation_file is not None:
									_UpperCAmelCase	 =     self.validation_file.split('.')[-1]
									assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Optional[Any]:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'r'							,     encoding='utf-8'					) as f:
						_UpperCAmelCase	 =     [json.loads(_UpperCAmelCase					) for line in f.read().splitlines() if (len(_UpperCAmelCase					) > 0 and not line.isspace())]
			assert len(_UpperCAmelCase					) == len(_UpperCAmelCase					)

			_UpperCAmelCase	 =     {c: dataset[c] for c in dataset.column_names}
			_UpperCAmelCase	 =     refs
			return Dataset.from_dict(_UpperCAmelCase					)





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			# 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.

			_UpperCAmelCase	 =     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.
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
			else:
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			_UpperCAmelCase	 =     None
			if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
						_UpperCAmelCase	 =     get_last_checkpoint(training_args.output_dir					)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
									raise ValueError(
									    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
									    'Use --overwrite_output_dir to overcome.'					)
						elif last_checkpoint is not None:
									logger.info(
									    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
									    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     handlers=[logging.StreamHandler(sys.stdout					)]							,     )
			logger.setLevel(logging.INFO if is_main_process(training_args.local_rank					) else logging.WARN					)

			# Log on each process the small summary:
			logger.warning(
			    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
			    + F"distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {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 before initializing model.
			set_seed(training_args.seed					)

			# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
			# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
			# (the dataset will be downloaded automatically from the datasets Hub).
			#
			# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
			# 'text' is found. You can easily tweak this behavior (see below).
			#
			# In distributed training, the load_dataset function guarantee that only one local process can concurrently
			# download the dataset.
			if data_args.dataset_name is not None:
						# Downloading and loading a dataset from the hub.
						_UpperCAmelCase	 =     load_dataset(data_args.dataset_name							,     data_args.dataset_config_name					)
						if "validation" not in datasets.keys():
									_UpperCAmelCase	 =     load_dataset(
									    data_args.dataset_name							,     data_args.dataset_config_name							,     split=F"train[:{data_args.validation_split_percentage}%]"							,     )
									_UpperCAmelCase	 =     load_dataset(
									    data_args.dataset_name							,     data_args.dataset_config_name							,     split=F"train[{data_args.validation_split_percentage}%:]"							,     )
			else:
						_UpperCAmelCase	 =     {}
						if data_args.train_file is not None:
									_UpperCAmelCase	 =     data_args.train_file
						if data_args.validation_file is not None:
									_UpperCAmelCase	 =     data_args.validation_file
						_UpperCAmelCase	 =     data_args.train_file.split('.'					)[-1]
						if extension == "txt":
									_UpperCAmelCase	 =     'text'
						_UpperCAmelCase	 =     load_dataset(_UpperCAmelCase							,     data_files=_UpperCAmelCase					)
			# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
			# https://huggingface.co/docs/datasets/loading_datasets.html.

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			_UpperCAmelCase	 =     {
			    'cache_dir': model_args.cache_dir,
			    'revision': model_args.model_revision,
			    'use_auth_token': True if model_args.use_auth_token else None,
			}
			if model_args.config_name:
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(model_args.config_name							,     **_UpperCAmelCase					)
			elif model_args.model_name_or_path:
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(model_args.model_name_or_path							,     **_UpperCAmelCase					)
			else:
						_UpperCAmelCase	 =     CONFIG_MAPPING[model_args.model_type]()
						logger.warning('You are instantiating a new config instance from scratch.'					)
						if model_args.config_overrides is not None:
									logger.info(F"Overriding config: {model_args.config_overrides}"					)
									config.update_from_string(model_args.config_overrides					)
									logger.info(F"New config: {config}"					)

			_UpperCAmelCase	 =     {
			    'cache_dir': model_args.cache_dir,
			    'use_fast': model_args.use_fast_tokenizer,
			    'revision': model_args.model_revision,
			    'use_auth_token': True if model_args.use_auth_token else None,
			}
			if model_args.tokenizer_name:
						_UpperCAmelCase	 =     AutoTokenizer.from_pretrained(model_args.tokenizer_name							,     **_UpperCAmelCase					)
			elif model_args.model_name_or_path:
						_UpperCAmelCase	 =     AutoTokenizer.from_pretrained(model_args.model_name_or_path							,     **_UpperCAmelCase					)
			else:
						raise ValueError(
						    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
						    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'					)

			if model_args.model_name_or_path:
						_UpperCAmelCase	 =     AutoModelForMaskedLM.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							,     revision=model_args.model_revision							,     use_auth_token=True if model_args.use_auth_token else None							,     )
			else:
						logger.info('Training new model from scratch'					)
						_UpperCAmelCase	 =     AutoModelForMaskedLM.from_config(_UpperCAmelCase					)

			model.resize_token_embeddings(len(_UpperCAmelCase					)					)

			# Preprocessing the datasets.
			# First we tokenize all the texts.
			if training_args.do_train:
						_UpperCAmelCase	 =     datasets['train'].column_names
			else:
						_UpperCAmelCase	 =     datasets['validation'].column_names
			_UpperCAmelCase	 =     'text' if 'text' in column_names else column_names[0]

			_UpperCAmelCase	 =     'max_length' if data_args.pad_to_max_length else False

			def tokenize_function(_UpperCAmelCase       :     str					):
						# Remove empty lines
						_UpperCAmelCase	 =     [line for line in examples['text'] if len(_UpperCAmelCase					) > 0 and not line.isspace()]
						return tokenizer(examples['text']							,     padding=_UpperCAmelCase							,     truncation=_UpperCAmelCase							,     max_length=data_args.max_seq_length					)

			_UpperCAmelCase	 =     datasets.map(
			    _UpperCAmelCase							,     batched=_UpperCAmelCase							,     num_proc=data_args.preprocessing_num_workers							,     remove_columns=[text_column_name]							,     load_from_cache_file=not data_args.overwrite_cache							,     )

			# Add the chinese references if provided
			if data_args.train_ref_file is not None:
						_UpperCAmelCase	 =     add_chinese_references(tokenized_datasets['train']							,     data_args.train_ref_file					)
			if data_args.validation_ref_file is not None:
						_UpperCAmelCase	 =     add_chinese_references(
						    tokenized_datasets['validation']							,     data_args.validation_ref_file					)
			# If we have ref files, need to avoid it removed by trainer
			_UpperCAmelCase	 =     data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						_UpperCAmelCase	 =     False

			# Data collator
			# This one will take care of randomly masking the tokens.
			_UpperCAmelCase	 =     DataCollatorForWholeWordMask(tokenizer=_UpperCAmelCase							,     mlm_probability=data_args.mlm_probability					)

			# Initialize our Trainer
			_UpperCAmelCase	 =     Trainer(
			    model=_UpperCAmelCase							,     args=_UpperCAmelCase							,     train_dataset=tokenized_datasets['train'] if training_args.do_train else None							,     eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None							,     tokenizer=_UpperCAmelCase							,     data_collator=_UpperCAmelCase							,     )

			# Training
			if training_args.do_train:
						if last_checkpoint is not None:
									_UpperCAmelCase	 =     last_checkpoint
						elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path					):
									_UpperCAmelCase	 =     model_args.model_name_or_path
						else:
									_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     trainer.train(resume_from_checkpoint=_UpperCAmelCase					)
						trainer.save_model()  # Saves the tokenizer too for easy upload

						_UpperCAmelCase	 =     os.path.join(training_args.output_dir							,     'train_results.txt'					)
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase							,     'w'					) as writer:
												logger.info('***** Train results *****'					)
												for key, value in sorted(train_result.metrics.items()					):
															logger.info(F"  {key} = {value}"					)
															writer.write(F"{key} = {value}\n"					)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir							,     'trainer_state.json'					)					)

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

						_UpperCAmelCase	 =     trainer.evaluate()

						_UpperCAmelCase	 =     math.exp(eval_output['eval_loss']					)
						_UpperCAmelCase	 =     perplexity

						_UpperCAmelCase	 =     os.path.join(training_args.output_dir							,     'eval_results_mlm_wwm.txt'					)
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase							,     'w'					) as writer:
												logger.info('***** Eval results *****'					)
												for key, value in sorted(results.items()					):
															logger.info(F"  {key} = {value}"					)
															writer.write(F"{key} = {value}\n"					)

			return results





def 							A			(  _UpperCAmelCase       :     str					)       ->				Optional[Any]:







			'''simple docstring'''
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()
