
'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCAmelCase_							: Optional[Any] =							logging.get_logger(__name__)

UpperCAmelCase_							: int =							{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
UpperCAmelCase_							: Any =							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]







def 		A_							(						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




      _lowerCamelCase					: int		    = {}
      with open(_lowerCAmelCase					,						"r"				) as file:
            for line_number, line in enumerate(_lowerCAmelCase				):
                  _lowerCamelCase					: Union[str, Any]		    = line.strip()
                  if line:
                        _lowerCamelCase					: int		    = line.split()
                        _lowerCamelCase					: Optional[Any]		    = line_number
                        _lowerCamelCase					: Tuple		    = words[0]
                        _lowerCamelCase					: Optional[int]		    = value
      return result







def 		A_							(						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     Optional[int]				):



      """simple docstring"""




      for attribute in key.split("."				):
            _lowerCamelCase					: Optional[int]		    = getattr(_lowerCAmelCase					,						_lowerCAmelCase				)

      _lowerCamelCase					: List[Any]		    = None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_lowerCAmelCase				):
                  _lowerCamelCase					: str		    = PARAM_MAPPING[full_name.split("."				)[-1]]
                  _lowerCamelCase					: Any		    = "param"

      if weight_type is not None and weight_type != "param":
            _lowerCamelCase					: Optional[Any]		    = getattr(_lowerCAmelCase					,						_lowerCAmelCase				).shape
      elif weight_type is not None and weight_type == "param":
            _lowerCamelCase					: List[Any]		    = hf_pointer
            for attribute in hf_param_name.split("."				):
                  _lowerCamelCase					: Optional[int]		    = getattr(_lowerCAmelCase					,						_lowerCAmelCase				)
            _lowerCamelCase					: List[str]		    = shape_pointer.shape

            # let's reduce dimension
            _lowerCamelCase					: Optional[int]		    = value[0]
      else:
            _lowerCamelCase					: str		    = hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
                F' {value.shape} for {full_name}'				)

      if weight_type == "weight":
            _lowerCamelCase					: Union[str, Any]		    = value
      elif weight_type == "weight_g":
            _lowerCamelCase					: List[str]		    = value
      elif weight_type == "weight_v":
            _lowerCamelCase					: Optional[Any]		    = value
      elif weight_type == "bias":
            _lowerCamelCase					: Optional[int]		    = value
      elif weight_type == "param":
            for attribute in hf_param_name.split("."				):
                  _lowerCamelCase					: Optional[int]		    = getattr(_lowerCAmelCase					,						_lowerCAmelCase				)
            _lowerCamelCase					: str		    = value
      else:
            _lowerCamelCase					: Optional[Any]		    = value

      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'				)







def 		A_							(						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     Union[str, Any]					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Optional[int]				):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_lowerCAmelCase				):
                  _lowerCamelCase					: str		    = PARAM_MAPPING[full_name.split("."				)[-1]]
                  _lowerCamelCase					: List[Any]		    = "param"

      if weight_type is not None and weight_type != "param":
            _lowerCamelCase					: Union[str, Any]		    = ".".join([key, weight_type]				)
      elif weight_type is not None and weight_type == "param":
            _lowerCamelCase					: str		    = ".".join([key, hf_param_name]				)
      else:
            _lowerCamelCase					: str		    = key

      _lowerCamelCase					: Union[str, Any]		    = value if "lm_head" in full_key else value[0]


UpperCAmelCase_							: Dict =							{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}







def 		A_							(						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     List[Any]=None					,						_lowerCAmelCase :     Optional[int]=None				):



      """simple docstring"""




      _lowerCamelCase					: Union[str, Any]		    = False
      for key, mapped_key in MAPPING.items():
            _lowerCamelCase					: Dict		    = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
                  _lowerCamelCase					: List[str]		    = True
                  if "*" in mapped_key:
                        _lowerCamelCase					: Optional[int]		    = name.split(_lowerCAmelCase				)[0].split("."				)[-2]
                        _lowerCamelCase					: Tuple		    = mapped_key.replace("*"					,						_lowerCAmelCase				)
                  if "weight_g" in name:
                        _lowerCamelCase					: List[Any]		    = "weight_g"
                  elif "weight_v" in name:
                        _lowerCamelCase					: int		    = "weight_v"
                  elif "bias" in name:
                        _lowerCamelCase					: List[Any]		    = "bias"
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        _lowerCamelCase					: int		    = "weight"
                  else:
                        _lowerCamelCase					: Union[str, Any]		    = None
                  if hf_dict is not None:
                        rename_dict(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase				)
                  else:
                        set_recursively(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase				)
                  return is_used
      return is_used







def 		A_							(						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Dict				):



      """simple docstring"""




      _lowerCamelCase					: List[str]		    = []
      _lowerCamelCase					: str		    = fairseq_model.state_dict()

      _lowerCamelCase					: List[str]		    = hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            _lowerCamelCase					: Union[str, Any]		    = False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						hf_model.config.feat_extract_norm == "group"					,						)
                  _lowerCamelCase					: str		    = True
            else:
                  _lowerCamelCase					: str		    = load_wavaveca_layer(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase				)
            if not is_used:
                  unused_weights.append(_lowerCAmelCase				)

      logger.warning(F'Unused weights: {unused_weights}'				)







def 		A_							(						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Any				):



      """simple docstring"""




      _lowerCamelCase					: int		    = full_name.split("conv_layers."				)[-1]
      _lowerCamelCase					: List[Any]		    = name.split("."				)
      _lowerCamelCase					: Optional[Any]		    = int(items[0]				)
      _lowerCamelCase					: List[Any]		    = int(items[1]				)

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            F'{full_name} has size {value.shape}, but'
                            F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'				)
                  _lowerCamelCase					: List[str]		    = value
                  logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            F'{full_name} has size {value.shape}, but'
                            F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'				)
                  _lowerCamelCase					: Tuple		    = value
                  logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            F'{full_name} has size {value.shape}, but'
                            F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'				)
                  _lowerCamelCase					: int		    = value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            F'{full_name} has size {value.shape}, but'
                            F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'				)
                  _lowerCamelCase					: Union[str, Any]		    = value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
      else:
            unused_weights.append(_lowerCAmelCase				)







@torch.no_grad()
def 		A_							(						_lowerCAmelCase :     Optional[int]					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Tuple=None					,						_lowerCAmelCase :     str=None					,						_lowerCAmelCase :     List[str]=True					,						_lowerCAmelCase :     int=False				):



      """simple docstring"""




      if config_path is not None:
            _lowerCamelCase					: Tuple		    = WavaVecaConfig.from_pretrained(_lowerCAmelCase				)
      else:
            _lowerCamelCase					: List[str]		    = WavaVecaConfig()

      if is_seq_class:
            _lowerCamelCase					: Tuple		    = read_txt_into_dict(_lowerCAmelCase				)
            _lowerCamelCase					: str		    = idalabel
            _lowerCamelCase					: int		    = WavaVecaForSequenceClassification(_lowerCAmelCase				)
            _lowerCamelCase					: Union[str, Any]		    = WavaVecaFeatureExtractor(
                feature_size=1					,						sampling_rate=16000					,						padding_value=0					,						do_normalize=_lowerCAmelCase					,						return_attention_mask=_lowerCAmelCase					,						)
            feature_extractor.save_pretrained(_lowerCAmelCase				)

      elif is_finetuned:
            if dict_path:
                  _lowerCamelCase					: List[str]		    = Dictionary.load(_lowerCAmelCase				)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  _lowerCamelCase					: Optional[int]		    = target_dict.pad_index
                  _lowerCamelCase					: str		    = target_dict.bos_index
                  _lowerCamelCase					: Tuple		    = target_dict.eos_index
                  _lowerCamelCase					: Optional[Any]		    = len(target_dict.symbols				)
                  _lowerCamelCase					: Optional[int]		    = os.path.join(_lowerCAmelCase					,						"vocab.json"				)
                  if not os.path.isdir(_lowerCAmelCase				):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCAmelCase				)				)
                        return
                  os.makedirs(_lowerCAmelCase					,						exist_ok=_lowerCAmelCase				)
                  _lowerCamelCase					: Optional[int]		    = target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  _lowerCamelCase					: str		    = 0
                  _lowerCamelCase					: List[str]		    = 1
                  with open(_lowerCAmelCase					,						"w"					,						encoding="utf-8"				) as vocab_handle:
                        json.dump(_lowerCAmelCase					,						_lowerCAmelCase				)
                  _lowerCamelCase					: Tuple		    = WavaVecaCTCTokenizer(
                      _lowerCAmelCase					,						unk_token=target_dict.unk_word					,						pad_token=target_dict.pad_word					,						bos_token=target_dict.bos_word					,						eos_token=target_dict.eos_word					,						word_delimiter_token="|"					,						do_lower_case=_lowerCAmelCase					,						)
                  _lowerCamelCase					: Union[str, Any]		    = True if config.feat_extract_norm == "layer" else False
                  _lowerCamelCase					: Optional[int]		    = WavaVecaFeatureExtractor(
                      feature_size=1					,						sampling_rate=16000					,						padding_value=0					,						do_normalize=_lowerCAmelCase					,						return_attention_mask=_lowerCAmelCase					,						)
                  _lowerCamelCase					: Any		    = WavaVecaProcessor(feature_extractor=_lowerCAmelCase					,						tokenizer=_lowerCAmelCase				)
                  processor.save_pretrained(_lowerCAmelCase				)

            _lowerCamelCase					: List[str]		    = WavaVecaForCTC(_lowerCAmelCase				)
      else:
            _lowerCamelCase					: List[str]		    = WavaVecaForPreTraining(_lowerCAmelCase				)

      if is_finetuned or is_seq_class:
            _lowerCamelCase    ,		_lowerCamelCase    ,		_lowerCamelCase					: Dict		    = fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]					,						arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
      else:
            _lowerCamelCase					: str		    = argparse.Namespace(task="audio_pretraining"				)
            _lowerCamelCase					: Optional[Any]		    = fairseq.tasks.setup_task(_lowerCAmelCase				)

            _lowerCamelCase    ,		_lowerCamelCase    ,		_lowerCamelCase					: str		    = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,						task=_lowerCAmelCase				)

      _lowerCamelCase					: Optional[Any]		    = model[0].eval()

      recursively_load_weights(_lowerCAmelCase					,						_lowerCAmelCase					,						not is_finetuned				)

      hf_wavavec.save_pretrained(_lowerCAmelCase				)


if __name__ == "__main__":
 UpperCAmelCase_							: List[Any] =							argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
 parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 parser.add_argument(
     '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
 )
 parser.add_argument(
     '--is_seq_class',
     action='store_true',
     help='Whether the model to convert is a fine-tuned sequence classification model or not',
 )
 UpperCAmelCase_							: List[Any] =							parser.parse_args()

 UpperCAmelCase_							: Union[str, Any] =							not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )




'''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


__a  			=						logging.getLogger(__name__)


__a  			=						list(MODEL_WITH_LM_HEAD_MAPPING.keys())
__a  			=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







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







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						field(
        default=_a						,						metadata={"help": "The input training data file (a text file)."}      )
    lowercase   =						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"
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}						,						)

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

    lowercase   =						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)."
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "Overwrite the cached training and evaluation sets"}      )





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = False	,  _lowerCAmelCase = None	,  )						->					Tuple:
      def _dataset(_lowerCAmelCase	,  _lowerCAmelCase=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 		__snake_case(							)						->					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__    :       Optional[int]       		= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
      snake_case__		,							snake_case__		,							snake_case__    :       int       		= 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__    :       Any       		= AutoConfig.from_pretrained(model_args.config_name	,  cache_dir=model_args.cache_dir						)
      elif model_args.model_name_or_path:
            snake_case__    :       Any       		= AutoConfig.from_pretrained(model_args.model_name_or_path	,  cache_dir=model_args.cache_dir						)
      else:
            snake_case__    :       Tuple       		= CONFIG_MAPPING[model_args.model_type]()
            logger.warning("""You are instantiating a new config instance from scratch."""						)

      if model_args.tokenizer_name:
            snake_case__    :       str       		= AutoTokenizer.from_pretrained(model_args.tokenizer_name	,  cache_dir=model_args.cache_dir						)
      elif model_args.model_name_or_path:
            snake_case__    :       str       		= 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__    :       Union[str, Any]       		= 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__    :       List[Any]       		= 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__    :       List[Any]       		= tokenizer.max_len
            # Our input block size will be the max possible for the model
      else:
            snake_case__    :       List[str]       		= min(data_args.block_size	,  tokenizer.max_len						)

      # Get datasets

      snake_case__    :       int       		= (
          get_dataset(_lowerCAmelCase	,  tokenizer=_lowerCAmelCase	,  cache_dir=model_args.cache_dir						) if training_args.do_train else None
      )
      snake_case__    :       Any       		= (
          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__    :       str       		= 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__    :       List[str]       		= DataCollatorForWholeWordMask(
                      tokenizer=_lowerCAmelCase	,  mlm_probability=data_args.mlm_probability						)
            else:
                  snake_case__    :       Optional[int]       		= DataCollatorForLanguageModeling(
                      tokenizer=_lowerCAmelCase	,  mlm=data_args.mlm	,  mlm_probability=data_args.mlm_probability						)

    # Initialize our Trainer
      snake_case__    :       Optional[Any]       		= 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__    :       Any       		= (
                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__    :       Union[str, Any]       		= {}
      if training_args.do_eval:
            logger.info("""*** Evaluate ***"""						)

            snake_case__    :       Dict       		= trainer.evaluate()

            snake_case__    :       Dict       		= math.exp(eval_output["""eval_loss"""]						)
            snake_case__    :       str       		= {"""perplexity""": perplexity}

            snake_case__    :       Any       		= 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 		__snake_case(							_lowerCAmelCase						)						->					Tuple:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
       main()
