





"""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


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






@dataclass
class 		__snake_case :





 """simple docstring"""


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

 def  UpperCamelCase__(   self  ):



     '''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 		__snake_case :





 """simple docstring"""


 _lowerCamelCase				=					field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}					)
 _lowerCamelCase				=					field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}					)
 _lowerCamelCase				=					field(default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """The input training data file (a text file)."""}					)
 _lowerCamelCase				=					field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}							,							)
 _lowerCamelCase				=					field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}							,							)
 _lowerCamelCase				=					field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}							,							)
 _lowerCamelCase				=					field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)
 _lowerCamelCase				=					field(
     default=5							,							metadata={
         """help""": """The percentage of the train set used as validation set in case there's no validation split"""
     }							,							)
 _lowerCamelCase				=					field(
     default=SCREAMING_SNAKE_CASE__							,							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				=					field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """The number of processes to use for the preprocessing."""}							,							)
 _lowerCamelCase				=					field(
     default=0.1_5							,							metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}					)
 _lowerCamelCase				=					field(
     default=SCREAMING_SNAKE_CASE__							,							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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if self.train_file is not None:
         __A   :						List[Any]									=					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   :						Optional[Any]									=					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					(       snake_case_  :  List[Any]    ,snake_case_  :  Dict						)       ->Union[str, Any]:



    '''simple docstring'''







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

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







def 		__lowercase					(       )       ->str:



    '''simple docstring'''







    __A   :						List[Any]									=					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   :						List[Any]									=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        __A   :						Optional[int]									=					parser.parse_args_into_dataclasses()

    # Detecting last checkpoint.
    __A   :						Dict									=					None
    if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
        __A   :						Optional[int]									=					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'''    ,lowercase__						)

    # 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   :						List[Any]									=					load_dataset(data_args.dataset_name    ,data_args.dataset_config_name						)
        if "validation" not in datasets.keys():
            __A   :						Optional[int]									=					load_dataset(
                data_args.dataset_name    ,data_args.dataset_config_name    ,split=F"""train[:{data_args.validation_split_percentage}%]"""    ,)
            __A   :						List[str]									=					load_dataset(
                data_args.dataset_name    ,data_args.dataset_config_name    ,split=F"""train[{data_args.validation_split_percentage}%:]"""    ,)
    else:
        __A   :						Union[str, Any]									=					{}
        if data_args.train_file is not None:
            __A   :						Union[str, Any]									=					data_args.train_file
        if data_args.validation_file is not None:
            __A   :						List[str]									=					data_args.validation_file
        __A   :						List[str]									=					data_args.train_file.split('''.'''						)[-1]
        if extension == "txt":
            __A   :						int									=					'text'
        __A   :						Tuple									=					load_dataset(lowercase__    ,data_files=lowercase__						)
    # 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   :						Union[str, Any]									=					{
        '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   :						Dict									=					AutoConfig.from_pretrained(model_args.config_name    ,**lowercase__						)
    elif model_args.model_name_or_path:
        __A   :						List[Any]									=					AutoConfig.from_pretrained(model_args.model_name_or_path    ,**lowercase__						)
    else:
        __A   :						List[str]									=					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   :						int									=					{
        '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   :						Dict									=					AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,**lowercase__						)
    elif model_args.model_name_or_path:
        __A   :						Optional[Any]									=					AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,**lowercase__						)
    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   :						Optional[int]									=					AutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path    ,from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)    ,config=lowercase__    ,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   :						List[Any]									=					AutoModelForMaskedLM.from_config(lowercase__						)

    model.resize_token_embeddings(len(lowercase__						)						)

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    if training_args.do_train:
        __A   :						Union[str, Any]									=					datasets['train'].column_names
    else:
        __A   :						str									=					datasets['validation'].column_names
    __A   :						List[Any]									=					'text' if 'text' in column_names else column_names[0]

    __A   :						Optional[Any]									=					'max_length' if data_args.pad_to_max_length else False

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

    __A   :						List[Any]									=					datasets.map(
        lowercase__    ,batched=lowercase__    ,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   :						Optional[Any]									=					add_chinese_references(tokenized_datasets['''train''']    ,data_args.train_ref_file						)
    if data_args.validation_ref_file is not None:
        __A   :						Dict									=					add_chinese_references(
            tokenized_datasets['''validation''']    ,data_args.validation_ref_file						)
    # If we have ref files, need to avoid it removed by trainer
    __A   :						int									=					data_args.train_ref_file or data_args.validation_ref_file
    if has_ref:
        __A   :						str									=					False

    # Data collator
    # This one will take care of randomly masking the tokens.
    __A   :						Union[str, Any]									=					DataCollatorForWholeWordMask(tokenizer=lowercase__    ,mlm_probability=data_args.mlm_probability						)

    # Initialize our Trainer
    __A   :						str									=					Trainer(
        model=lowercase__    ,args=lowercase__    ,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=lowercase__    ,data_collator=lowercase__    ,)

    # Training
    if training_args.do_train:
        if last_checkpoint is not None:
            __A   :						Any									=					last_checkpoint
        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path						):
            __A   :						Optional[Any]									=					model_args.model_name_or_path
        else:
            __A   :						str									=					None
        __A   :						Optional[int]									=					trainer.train(resume_from_checkpoint=lowercase__						)
        trainer.save_model()  # Saves the tokenizer too for easy upload

        __A   :						List[Any]									=					os.path.join(training_args.output_dir    ,'''train_results.txt'''						)
        if trainer.is_world_process_zero():
            with open(lowercase__    ,'''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   :						List[Any]									=					{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''						)

        __A   :						int									=					trainer.evaluate()

        __A   :						int									=					math.exp(eval_output['''eval_loss''']						)
        __A   :						int									=					perplexity

        __A   :						Optional[Any]									=					os.path.join(training_args.output_dir    ,'''eval_results_mlm_wwm.txt'''						)
        if trainer.is_world_process_zero():
            with open(lowercase__    ,'''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					(       snake_case_  :  List[str]						)       ->Tuple:



    '''simple docstring'''







    main()


if __name__ == "__main__":
       main()










"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_			=      {
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""VivitImageProcessor"""]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """VivitModel""",
           """VivitPreTrainedModel""",
           """VivitForVideoClassification""",
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




