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 		_lowercase						:
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={
         'help': (
             'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
         )
     }						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowerCAmelCase__       )}						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={
         'help': (
             'Override some existing default config settings when a model is trained from scratch. Example: '
             'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
         )
     }						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}						,      )
 lowercase				     =   field(
     default='main'						,      metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={
         'help': (
             'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
             'with private models).'
         )
     }						,      )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						List[str]:

  """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 		_lowercase						:
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'The name of the dataset to use (via the datasets library).'}       )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
 lowercase				     =   field(default=lowerCAmelCase__						,      metadata={'help': 'The input training data file (a text file).'}       )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
 lowercase				     =   field(
     default=5						,      metadata={
         'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
     }						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={
         'help': (
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated. Default to the max input length of the model.'
         )
     }						,      )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={'help': 'The number of processes to use for the preprocessing.'}						,      )
 lowercase				     =   field(
     default=0.15						,      metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}       )
 lowercase				     =   field(
     default=lowerCAmelCase__						,      metadata={
         'help': (
             'Whether to pad all samples to `max_seq_length`. '
             'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
         )
     }						,      )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						List[Any]:

  """simple docstring"""





  if self.train_file is not None:
   UpperCamelCase_					:						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:
   UpperCamelCase_					:						int =					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     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     List[str]	):
 with open(_a  ,  'r'  ,  encoding='utf-8'	) as f:
  UpperCamelCase_					:						Union[str, Any] =					[json.loads(_a	) for line in f.read().splitlines() if (len(_a	) > 0 and not line.isspace())]
 assert len(_a	) == len(_a	)

 UpperCamelCase_					:						Dict =					{c: dataset[c] for c in dataset.column_names}
 UpperCamelCase_					:						List[str] =					refs
 return Dataset.from_dict(_a	)





def    __lowercase     (						):
 # 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.

 UpperCamelCase_					:						str =					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.
  UpperCamelCase_					:						Any =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
 else:
  UpperCamelCase_					:						Dict =					parser.parse_args_into_dataclasses()

 # Detecting last checkpoint.
 UpperCamelCase_					:						Optional[Any] =					None
 if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
  UpperCamelCase_					:						Dict =					get_last_checkpoint(training_args.output_dir	)
  if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
   raise ValueError(
       F"Output directory ({training_args.output_dir}) already exists and is not empty. "
       'Use --overwrite_output_dir to overcome.'	)
  elif last_checkpoint is not None:
   logger.info(
       F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
       'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'	)

    # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'  ,  datefmt='%m/%d/%Y %H:%M:%S'  ,  handlers=[logging.StreamHandler(sys.stdout	)]  ,  )
 logger.setLevel(logging.INFO if is_main_process(training_args.local_rank	) else logging.WARN	)

 # Log on each process the small summary:
 logger.warning(
     F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
     + F"distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}"	)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank	):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info('Training/evaluation parameters %s'  ,  _a	)

 # Set seed before initializing model.
 set_seed(training_args.seed	)

 # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
 # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
 # (the dataset will be downloaded automatically from the datasets Hub).
 #
 # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
 # 'text' is found. You can easily tweak this behavior (see below).
 #
 # In distributed training, the load_dataset function guarantee that only one local process can concurrently
 # download the dataset.
 if data_args.dataset_name is not None:
  # Downloading and loading a dataset from the hub.
  UpperCamelCase_					:						Optional[Any] =					load_dataset(data_args.dataset_name  ,  data_args.dataset_config_name	)
  if "validation" not in datasets.keys():
   UpperCamelCase_					:						int =					load_dataset(
       data_args.dataset_name  ,  data_args.dataset_config_name  ,  split=F"train[:{data_args.validation_split_percentage}%]"  ,  )
   UpperCamelCase_					:						List[Any] =					load_dataset(
       data_args.dataset_name  ,  data_args.dataset_config_name  ,  split=F"train[{data_args.validation_split_percentage}%:]"  ,  )
 else:
  UpperCamelCase_					:						Any =					{}
  if data_args.train_file is not None:
   UpperCamelCase_					:						List[Any] =					data_args.train_file
  if data_args.validation_file is not None:
   UpperCamelCase_					:						Optional[Any] =					data_args.validation_file
  UpperCamelCase_					:						Optional[int] =					data_args.train_file.split('.'	)[-1]
  if extension == "txt":
   UpperCamelCase_					:						Tuple =					'''text'''
  UpperCamelCase_					:						Any =					load_dataset(_a  ,  data_files=_a	)
 # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
 # https://huggingface.co/docs/datasets/loading_datasets.html.

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 UpperCamelCase_					:						Tuple =					{
     '''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:
  UpperCamelCase_					:						Tuple =					AutoConfig.from_pretrained(model_args.config_name  ,  **_a	)
 elif model_args.model_name_or_path:
  UpperCamelCase_					:						int =					AutoConfig.from_pretrained(model_args.model_name_or_path  ,  **_a	)
 else:
  UpperCamelCase_					:						List[Any] =					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}"	)

 UpperCamelCase_					:						List[str] =					{
     '''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:
  UpperCamelCase_					:						List[Any] =					AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,  **_a	)
 elif model_args.model_name_or_path:
  UpperCamelCase_					:						str =					AutoTokenizer.from_pretrained(model_args.model_name_or_path  ,  **_a	)
 else:
  raise ValueError(
      'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
      'You can do it from another script, save it, and load it from here, using --tokenizer_name.'	)

 if model_args.model_name_or_path:
  UpperCamelCase_					:						Optional[int] =					AutoModelForMaskedLM.from_pretrained(
      model_args.model_name_or_path  ,  from_tf=bool('.ckpt' in model_args.model_name_or_path	)  ,  config=_a  ,  cache_dir=model_args.cache_dir  ,  revision=model_args.model_revision  ,  use_auth_token=True if model_args.use_auth_token else None  ,  )
 else:
  logger.info('Training new model from scratch'	)
  UpperCamelCase_					:						str =					AutoModelForMaskedLM.from_config(_a	)

 model.resize_token_embeddings(len(_a	)	)

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

 UpperCamelCase_					:						int =					'''max_length''' if data_args.pad_to_max_length else False

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

 UpperCamelCase_					:						List[Any] =					datasets.map(
     _a  ,  batched=_a  ,  num_proc=data_args.preprocessing_num_workers  ,  remove_columns=[text_column_name]  ,  load_from_cache_file=not data_args.overwrite_cache  ,  )

 # Add the chinese references if provided
 if data_args.train_ref_file is not None:
  UpperCamelCase_					:						List[Any] =					add_chinese_references(tokenized_datasets['train']  ,  data_args.train_ref_file	)
 if data_args.validation_ref_file is not None:
  UpperCamelCase_					:						Optional[Any] =					add_chinese_references(
      tokenized_datasets['validation']  ,  data_args.validation_ref_file	)
 # If we have ref files, need to avoid it removed by trainer
 UpperCamelCase_					:						List[str] =					data_args.train_ref_file or data_args.validation_ref_file
 if has_ref:
  UpperCamelCase_					:						List[Any] =					False

 # Data collator
 # This one will take care of randomly masking the tokens.
 UpperCamelCase_					:						Optional[Any] =					DataCollatorForWholeWordMask(tokenizer=_a  ,  mlm_probability=data_args.mlm_probability	)

 # Initialize our Trainer
 UpperCamelCase_					:						int =					Trainer(
     model=_a  ,  args=_a  ,  train_dataset=tokenized_datasets['train'] if training_args.do_train else None  ,  eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None  ,  tokenizer=_a  ,  data_collator=_a  ,  )

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

  UpperCamelCase_					:						Optional[Any] =					os.path.join(training_args.output_dir  ,  'train_results.txt'	)
  if trainer.is_world_process_zero():
   with open(_a  ,  'w'	) as writer:
    logger.info('***** Train results *****'	)
    for key, value in sorted(train_result.metrics.items()	):
     logger.info(F"  {key} = {value}"	)
     writer.write(F"{key} = {value}\n"	)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
   trainer.state.save_to_json(os.path.join(training_args.output_dir  ,  'trainer_state.json'	)	)

    # Evaluation
 UpperCamelCase_					:						Any =					{}
 if training_args.do_eval:
  logger.info('*** Evaluate ***'	)

  UpperCamelCase_					:						Dict =					trainer.evaluate()

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

  UpperCamelCase_					:						Tuple =					os.path.join(training_args.output_dir  ,  'eval_results_mlm_wwm.txt'	)
  if trainer.is_world_process_zero():
   with open(_a  ,  'w'	) as writer:
    logger.info('***** Eval results *****'	)
    for key, value in sorted(results.items()	):
     logger.info(F"  {key} = {value}"	)
     writer.write(F"{key} = {value}\n"	)

 return results





def    __lowercase     (						lowerCamelCase					:     Optional[int]	):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
   main()








"""simple docstring"""

import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

lowercase__    :  Dict							    =	[
    '''word_embeddings_layernorm.weight''',
    '''word_embeddings_layernorm.bias''',
    '''input_layernorm.weight''',
    '''input_layernorm.bias''',
    '''post_attention_layernorm.weight''',
    '''post_attention_layernorm.bias''',
    '''self_attention.dense.bias''',
    '''mlp.dense_4h_to_h.bias''',
    '''ln_f.weight''',
    '''ln_f.bias''',
]

lowercase__    :  str							    =	[
    '''mlp.dense_4h_to_h.weight''',
    '''self_attention.dense.weight''',
]




def __lowercase			(							_a		,			_a    ):
    snake_case_ :     Optional[int]				=	{
        '''word_embeddings.weight''': '''word_embeddings.weight''',
        '''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
        '''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
        '''weight''': '''ln_f.weight''',
        '''bias''': '''ln_f.bias''',
    }

    if key in layer_rename_map:
        return layer_rename_map[key]

    # Handle transformer blocks
    snake_case_ :     List[Any]				=	int(re.match(r'''.*layer_(\d*).*'''		,			_a    )[1]    )
    layer_number -= 3
    return f"h.{layer_number}." + key




def __lowercase			(							_a    ):
    if dtype == torch.bool:
        return 1 / 8
    snake_case_ :     Dict				=	re.search(r'''[^\d](\d+)$'''		,			str(_a    )    )
    if bit_search is None:
        raise ValueError(f"`dtype` is not a valid dtype: {dtype}."    )
    snake_case_ :     Optional[int]				=	int(bit_search.groups()[0]    )
    return bit_size // 8




def __lowercase			(							_a		,			_a		,			_a		,			_a		,			_a    ):
    # Construct model
    if bloom_config_file == "":
        snake_case_ :     int				=	BloomConfig()
    else:
        snake_case_ :     List[str]				=	BloomConfig.from_json_file(_a    )

    if shard_model:
        snake_case_ :     List[str]				=	os.listdir(_a    )
        snake_case_ :     int				=	sorted(filter(lambda _a    : s.startswith('''layer'''    ) and "model_00" in s		,			_a    )    )

        snake_case_ :     List[str]				=	{'''weight_map''': {}, '''metadata''': {}}
        snake_case_ :     Any				=	0

        snake_case_ :     Union[str, Any]				=	None

        snake_case_ :     List[str]				=	BloomConfig()

        for j, file in enumerate(_a    ):
            print('''Processing file: {}'''.format(_a    )    )
            snake_case_ :     Dict				=	None

            for i in range(_a    ):
                # load all TP files
                snake_case_ :     Union[str, Any]				=	file.replace('''model_00'''		,			f"model_0{i}"    )
                snake_case_ :     List[str]				=	torch.load(os.path.join(_a		,			_a    )		,			map_location='''cpu'''    )

                # Rename keys in the transformers names
                snake_case_ :     str				=	list(temp.keys()    )
                for key in keys:
                    snake_case_ :     Any				=	temp.pop(_a    )

                if tensors is None:
                    snake_case_ :     Any				=	temp
                else:
                    for key in tensors.keys():
                        if any(key.endswith(_a    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
                            # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                            tensors[key] += temp[key]
                        else:
                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                            snake_case_ :     Tuple				=	1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN    ) else 0
                            # We concatenate these weights accross TP ranks
                            snake_case_ :     List[str]				=	torch.cat([tensors[key], temp[key]]		,			dim=_a    )

            # Divide by the number of TP the weights we want to average
            for key in tensors.keys():
                if any(key.endswith(_a    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
                    snake_case_ :     Any				=	tensors[key] / pretraining_tp
            torch.save(
                _a		,			os.path.join(
                    _a		,			'''pytorch_model_{}-of-{}.bin'''.format(str(j + 1    ).zfill(5    )		,			str(len(_a    )    ).zfill(5    )    )		,			)		,			)

            for key in tensors.keys():
                snake_case_ :     List[str]				=	tensors[key]
                total_size += value.numel() * get_dtype_size(value.dtype    )
                if key not in index_dict["weight_map"]:
                    snake_case_ :     List[str]				=	'''pytorch_model_{}-of-{}.bin'''.format(
                        str(j + 1    ).zfill(5    )		,			str(len(_a    )    ).zfill(5    )    )

        snake_case_ :     int				=	BloomConfig()
        snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
        snake_case_ :     Dict				=	total_size
        with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
            f.write(config.to_json_string()    )
        with open(os.path.join(_a		,			WEIGHTS_NAME + '''.index.json'''    )		,			'''w'''		,			encoding='''utf-8'''    ) as f:
            snake_case_ :     Tuple				=	json.dumps(_a		,			indent=2		,			sort_keys=_a    ) + '''\n'''
            f.write(_a    )
    else:
        snake_case_ :     Union[str, Any]				=	BloomModel(_a    )

        snake_case_ :     List[str]				=	os.listdir(_a    )
        snake_case_ :     Dict				=	sorted(filter(lambda _a    : s.startswith('''layer'''    ) and "model_00" in s		,			_a    )    )

        snake_case_ :     List[Any]				=	None
        for i, file in enumerate(_a    ):
            snake_case_ :     Optional[Any]				=	None
            for i in range(_a    ):
                # load all TP files
                snake_case_ :     List[str]				=	file.replace('''model_00'''		,			f"model_0{i}"    )
                snake_case_ :     Optional[Any]				=	torch.load(os.path.join(_a		,			_a    )		,			map_location='''cpu'''    )

                # Rename keys in the transformers names
                snake_case_ :     str				=	list(temp.keys()    )
                for key in keys:
                    snake_case_ :     str				=	temp.pop(_a    )

                if tensors is None:
                    snake_case_ :     int				=	temp
                else:
                    for key in tensors.keys():
                        # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                        if any(key.endswith(_a    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
                            tensors[key] += temp[key]
                        else:
                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                            snake_case_ :     Tuple				=	1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN    ) else 0
                            # We concatenate these weights accross TP ranks
                            snake_case_ :     Optional[Any]				=	torch.cat([tensors[key], temp[key]]		,			dim=_a    )

            # Divide by the number of TP the weights we want to average
            for key in tensors.keys():
                if any(key.endswith(_a    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
                    snake_case_ :     Union[str, Any]				=	tensors[key] / pretraining_tp

            snake_case_ :     Any				=	model.load_state_dict(_a		,			strict=_a    )
            assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
            if missing_keys is None:
                snake_case_ :     Optional[int]				=	set(other_keys.missing_keys    )
            else:
                snake_case_ :     Tuple				=	missing_keys.intersection(set(other_keys.missing_keys    )    )

        assert not missing_keys, f"The keys {missing_keys} are missing"

        # Save pytorch-model
        os.makedirs(_a		,			exist_ok=_a    )
        snake_case_ :     List[str]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
        snake_case_ :     Optional[Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
        print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}"    )
        if config.torch_dtype is not None:
            snake_case_ :     Optional[Any]				=	model.to(config.torch_dtype    )
        torch.save(model.state_dict()		,			_a    )
        print(f"Save configuration file to {pytorch_config_dump_path}"    )
        with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
            f.write(config.to_json_string()    )


if __name__ == "__main__":
     lowercase__    :  str							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--bloom_checkpoint_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to the Megatron-LM checkpoint path.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--bloom_config_file''',
         default='''''',
         type=str,
         help=(
             '''An optional config json file corresponding to the pre-trained model. \n'''
             '''This specifies the model architecture.'''
         ),
     )
     parser.add_argument(
         '''--shard_model''',
         action='''store_true''',
         help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
     )
     parser.add_argument(
         '''--pretraining_tp''',
         default=4,
         type=int,
         help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
     )
     lowercase__    :  List[Any]							    =	parser.parse_args()
     convert_bloom_checkpoint_to_pytorch(
         args.bloom_checkpoint_path,
         args.bloom_config_file,
         args.pytorch_dump_folder_path,
         args.shard_model,
         args.pretraining_tp,
     )





