


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


a			:			Optional[Any]												=					logging.getLogger(__name__)
a			:			Any												=					list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a			:			Any												=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 						lowerCamelCase_							:



					'''simple docstring'''







					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  metadata={
					        "help": (
					            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
					        )
					    }  ,  )
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowerCAmelCase__					)}  ,  )
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  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=lowerCAmelCase__  ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}					)
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}					)
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,  )
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  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=lowerCAmelCase__  ,  metadata={
					        "help": (
					            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
					            "with private models)."
					        )
					    }  ,  )






					def A   (					self    ) ->      int:







								'''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_							:



					'''simple docstring'''







					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  metadata={"help": "The name of the dataset to use (via the datasets library)."}					)
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}					)
					__UpperCAmelCase							  =					field(default=lowerCAmelCase__  ,  metadata={"help": "The input training data file (a text file)."}					)
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}  ,  )
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}  ,  )
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}  ,  )
					__UpperCAmelCase							  =					field(
					    default=lowerCAmelCase__  ,  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=lowerCAmelCase__  ,  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=lowerCAmelCase__  ,  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=lowerCAmelCase__  ,  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 A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

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




def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







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

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


def 		lowercase_	(  ):


			'''simple docstring'''







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

			# Detecting last checkpoint.
			__lowercase			       = None
			if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
						__lowercase			       = 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.
						__lowercase			       = load_dataset(data_args.dataset_name							,      data_args.dataset_config_name	)
						if "validation" not in datasets.keys():
									__lowercase			       = load_dataset(
									    data_args.dataset_name							,      data_args.dataset_config_name							,      split=F'train[:{data_args.validation_split_percentage}%]'							,      )
									__lowercase			       = load_dataset(
									    data_args.dataset_name							,      data_args.dataset_config_name							,      split=F'train[{data_args.validation_split_percentage}%:]'							,      )
			else:
						__lowercase			       = {}
						if data_args.train_file is not None:
									__lowercase			       = data_args.train_file
						if data_args.validation_file is not None:
									__lowercase			       = data_args.validation_file
						__lowercase			       = data_args.train_file.split('''.'''	)[-1]
						if extension == "txt":
									__lowercase			       = '''text'''
						__lowercase			       = 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.
			__lowercase			       = {
			    '''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:
						__lowercase			       = AutoConfig.from_pretrained(model_args.config_name							,      **_UpperCamelCase	)
			elif model_args.model_name_or_path:
						__lowercase			       = AutoConfig.from_pretrained(model_args.model_name_or_path							,      **_UpperCamelCase	)
			else:
						__lowercase			       = 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}'	)

			__lowercase			       = {
			    '''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:
						__lowercase			       = AutoTokenizer.from_pretrained(model_args.tokenizer_name							,      **_UpperCamelCase	)
			elif model_args.model_name_or_path:
						__lowercase			       = 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:
						__lowercase			       = 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'''	)
						__lowercase			       = AutoModelForMaskedLM.from_config(_UpperCamelCase	)

			model.resize_token_embeddings(len(_UpperCamelCase	)	)

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

			__lowercase			       = '''max_length''' if data_args.pad_to_max_length else False

			def tokenize_function(_UpperCamelCase	):
						# Remove empty lines
						__lowercase			       = [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	)

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

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

			# Initialize our Trainer
			__lowercase			       = 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:
									__lowercase			       = last_checkpoint
						elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path	):
									__lowercase			       = model_args.model_name_or_path
						else:
									__lowercase			       = None
						__lowercase			       = trainer.train(resume_from_checkpoint=_UpperCamelCase	)
						trainer.save_model()  # Saves the tokenizer too for easy upload

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

						__lowercase			       = trainer.evaluate()

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

						__lowercase			       = 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 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			main()


if __name__ == "__main__":
	main()






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

a			:			Union[str, Any]												=					'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
a			:			Tuple												=					[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
a			:			List[Any]												=					dict(zip(vocab, range(len(vocab))))
a			:			Optional[int]												=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
	a			:			Union[str, Any]												=					Path(tmpdirname)
	a			:			Dict												=					build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	a			:			Optional[int]												=					build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	a			:			Tuple												=					build_dir / VOCAB_FILES_NAMES['''merges_file''']
	with open(src_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(tgt_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(merges_file, '''w''') as fp:
		fp.write('''\n'''.join(merges))

	a			:			Optional[int]												=					FSMTTokenizer(
	    langs=['''en''', '''ru'''],
	    src_vocab_size=len(vocab),
	    tgt_vocab_size=len(vocab),
	    src_vocab_file=src_vocab_file,
	    tgt_vocab_file=tgt_vocab_file,
	    merges_file=merges_file,
	)

a			:			List[str]												=					FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

a			:			Union[str, Any]												=					FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
a			:			Dict												=					tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a			:			Union[str, Any]												=					tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru


