





"""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()

lowerCamelCase_       =		[
    "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",
]

lowerCamelCase_       =		[
    "mlp.dense_4h_to_h.weight",
    "self_attention.dense.weight",
]



def 					__lowerCamelCase       (							a_					:		List[Any]			,       a_					:		Any							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :str		    =  {
          '''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
      __SCREAMING_SNAKE_CASE       :Tuple		    =  int(re.match(r'''.*layer_(\d*).*'''			,       a_							)[1]							)
      layer_number -= 3
      return f'''h.{layer_number}.''' + key



def 					__lowerCamelCase       (							a_					:		int							)      ->				Optional[Any]:
      if dtype == torch.bool:
            return 1 / 8
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  re.search(r'''[^\d](\d+)$'''			,       str(a_							)							)
      if bit_search is None:
            raise ValueError(f'''`dtype` is not a valid dtype: {dtype}.'''							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  int(bit_search.groups()[0]							)
      return bit_size // 8



def 					__lowerCamelCase       (							a_					:		List[str]			,       a_					:		str			,       a_					:		Any			,       a_					:		Any			,       a_					:		List[str]							)      ->				int:
      # Construct model
      if bloom_config_file == "":
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  BloomConfig()
      else:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  BloomConfig.from_json_file(a_							)

      if shard_model:
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  os.listdir(a_							)
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  sorted(filter(lambda a_							: s.startswith('''layer'''							) and "model_00" in s			,       a_							)							)

            __SCREAMING_SNAKE_CASE       :Any		    =  {'''weight_map''': {}, '''metadata''': {}}
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  0

            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  None

            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  BloomConfig()

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

                  for i in range(a_							):
                        # load all TP files
                        __SCREAMING_SNAKE_CASE       :Optional[int]		    =  file.replace('''model_00'''			,       f'''model_0{i}'''							)
                        __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.load(os.path.join(a_			,       a_							)			,       map_location='''cpu'''							)

                        # Rename keys in the transformers names
                        __SCREAMING_SNAKE_CASE       :List[str]		    =  list(temp.keys()							)
                        for key in keys:
                              __SCREAMING_SNAKE_CASE       :Any		    =  temp.pop(a_							)

                        if tensors is None:
                              __SCREAMING_SNAKE_CASE       :int		    =  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
                                          __SCREAMING_SNAKE_CASE       :Optional[int]		    =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN							) else 0
                                          # We concatenate these weights accross TP ranks
                                          __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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							):
                              __SCREAMING_SNAKE_CASE       :List[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():
                        __SCREAMING_SNAKE_CASE       :Any		    =  tensors[key]
                        total_size += value.numel() * get_dtype_size(value.dtype							)
                        if key not in index_dict["weight_map"]:
                              __SCREAMING_SNAKE_CASE       :Tuple		    =  '''pytorch_model_{}-of-{}.bin'''.format(
                                  str(j + 1							).zfill(5							)			,       str(len(a_							)							).zfill(5							)							)

            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  BloomConfig()
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  pytorch_dump_folder_path + '''/''' + CONFIG_NAME
            __SCREAMING_SNAKE_CASE       :str		    =  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:
                  __SCREAMING_SNAKE_CASE       :str		    =  json.dumps(a_			,       indent=2			,       sort_keys=a_							) + '''\n'''
                  f.write(a_							)
      else:
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  BloomModel(a_							)

            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  os.listdir(a_							)
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  sorted(filter(lambda a_							: s.startswith('''layer'''							) and "model_00" in s			,       a_							)							)

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

                        # Rename keys in the transformers names
                        __SCREAMING_SNAKE_CASE       :List[Any]		    =  list(temp.keys()							)
                        for key in keys:
                              __SCREAMING_SNAKE_CASE       :List[Any]		    =  temp.pop(a_							)

                        if tensors is None:
                              __SCREAMING_SNAKE_CASE       :Optional[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
                                          __SCREAMING_SNAKE_CASE       :Dict		    =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN							) else 0
                                          # We concatenate these weights accross TP ranks
                                          __SCREAMING_SNAKE_CASE       :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							):
                              __SCREAMING_SNAKE_CASE       :Any		    =  tensors[key] / pretraining_tp

                  __SCREAMING_SNAKE_CASE       :Optional[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:
                        __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  set(other_keys.missing_keys							)
                  else:
                        __SCREAMING_SNAKE_CASE       :Dict		    =  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_							)
            __SCREAMING_SNAKE_CASE       :Dict		    =  pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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:
                  __SCREAMING_SNAKE_CASE       :Dict		    =  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__":
  lowerCamelCase_       =		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",
  )
  lowerCamelCase_       =		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,
  )






"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''xlm-prophetnet'''
       SCREAMING_SNAKE_CASE_       :				Any     						=  ['''past_key_values''']
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  {
           '''num_attention_heads''': '''num_encoder_attention_heads''',
       }




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = "gelu"		,SCREAMING_SNAKE_CASE__ = 3_05_22		,SCREAMING_SNAKE_CASE__ = 10_24		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 5_12		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = 32		,SCREAMING_SNAKE_CASE__ = 1_28		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 2		,**SCREAMING_SNAKE_CASE__		,)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  num_encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_encoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  decoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_decoder_layers
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_decoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[str]		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Dict		    =  init_std  # Normal(0, this parameter)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  activation_function

             # parameters for xlmprophetnet
             __SCREAMING_SNAKE_CASE       :Tuple		    =  ngram
             __SCREAMING_SNAKE_CASE       :int		    =  num_buckets
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  relative_max_distance
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  disable_ngram_loss
             __SCREAMING_SNAKE_CASE       :Dict		    =  eps

             # 3 Types of Dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  attention_dropout
             __SCREAMING_SNAKE_CASE       :Dict		    =  activation_dropout
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  dropout

             __SCREAMING_SNAKE_CASE       :int		    =  use_cache

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,add_cross_attention=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)




       @property
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             return self.num_encoder_layers + self.num_decoder_layers




       @num_hidden_layers.setter
       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             raise NotImplementedError(
                 '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
                 ''' `num_decoder_layers`.'''							)