





'''simple docstring'''




import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)


_SCREAMING_SNAKE_CASE							     =			list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_SCREAMING_SNAKE_CASE							     =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 			_lowerCAmelCase		:






     """simple docstring"""






     snake_case_						      =   field(
         default=A__							,     metadata={
             "help": (
                 "The model checkpoint for weights initialization. Leave None if you want to train a model from"
                 " scratch."
             )
         }							,     )
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(A__	)}							,     )
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,     )




@dataclass
class 			_lowerCAmelCase		:






     """simple docstring"""






     snake_case_						      =   field(
         default=A__							,     metadata={"help": "The input training data file (a text file)."}	)
     snake_case_						      =   field(
         default=A__							,     metadata={
             "help": (
                 "The input training data files (multiple files in glob format). "
                 "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
             )
         }							,     )
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}							,     )
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}							,     )
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}							,     )
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}							,     )

     snake_case_						      =   field(
         default=A__							,     metadata={"help": "Train with masked-language modeling loss instead of language modeling."}	)
     snake_case_						      =   field(default=A__							,     metadata={"help": "Whether ot not to use whole word mask."}	)
     snake_case_						      =   field(
         default=0.15							,     metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}	)
     snake_case_						      =   field(
         default=1 / 6							,     metadata={
             "help": (
                 "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
                 " modeling."
             )
         }							,     )
     snake_case_						      =   field(
         default=5							,     metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}	)

     snake_case_						      =   field(
         default=-1							,     metadata={
             "help": (
                 "Optional input sequence length after tokenization."
                 "The training dataset will be truncated in block of this size for training."
                 "Default to the model max input length for single sentence inputs (take into account special tokens)."
             )
         }							,     )
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "Overwrite the cached training and evaluation sets"}	)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							DataTrainingArguments      ,    __lowerCAmelCase       :							PreTrainedTokenizer      ,    __lowerCAmelCase       :							bool = False      ,    __lowerCAmelCase       :							Optional[str] = None      ,    )		->  List[Any]:
   def _dataset(__lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							List[Any]=None		):
      if args.line_by_line:
         if ref_path is not None:
            if not args.whole_word_mask or not args.mlm:
               raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask"""		)
            return LineByLineWithRefDataset(
                tokenizer=__lowerCAmelCase      ,    file_path=__lowerCAmelCase      ,    block_size=args.block_size      ,    ref_path=__lowerCAmelCase      ,    )

         return LineByLineTextDataset(tokenizer=__lowerCAmelCase      ,    file_path=__lowerCAmelCase      ,    block_size=args.block_size		)
      else:
         return TextDataset(
             tokenizer=__lowerCAmelCase      ,    file_path=__lowerCAmelCase      ,    block_size=args.block_size      ,    overwrite_cache=args.overwrite_cache      ,    cache_dir=__lowerCAmelCase      ,    )

   if evaluate:
      return _dataset(args.eval_data_file      ,    args.eval_ref_file		)
   elif args.train_data_files:
      return ConcatDataset([_dataset(__lowerCAmelCase		) for f in glob(args.train_data_files		)]		)
   else:
      return _dataset(args.train_data_file      ,    args.train_ref_file		)





def 	__lowerCamelCase			(      )		->  Dict:
   # 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.

   snake_case 						=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
   snake_case						,    snake_case						,    snake_case 						=   parser.parse_args_into_dataclasses()

   if data_args.eval_data_file is None and training_args.do_eval:
      raise ValueError(
          """Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
          """or remove the --do_eval argument."""		)
   if (
       os.path.exists(training_args.output_dir		)
       and os.listdir(training_args.output_dir		)
       and training_args.do_train
       and not training_args.overwrite_output_dir
   ):
      raise ValueError(
          F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
          """ --overwrite_output_dir to overcome."""		)

   # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,    datefmt="""%m/%d/%Y %H:%M:%S"""      ,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      ,    )
   logger.warning(
       """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""      ,    training_args.local_rank      ,    training_args.device      ,    training_args.n_gpu      ,    bool(training_args.local_rank != -1		)      ,    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"""      ,    __lowerCAmelCase		)

   # Set seed
   set_seed(training_args.seed		)

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   if model_args.config_name:
      snake_case 						=   AutoConfig.from_pretrained(model_args.config_name      ,    cache_dir=model_args.cache_dir		)
   elif model_args.model_name_or_path:
      snake_case 						=   AutoConfig.from_pretrained(model_args.model_name_or_path      ,    cache_dir=model_args.cache_dir		)
   else:
      snake_case 						=   CONFIG_MAPPING[model_args.model_type]()
      logger.warning("""You are instantiating a new config instance from scratch."""		)

   if model_args.tokenizer_name:
      snake_case 						=   AutoTokenizer.from_pretrained(model_args.tokenizer_name      ,    cache_dir=model_args.cache_dir		)
   elif model_args.model_name_or_path:
      snake_case 						=   AutoTokenizer.from_pretrained(model_args.model_name_or_path      ,    cache_dir=model_args.cache_dir		)
   else:
      raise ValueError(
          """You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
          """ script, save it,and load it from here, using --tokenizer_name"""		)

   if model_args.model_name_or_path:
      snake_case 						=   AutoModelWithLMHead.from_pretrained(
          model_args.model_name_or_path      ,    from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)      ,    config=__lowerCAmelCase      ,    cache_dir=model_args.cache_dir      ,    )
   else:
      logger.info("""Training new model from scratch"""		)
      snake_case 						=   AutoModelWithLMHead.from_config(__lowerCAmelCase		)

   model.resize_token_embeddings(len(__lowerCAmelCase		)		)

   if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
      raise ValueError(
          """BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
          """--mlm flag (masked language modeling)."""		)

   if data_args.block_size <= 0:
      snake_case 						=   tokenizer.max_len
      # Our input block size will be the max possible for the model
   else:
      snake_case 						=   min(data_args.block_size      ,    tokenizer.max_len		)

   # Get datasets

   snake_case 						=   (
       get_dataset(__lowerCAmelCase      ,    tokenizer=__lowerCAmelCase      ,    cache_dir=model_args.cache_dir		) if training_args.do_train else None
   )
   snake_case 						=   (
       get_dataset(__lowerCAmelCase      ,    tokenizer=__lowerCAmelCase      ,    evaluate=__lowerCAmelCase      ,    cache_dir=model_args.cache_dir		)
       if training_args.do_eval
       else None
   )
   if config.model_type == "xlnet":
      snake_case 						=   DataCollatorForPermutationLanguageModeling(
          tokenizer=__lowerCAmelCase      ,    plm_probability=data_args.plm_probability      ,    max_span_length=data_args.max_span_length      ,    )
   else:
      if data_args.mlm and data_args.whole_word_mask:
         snake_case 						=   DataCollatorForWholeWordMask(
             tokenizer=__lowerCAmelCase      ,    mlm_probability=data_args.mlm_probability		)
      else:
         snake_case 						=   DataCollatorForLanguageModeling(
             tokenizer=__lowerCAmelCase      ,    mlm=data_args.mlm      ,    mlm_probability=data_args.mlm_probability		)

    # Initialize our Trainer
   snake_case 						=   Trainer(
       model=__lowerCAmelCase      ,    args=__lowerCAmelCase      ,    data_collator=__lowerCAmelCase      ,    train_dataset=__lowerCAmelCase      ,    eval_dataset=__lowerCAmelCase      ,    prediction_loss_only=__lowerCAmelCase      ,    )

   # Training
   if training_args.do_train:
      snake_case 						=   (
          model_args.model_name_or_path
          if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		)
          else None
      )
      trainer.train(model_path=__lowerCAmelCase		)
      trainer.save_model()
      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      if trainer.is_world_master():
         tokenizer.save_pretrained(training_args.output_dir		)

    # Evaluation
   snake_case 						=   {}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***"""		)

      snake_case 						=   trainer.evaluate()

      snake_case 						=   math.exp(eval_output["""eval_loss"""]		)
      snake_case 						=   {"""perplexity""": perplexity}

      snake_case 						=   os.path.join(training_args.output_dir      ,    """eval_results_lm.txt"""		)
      if trainer.is_world_master():
         with open(__lowerCAmelCase      ,    """w"""		) as writer:
            logger.info("""***** Eval results *****"""		)
            for key in sorted(result.keys()		):
               logger.info("""  %s = %s"""      ,    __lowerCAmelCase      ,    str(result[key]		)		)
               writer.write("""%s = %s\n""" % (key, str(result[key]		))		)

      results.update(__lowerCAmelCase		)

   return results





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[Any]		)		->  int:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()








'''simple docstring'''




import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_SCREAMING_SNAKE_CASE							     =			"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
_SCREAMING_SNAKE_CASE							     =			direct_transformers_import(PATH_TO_TRANSFORMERS)

_SCREAMING_SNAKE_CASE							     =			transformers.models.auto.configuration_auto.CONFIG_MAPPING

_SCREAMING_SNAKE_CASE							     =			{
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Union[str, Any]		)		->  List[Any]:
   snake_case 						=   False
   for attribute in attributes:
      for modeling_source in source_strings:
         # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
         if (
             F'''config.{attribute}''' in modeling_source
             or F'''getattr(config, "{attribute}"''' in modeling_source
             or F'''getattr(self.config, "{attribute}"''' in modeling_source
         ):
            snake_case 						=   True
         # Deal with multi-line cases
         elif (
             re.search(
                 rF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''      ,    __lowerCAmelCase      ,    )
             is not None
         ):
            snake_case 						=   True
         # `SequenceSummary` is called with `SequenceSummary(config)`
         elif attribute in [
             "summary_type",
             "summary_use_proj",
             "summary_activation",
             "summary_last_dropout",
             "summary_proj_to_labels",
             "summary_first_dropout",
         ]:
            if "SequenceSummary" in modeling_source:
               snake_case 						=   True
         if attribute_used:
            break
      if attribute_used:
         break

    # common and important attributes, even if they do not always appear in the modeling files
   snake_case 						=   [
       """bos_index""",
       """eos_index""",
       """pad_index""",
       """unk_index""",
       """mask_index""",
       """image_size""",
       """use_cache""",
       """out_features""",
       """out_indices""",
   ]
   snake_case 						=   ["""encoder_no_repeat_ngram_size"""]

   # Special cases to be allowed
   snake_case 						=   True
   if not attribute_used:
      snake_case 						=   False
      for attribute in attributes:
         # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
         if attribute in ["is_encoder_decoder"] and default_value is True:
            snake_case 						=   True
         elif attribute in ["tie_word_embeddings"] and default_value is False:
            snake_case 						=   True

         # Allow cases without checking the default value in the configuration class
         elif attribute in attributes_to_allow + attributes_used_in_generation:
            snake_case 						=   True
         elif attribute.endswith("""_token_id"""		):
            snake_case 						=   True

         # configuration class specific cases
         if not case_allowed:
            snake_case 						=   SPECIAL_CASES_TO_ALLOW.get(config_class.__name__      ,    []		)
            snake_case 						=   allowed_cases is True or attribute in allowed_cases

   return attribute_used or case_allowed





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]		)		->  List[Any]:
   snake_case 						=   dict(inspect.signature(config_class.__init__		).parameters		)
   snake_case 						=   [x for x in list(signature.keys()		) if x not in ["""self""", """kwargs"""]]
   snake_case 						=   [signature[param].default for param in parameter_names]

   # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
   # as one variant is used, the test should pass
   snake_case 						=   {}
   if len(config_class.attribute_map		) > 0:
      snake_case 						=   {v: k for k, v in config_class.attribute_map.items()}

   # Get the path to modeling source files
   snake_case 						=   inspect.getsourcefile(__lowerCAmelCase		)
   snake_case 						=   os.path.dirname(__lowerCAmelCase		)
   # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
   snake_case 						=   [os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		) for fn in os.listdir(__lowerCAmelCase		) if fn.startswith("""modeling_"""		)]

   # Get the source code strings
   snake_case 						=   []
   for path in modeling_paths:
      if os.path.isfile(__lowerCAmelCase		):
         with open(__lowerCAmelCase		) as fp:
            modeling_sources.append(fp.read()		)

   snake_case 						=   []
   for config_param, default_value in zip(__lowerCAmelCase      ,    __lowerCAmelCase		):
      # `attributes` here is all the variant names for `config_param`
      snake_case 						=   [config_param]
      # some configuration classes have non-empty `attribute_map`, and both names could be used in the
      # corresponding modeling files. As long as one of them appears, it is fine.
      if config_param in reversed_attribute_map:
         attributes.append(reversed_attribute_map[config_param]		)

      if not check_attribute_being_used(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		):
         unused_attributes.append(attributes[0]		)

   return sorted(__lowerCAmelCase		)





def 	__lowerCamelCase			(      )		->  Optional[Any]:
   snake_case 						=   {}
   for _config_class in list(CONFIG_MAPPING.values()		):
      # Skip deprecated models
      if "models.deprecated" in _config_class.__module__:
         continue
      # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
      snake_case 						=   [
          cls
          for name, cls in inspect.getmembers(
              inspect.getmodule(_config_class		)      ,    lambda __lowerCAmelCase		: inspect.isclass(__lowerCAmelCase		)
              and issubclass(__lowerCAmelCase      ,    __lowerCAmelCase		)
              and inspect.getmodule(__lowerCAmelCase		) == inspect.getmodule(_config_class		)      ,    )
      ]
      for config_class in config_classes_in_module:
         snake_case 						=   check_config_attributes_being_used(__lowerCAmelCase		)
         if len(__lowerCAmelCase		) > 0:
            snake_case 						=   unused_attributes

   if len(__lowerCAmelCase		) > 0:
      snake_case 						=   """The following configuration classes contain unused attributes in the corresponding modeling files:\n"""
      for name, attributes in configs_with_unused_attributes.items():
         error += F'''{name}: {attributes}\n'''

      raise ValueError(__lowerCAmelCase		)


if __name__ == "__main__":
 check_config_attributes()

