
'''simple docstring'''
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


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







@dataclass
class   a_ :
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={
			        'help': (
			            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
			        )
			    }		,  )
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCamelCase__					)}		,  )
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  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'
			        )
			    }		,  )
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}		,  )
			lowerCamelCase__	:			bool       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}		,  )
			lowerCamelCase__	:			str       =    field(
			    default='main'		,  metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}		,  )
			lowerCamelCase__	:			bool       =    field(
			    default=UpperCamelCase__		,  metadata={
			        'help': (
			            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
			            'with private models).'
			        )
			    }		,  )




			def 					lowerCAmelCase__					(       self	):
				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   a_ :
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'The name of the dataset to use (via the datasets library).'}					)
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}					)
			lowerCamelCase__	:			Optional[str]       =    field(default=UpperCamelCase__		,  metadata={'help': 'The input training data file (a text file).'}					)
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}		,  )
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}		,  )
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}		,  )
			lowerCamelCase__	:			bool       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'Overwrite the cached training and evaluation sets'}					)
			lowerCamelCase__	:			Optional[int]       =    field(
			    default=5		,  metadata={
			        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
			    }		,  )
			lowerCamelCase__	:			Optional[int]       =    field(
			    default=UpperCamelCase__		,  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.'
			        )
			    }		,  )
			lowerCamelCase__	:			Optional[int]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'The number of processes to use for the preprocessing.'}		,  )
			lowerCamelCase__	:			float       =    field(
			    default=0.1_5		,  metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}					)
			lowerCamelCase__	:			bool       =    field(
			    default=UpperCamelCase__		,  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	):
				if self.train_file is not None:
					a_      				=						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:
					a_      				=						self.validation_file.split("""."""	)[-1]
					assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def      UpperCamelCase_				(      A__  ,      A__						):
	with open(A__  ,      """r"""  ,      encoding="""utf-8"""						) as f:
		a_      				=						[json.loads(A__						) for line in f.read().splitlines() if (len(A__						) > 0 and not line.isspace())]
	assert len(A__						) == len(A__						)

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






def      UpperCamelCase_				(      ):
	# 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, 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.
		a_				,  a_				,  a_      				=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
	else:
		a_				,  a_				,  a_      				=						parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	a_      				=						None
	if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
		a_      				=						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"""  ,      A__						)

	# 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.
		a_      				=						load_dataset(data_args.dataset_name  ,      data_args.dataset_config_name						)
		if "validation" not in datasets.keys():
			a_      				=						load_dataset(
			    data_args.dataset_name  ,      data_args.dataset_config_name  ,      split=F'''train[:{data_args.validation_split_percentage}%]'''  ,      )
			a_      				=						load_dataset(
			    data_args.dataset_name  ,      data_args.dataset_config_name  ,      split=F'''train[{data_args.validation_split_percentage}%:]'''  ,      )
	else:
		a_      				=						{}
		if data_args.train_file is not None:
			a_      				=						data_args.train_file
		if data_args.validation_file is not None:
			a_      				=						data_args.validation_file
		a_      				=						data_args.train_file.split("""."""						)[-1]
		if extension == "txt":
			a_      				=						"""text"""
		a_      				=						load_dataset(A__  ,      data_files=A__						)
	# 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.
	a_      				=						{
	    """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:
		a_      				=						AutoConfig.from_pretrained(model_args.config_name  ,      **A__						)
	elif model_args.model_name_or_path:
		a_      				=						AutoConfig.from_pretrained(model_args.model_name_or_path  ,      **A__						)
	else:
		a_      				=						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}'''						)

	a_      				=						{
	    """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:
		a_      				=						AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,      **A__						)
	elif model_args.model_name_or_path:
		a_      				=						AutoTokenizer.from_pretrained(model_args.model_name_or_path  ,      **A__						)
	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:
		a_      				=						AutoModelForMaskedLM.from_pretrained(
		    model_args.model_name_or_path  ,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path						)  ,      config=A__  ,      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"""						)
		a_      				=						AutoModelForMaskedLM.from_config(A__						)

	model.resize_token_embeddings(len(A__						)						)

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

	a_      				=						"""max_length""" if data_args.pad_to_max_length else False

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

	a_      				=						datasets.map(
	    A__  ,      batched=A__  ,      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:
		a_      				=						add_chinese_references(tokenized_datasets["""train"""]  ,      data_args.train_ref_file						)
	if data_args.validation_ref_file is not None:
		a_      				=						add_chinese_references(
		    tokenized_datasets["""validation"""]  ,      data_args.validation_ref_file						)
	# If we have ref files, need to avoid it removed by trainer
	a_      				=						data_args.train_ref_file or data_args.validation_ref_file
	if has_ref:
		a_      				=						False

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

	# Initialize our Trainer
	a_      				=						Trainer(
	    model=A__  ,      args=A__  ,      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=A__  ,      data_collator=A__  ,      )

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

		a_      				=						os.path.join(training_args.output_dir  ,      """train_results.txt"""						)
		if trainer.is_world_process_zero():
			with open(A__  ,      """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
	a_      				=						{}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""						)

		a_      				=						trainer.evaluate()

		a_      				=						math.exp(eval_output["""eval_loss"""]						)
		a_      				=						perplexity

		a_      				=						os.path.join(training_args.output_dir  ,      """eval_results_mlm_wwm.txt"""						)
		if trainer.is_world_process_zero():
			with open(A__  ,      """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      UpperCamelCase_				(      A__						):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()


'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__											=logging.get_logger(__name__)


lowercase__											={
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

lowercase__											={
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

lowercase__											='</w>'
lowercase__											='@@ '






def      UpperCamelCase_				(      A__						):
	a_      				=						set()
	a_      				=						word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)						)
		a_      				=						char
	return pairs


# Speech2Text2 has no max input length
lowercase__											={'facebook/s2t-wav2vec2-large-en-de': 10_24}







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			List[str]       =    VOCAB_FILES_NAMES
			lowerCamelCase__	:			Optional[Any]       =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__	:			List[Any]       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__	:			int       =    ['input_ids', 'attention_mask']




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase="<s>"					,   UpperCAmelCase="<pad>"					,   UpperCAmelCase="</s>"					,   UpperCAmelCase="<unk>"					,   UpperCAmelCase=False					,   UpperCAmelCase=None					,   **UpperCAmelCase					,   ):
				super().__init__(
				    unk_token=UpperCAmelCase					,   bos_token=UpperCAmelCase					,   eos_token=UpperCAmelCase					,   pad_token=UpperCAmelCase					,   do_lower_case=UpperCAmelCase					,   **UpperCAmelCase					,   )

				a_      				=						do_lower_case

				with open(UpperCAmelCase					,   encoding="""utf-8"""	) as vocab_handle:
					a_      				=						json.load(UpperCAmelCase	)
				a_      				=						{v: k for k, v in self.encoder.items()}

				if merges_file is None:
					logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''	)

					a_      				=						None
					a_      				=						None
				else:
					with open(UpperCAmelCase					,   encoding="""utf-8"""	) as merges_handle:
						a_      				=						merges_handle.read().split("""\n"""	)[:-1]

					a_      				=						[tuple(merge.split()[:2]	) for merge in merges]
					a_      				=						dict(zip(UpperCAmelCase					,   range(len(UpperCAmelCase	)	)	)	)
					a_      				=						{}




			@property
			def 					lowerCAmelCase__					(       self	):
				return len(self.decoder	)




			def 					lowerCAmelCase__					(       self	):
				return dict(self.encoder					,   **self.added_tokens_encoder	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						tuple(token[:-1]	) + (token[-1] + BPE_TOKEN_MERGES,)
				if token in self.cache:
					return self.cache[token]
				a_      				=						get_pairs(UpperCAmelCase	)

				if not pairs:
					return token

				while True:
					a_      				=						min(UpperCAmelCase					,   key=lambda UpperCAmelCase	: self.bpe_ranks.get(UpperCAmelCase					,   float("""inf"""	)	)	)
					if bigram not in self.bpe_ranks:
						break
					a_				,  a_      				=						bigram
					a_      				=						[]
					a_      				=						0
					while i < len(UpperCAmelCase	):
						try:
							a_      				=						word.index(UpperCAmelCase					,   UpperCAmelCase	)
						except ValueError:
							new_word.extend(word[i:]	)
							break
						else:
							new_word.extend(word[i:j]	)
							a_      				=						j

						if word[i] == first and i < len(UpperCAmelCase	) - 1 and word[i + 1] == second:
							new_word.append(first + second	)
							i += 2
						else:
							new_word.append(word[i]	)
							i += 1
					a_      				=						tuple(UpperCAmelCase	)
					a_      				=						new_word
					if len(UpperCAmelCase	) == 1:
						break
					else:
						a_      				=						get_pairs(UpperCAmelCase	)
				a_      				=						""" """.join(UpperCAmelCase	)
				if word == "\n  " + BPE_TOKEN_MERGES:
					a_      				=						"""\n""" + BPE_TOKEN_MERGES

				if word.endswith(UpperCAmelCase	):
					a_      				=						word.replace(UpperCAmelCase					,   """"""	)

				a_      				=						word.replace(""" """					,   UpperCAmelCase	)
				a_      				=						word
				return word




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):

				if self.bpe_ranks is None:
					raise ValueError(
					    """This tokenizer was instantiated without a `merges.txt` file, so"""
					    """ that it can only be used for decoding, not for encoding."""
					    """Make sure to provide `merges.txt` file at instantiation to enable """
					    """encoding."""	)

				if self.do_lower_case:
					a_      				=						text.lower()

				a_      				=						text.split()

				a_      				=						[]
				for token in text:
					if token:
						split_tokens.extend(list(self.bpe(UpperCAmelCase	).split(""" """	)	)	)

				return split_tokens




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				return self.encoder.get(UpperCAmelCase					,   self.encoder.get(self.unk_token	)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.decoder.get(UpperCAmelCase					,   self.unk_token	)
				return result




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						""" """.join(UpperCAmelCase	)

				# make sure @@ tokens are concatenated
				a_      				=						"""""".join(string.split(UpperCAmelCase	)	)

				return string






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None	):
				if not os.path.isdir(UpperCAmelCase	):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
				a_      				=						os.path.join(
				    UpperCAmelCase					,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)
				a_      				=						os.path.join(
				    UpperCAmelCase					,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]	)

				with open(UpperCAmelCase					,   """w"""					,   encoding="""utf-8"""	) as f:
					f.write(json.dumps(self.encoder					,   indent=2					,   sort_keys=UpperCAmelCase					,   ensure_ascii=UpperCAmelCase	) + """\n"""	)

				a_      				=						0
				if self.bpe_ranks is None:
					return (vocab_file,)

				with open(UpperCAmelCase					,   """w"""					,   encoding="""utf-8"""	) as writer:
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,   key=lambda UpperCAmelCase	: kv[1]	):
						if index != token_index:
							logger.warning(
							    f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
							    """ Please check that the tokenizer is not corrupted!"""	)
							a_      				=						token_index
						writer.write(""" """.join(UpperCAmelCase	) + """\n"""	)
						index += 1

				return (vocab_file, merges_file)
