
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__		:str			  =   logging.getLogger(__name__)
lowercase__		:Dict			  =   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowercase__		:Any			  =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class     lowercase		:
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={
               '''help''': (
                   '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
               )
           }	,  )
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowerCAmelCase__		)}	,  )
       lowercase_	:	Optional[str]	 =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'''
               )
           }	,  )
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}	,  )
       lowercase_	:	bool	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}	,  )
       lowercase_	:	str	 =field(
           default='''main'''	,  metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}	,  )
       lowercase_	:	bool	 =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):
            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     lowercase		:
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}		)
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}		)
       lowercase_	:	Optional[str]	 =field(default=lowerCAmelCase__	,  metadata={'''help''': '''The input training data file (a text file).'''}		)
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}	,  )
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}	,  )
       lowercase_	:	Optional[str]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}	,  )
       lowercase_	:	bool	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)
       lowercase_	:	Optional[int]	 =field(
           default=5	,  metadata={
               '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
           }	,  )
       lowercase_	:	Optional[int]	 =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.'''
               )
           }	,  )
       lowercase_	:	Optional[int]	 =field(
           default=lowerCAmelCase__	,  metadata={'''help''': '''The number of processes to use for the preprocessing.'''}	,  )
       lowercase_	:	float	 =field(
           default=0.15	,  metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}		)
       lowercase_	:	bool	 =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):
            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 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
     '''simple docstring'''
     with open(_A			,      '''r'''			,      encoding='''utf-8'''		) as f:
          lowercase						     =						[json.loads(_A		) for line in f.read().splitlines() if (len(_A		) > 0 and not line.isspace())]
     assert len(_A		) == len(_A		)

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



def 	UpperCamelCase   (							):
     '''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'''			,      _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.
          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(_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.
     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			,      **_A		)
     elif model_args.model_name_or_path:
          lowercase						     =						AutoConfig.from_pretrained(model_args.model_name_or_path			,      **_A		)
     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			,      **_A		)
     elif model_args.model_name_or_path:
          lowercase						     =						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:
          lowercase						     =						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'''		)
          lowercase						     =						AutoModelForMaskedLM.from_config(_A		)

     model.resize_token_embeddings(len(_A		)		)

     # 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(lowerCAmelCase__		):
          # Remove empty lines
          lowercase						     =						[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		)

     lowercase						     =						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:
          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=_A			,      mlm_probability=data_args.mlm_probability		)

     # Initialize our Trainer
     lowercase						     =						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:
               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=_A		)
          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(_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
     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(_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   (							lowerCAmelCase__		):
     '''simple docstring'''
     main()


if __name__ == "__main__":
       main()






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''')

												lowercase						     =						{
												    '''input_ids''': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]       ,dtype=tf.intaa),  # "My dog is cute"
												    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]       ,dtype=tf.intaa),
												}

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						tf.TensorShape((1, 6, 7_6_8))
												self.assertEqual(output.shape       ,A__)
												# compare the actual values for a slice.
												lowercase						     =						tf.convert_to_tensor(
												    [
												        [
												            [0.0681762, 0.10894451, 0.06772504],
												            [-0.06423668, 0.02366615, 0.04329344],
												            [-0.06057295, 0.09974135, -0.00070584],
												        ]
												    ]       ,dtype=tf.floataa       ,)

												self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




