
'''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()

SCREAMING_SNAKE_CASE     =       [
    """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""",
]

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







def snake_case_     (					lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Any				       =							{
      "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
  UpperCAmelCase__      :					List[Any]				       =							int(re.match(R".*layer_(\d*).*"   ,       lowercase__		)[1]		)
  layer_number -= 3
  return F"""h.{layer_number}.""" + key







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







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__   ,       lowercase__		):
  # Construct model
  if bloom_config_file == "":
    UpperCAmelCase__      :					Tuple				       =							BloomConfig()
  else:
    UpperCAmelCase__      :					List[str]				       =							BloomConfig.from_json_file(lowercase__		)

  if shard_model:
    UpperCAmelCase__      :					Any				       =							os.listdir(lowercase__		)
    UpperCAmelCase__      :					Any				       =							sorted(filter(lambda lowercase__		: s.startswith("layer"		) and "model_00" in s   ,       lowercase__		)		)

    UpperCAmelCase__      :					List[Any]				       =							{"weight_map": {}, "metadata": {}}
    UpperCAmelCase__      :					Union[str, Any]				       =							0

    UpperCAmelCase__      :					Any				       =							None

    UpperCAmelCase__      :					Tuple				       =							BloomConfig()

    for j, file in enumerate(lowercase__		):
      print("Processing file: {}".format(lowercase__		)		)
      UpperCAmelCase__      :					List[str]				       =							None

      for i in range(lowercase__		):
        # load all TP files
        UpperCAmelCase__      :					Optional[Any]				       =							file.replace("model_00"   ,       F"""model_0{i}"""		)
        UpperCAmelCase__      :					Optional[Any]				       =							torch.load(os.path.join(lowercase__   ,       lowercase__		)   ,       map_location="cpu"		)

        # Rename keys in the transformers names
        UpperCAmelCase__      :					List[str]				       =							list(temp.keys()		)
        for key in keys:
          UpperCAmelCase__      :					Tuple				       =							temp.pop(lowercase__		)

        if tensors is None:
          UpperCAmelCase__      :					List[Any]				       =							temp
        else:
          for key in tensors.keys():
            if any(key.endswith(lowercase__		) 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
              UpperCAmelCase__      :					str				       =							1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN		) else 0
              # We concatenate these weights accross TP ranks
              UpperCAmelCase__      :					Optional[Any]				       =							torch.cat([tensors[key], temp[key]]   ,       dim=lowercase__		)

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

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

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

    UpperCAmelCase__      :					List[Any]				       =							os.listdir(lowercase__		)
    UpperCAmelCase__      :					str				       =							sorted(filter(lambda lowercase__		: s.startswith("layer"		) and "model_00" in s   ,       lowercase__		)		)

    UpperCAmelCase__      :					int				       =							None
    for i, file in enumerate(lowercase__		):
      UpperCAmelCase__      :					Optional[Any]				       =							None
      for i in range(lowercase__		):
        # load all TP files
        UpperCAmelCase__      :					List[Any]				       =							file.replace("model_00"   ,       F"""model_0{i}"""		)
        UpperCAmelCase__      :					Optional[int]				       =							torch.load(os.path.join(lowercase__   ,       lowercase__		)   ,       map_location="cpu"		)

        # Rename keys in the transformers names
        UpperCAmelCase__      :					Dict				       =							list(temp.keys()		)
        for key in keys:
          UpperCAmelCase__      :					Optional[int]				       =							temp.pop(lowercase__		)

        if tensors is None:
          UpperCAmelCase__      :					List[str]				       =							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(lowercase__		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
              tensors[key] += temp[key]
            else:
              # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
              UpperCAmelCase__      :					Union[str, Any]				       =							1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN		) else 0
              # We concatenate these weights accross TP ranks
              UpperCAmelCase__      :					List[str]				       =							torch.cat([tensors[key], temp[key]]   ,       dim=lowercase__		)

            # Divide by the number of TP the weights we want to average
      for key in tensors.keys():
        if any(key.endswith(lowercase__		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
          UpperCAmelCase__      :					Dict				       =							tensors[key] / pretraining_tp

      UpperCAmelCase__      :					Any				       =							model.load_state_dict(lowercase__   ,       strict=lowercase__		)
      assert not other_keys.unexpected_keys, F"""The keys {other_keys.unexpected_keys} are unexpected"""
      if missing_keys is None:
        UpperCAmelCase__      :					Union[str, Any]				       =							set(other_keys.missing_keys		)
      else:
        UpperCAmelCase__      :					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(lowercase__   ,       exist_ok=lowercase__		)
    UpperCAmelCase__      :					Dict				       =							pytorch_dump_folder_path + "/" + WEIGHTS_NAME
    UpperCAmelCase__      :					List[str]				       =							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:
      UpperCAmelCase__      :					Union[str, Any]				       =							model.to(config.torch_dtype		)
    torch.save(model.state_dict()   ,       lowercase__		)
    print(F"""Save configuration file to {pytorch_config_dump_path}"""		)
    with open(lowercase__   ,       "w"   ,       encoding="utf-8"		) as f:
      f.write(config.to_json_string()		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       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""",
      )
      SCREAMING_SNAKE_CASE     =       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'''





def snake_case_     (					lowercase__		):
  return sum(i for i in range(1   ,       number // 2 + 1		) if number % i == 0		) == number


if __name__ == "__main__":
      print("""Program to check whether a number is a Perfect number or not...""")
      SCREAMING_SNAKE_CASE     =       int(input("""Enter number: """).strip())
      print(F'{number} is {"" if perfect(number) else "not "}a Perfect Number.')



