


'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   list					):
  '''simple docstring'''





  if not isinstance(A__							,    A__					):
    raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""					)
  if len(A__					) == 0:
    raise ValueError("""Input list must be a non empty list"""					)
  if len(A__					) == 1:
    return True
  lowerCAmelCase_			:      Optional[Any]			=   series[1] - series[0]
  for index in range(len(A__					) - 1					):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True







def 				UpperCamelCase_			(    A__					:   list					):
  '''simple docstring'''





  if not isinstance(A__							,    A__					):
    raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""					)
  if len(A__					) == 0:
    raise ValueError("""Input list must be a non empty list"""					)
  lowerCAmelCase_			:      List[str]			=   0
  for val in series:
    answer += val
  return answer / len(A__					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''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()

__A					:  Any											=      [
    "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",
]

__A					:  Any											=      [
    "mlp.dense_4h_to_h.weight",
    "self_attention.dense.weight",
]







def 				UpperCamelCase_			(    A__					:   str							,    A__					:   str					):
  '''simple docstring'''





  lowerCAmelCase_			:      Dict			=   {
      """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
  lowerCAmelCase_			:      List[str]			=   int(re.match(R""".*layer_(\d*).*"""							,    A__					)[1]					)
  layer_number -= 3
  return f'h.{layer_number}.' + key







def 				UpperCamelCase_			(    A__					:   List[str]					):
  '''simple docstring'''





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







def 				UpperCamelCase_			(    A__					:   int							,    A__					:   Tuple							,    A__					:   Any							,    A__					:   Tuple							,    A__					:   List[str]					):
  '''simple docstring'''





  if bloom_config_file == "":
    lowerCAmelCase_			:      Any			=   BloomConfig()
  else:
    lowerCAmelCase_			:      Any			=   BloomConfig.from_json_file(A__					)

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

    lowerCAmelCase_			:      Optional[Any]			=   {"""weight_map""": {}, """metadata""": {}}
    lowerCAmelCase_			:      Optional[Any]			=   0

    lowerCAmelCase_			:      List[str]			=   None

    lowerCAmelCase_			:      Optional[int]			=   BloomConfig()

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

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

        # Rename keys in the transformers names
        lowerCAmelCase_			:      str			=   list(temp.keys()					)
        for key in keys:
          lowerCAmelCase_			:      Optional[int]			=   temp.pop(A__					)

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

    lowerCAmelCase_			:      str			=   BloomConfig()
    lowerCAmelCase_			:      List[Any]			=   pytorch_dump_folder_path + """/""" + CONFIG_NAME
    lowerCAmelCase_			:      List[Any]			=   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:
      lowerCAmelCase_			:      Optional[int]			=   json.dumps(A__							,    indent=2							,    sort_keys=A__					) + """\n"""
      f.write(A__					)
  else:
    lowerCAmelCase_			:      int			=   BloomModel(A__					)

    lowerCAmelCase_			:      Union[str, Any]			=   os.listdir(A__					)
    lowerCAmelCase_			:      Tuple			=   sorted(filter(lambda A__					: s.startswith("""layer"""					) and "model_00" in s							,    A__					)					)

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

        # Rename keys in the transformers names
        lowerCAmelCase_			:      Union[str, Any]			=   list(temp.keys()					)
        for key in keys:
          lowerCAmelCase_			:      str			=   temp.pop(A__					)

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

      lowerCAmelCase_			:      Optional[int]			=   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:
        lowerCAmelCase_			:      Any			=   set(other_keys.missing_keys					)
      else:
        lowerCAmelCase_			:      List[Any]			=   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__					)
    lowerCAmelCase_			:      Dict			=   pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
    lowerCAmelCase_			:      Tuple			=   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:
      lowerCAmelCase_			:      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__":
  __A					:  Tuple											=      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",
  )
  __A					:  Dict											=      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,
  )



