




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 							__UpperCamelCase			:





    '''simple docstring'''



    __a							: 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."""
            )
        }     ,					)
    __a							: Optional[str]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__UpperCAmelCase  )}     ,					)
    __a							: 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"""
            )
        }     ,					)
    __a							: Optional[str]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
    __a							: Optional[str]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
    __a							: Optional[str]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}     ,					)
    __a							: bool										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}     ,					)
    __a							: str										=field(
        default="""main"""     ,					metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}     ,					)
    __a							: 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       __snake_case		( 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 							__UpperCamelCase			:





    '''simple docstring'''



    __a							: Optional[str]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
    __a							: Optional[str]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
    __a							: Optional[str]										=field(default=__UpperCAmelCase     ,					metadata={"""help""": """The input training data file (a text file)."""}  )
    __a							: Optional[str]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}     ,					)
    __a							: Optional[str]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}     ,					)
    __a							: Optional[str]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}     ,					)
    __a							: bool										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )
    __a							: Optional[int]										=field(
        default=5     ,					metadata={
            """help""": """The percentage of the train set used as validation set in case there's no validation split"""
        }     ,					)
    __a							: 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."""
            )
        }     ,					)
    __a							: Optional[int]										=field(
        default=__UpperCAmelCase     ,					metadata={"""help""": """The number of processes to use for the preprocessing."""}     ,					)
    __a							: float										=field(
        default=0.15     ,					metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}  )
    __a							: 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       __snake_case		( self						):
           if self.train_file is not None:
                  lowerCAmelCase   =		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:
                  lowerCAmelCase   =		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       (     _snake_case						, _snake_case							):
       with open(_snake_case						, '''r'''						, encoding='''utf-8'''							) as f:
              lowerCAmelCase   =		[json.loads(_snake_case							) for line in f.read().splitlines() if (len(_snake_case							) > 0 and not line.isspace())]
       assert len(_snake_case							) == len(_snake_case							)

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



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.

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

       # Detecting last checkpoint.
       lowerCAmelCase   =		None
       if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
              lowerCAmelCase   =		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'''						, _snake_case							)

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

       lowerCAmelCase   =		{
           '''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:
              lowerCAmelCase   =		AutoTokenizer.from_pretrained(model_args.tokenizer_name						, **_snake_case							)
       elif model_args.model_name_or_path:
              lowerCAmelCase   =		AutoTokenizer.from_pretrained(model_args.model_name_or_path						, **_snake_case							)
       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:
              lowerCAmelCase   =		AutoModelForMaskedLM.from_pretrained(
                  model_args.model_name_or_path						, from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)						, config=_snake_case						, 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'''							)
              lowerCAmelCase   =		AutoModelForMaskedLM.from_config(_snake_case							)

       model.resize_token_embeddings(len(_snake_case							)							)

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

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

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

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

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

       # Initialize our Trainer
       lowerCAmelCase   =		Trainer(
           model=_snake_case						, args=_snake_case						, 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=_snake_case						, data_collator=_snake_case						, )

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

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

              lowerCAmelCase   =		trainer.evaluate()

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

              lowerCAmelCase   =		os.path.join(training_args.output_dir						, '''eval_results_mlm_wwm.txt'''							)
              if trainer.is_world_process_zero():
                     with open(_snake_case						, '''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       (     _snake_case							):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
  main()









import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				__a							: str										=MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		hf_hub_download(
											    repo_id='''nateraw/video-demo'''       , filename='''archery.mp4'''       , repo_type='''dataset'''						)
											lowerCAmelCase   =		VideoClassificationPipeline(model=UpperCAmelCase_       , image_processor=UpperCAmelCase_       , top_k=2						)
											lowerCAmelCase   =		[
											    example_video_filepath,
											    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
											]
											return video_classifier, examples



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											for example in examples:
																		lowerCAmelCase   =		video_classifier(UpperCAmelCase_						)

																		self.assertEqual(
																		    UpperCAmelCase_       , [
																		        {'''score''': ANY(UpperCAmelCase_						), '''label''': ANY(UpperCAmelCase_						)},
																		        {'''score''': ANY(UpperCAmelCase_						), '''label''': ANY(UpperCAmelCase_						)},
																		    ]       , )



				@require_torch
				def       __snake_case		( self						):
											lowerCAmelCase   =		'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
											lowerCAmelCase   =		VideoMAEFeatureExtractor(
											    size={'''shortest_edge''': 10}       , crop_size={'''height''': 10, '''width''': 10}						)
											lowerCAmelCase   =		pipeline(
											    '''video-classification'''       , model=UpperCAmelCase_       , feature_extractor=UpperCAmelCase_       , frame_sampling_rate=4						)

											lowerCAmelCase   =		hf_hub_download(repo_id='''nateraw/video-demo'''       , filename='''archery.mp4'''       , repo_type='''dataset'''						)
											lowerCAmelCase   =		video_classifier(UpperCAmelCase_       , top_k=2						)
											self.assertEqual(
											    nested_simplify(UpperCAmelCase_       , decimals=4						)       , [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}]       , )

											lowerCAmelCase   =		video_classifier(
											    [
											        video_file_path,
											        video_file_path,
											    ]       , top_k=2       , )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase_       , decimals=4						)       , [
											        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
											        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
											    ]       , )






				@require_tf
				def       __snake_case		( self						):
											pass



