

"""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()

UpperCAmelCase													=[
    "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",
]

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



def   _A					(	_a	:						Tuple     ,   _a	:						Dict				):



      """simple docstring"""






      A				  =		{
          """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
      A				  =		int(re.match(r""".*layer_(\d*).*"""     ,   _a				)[1]				)
      layer_number -= 3
      return f'h.{layer_number}.' + key



def   _A					(	_a	:						Dict				):



      """simple docstring"""






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



def   _A					(	_a	:						Tuple     ,   _a	:						List[str]     ,   _a	:						Tuple     ,   _a	:						str     ,   _a	:						List[str]				):



      """simple docstring"""






      if bloom_config_file == "":
            A				  =		BloomConfig()
      else:
            A				  =		BloomConfig.from_json_file(_a				)

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

            A				  =		{"""weight_map""": {}, """metadata""": {}}
            A				  =		0

            A				  =		None

            A				  =		BloomConfig()

            for j, file in enumerate(_a				):
                  print("""Processing file: {}""".format(_a				)				)
                  A				  =		None

                  for i in range(_a				):
                        # load all TP files
                        A				  =		file.replace("""model_00"""     ,   f'model_0{i}'				)
                        A				  =		torch.load(os.path.join(_a     ,   _a				)     ,   map_location="""cpu"""				)

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

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

            A				  =		BloomConfig()
            A				  =		pytorch_dump_folder_path + """/""" + CONFIG_NAME
            A				  =		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:
                  A				  =		json.dumps(_a     ,   indent=2     ,   sort_keys=_a				) + """\n"""
                  f.write(_a				)
      else:
            A				  =		BloomModel(_a				)

            A				  =		os.listdir(_a				)
            A				  =		sorted(filter(lambda _a				: s.startswith("""layer"""				) and "model_00" in s     ,   _a				)				)

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

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

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

                  A				  =		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:
                        A				  =		set(other_keys.missing_keys				)
                  else:
                        A				  =		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				)
            A				  =		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
            A				  =		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:
                  A				  =		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__":
  UpperCAmelCase													=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",
  )
  UpperCAmelCase													=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 __future__ import annotations



def   _A					(	_a	:						list[float]     ,   _a	:						list[float]				):



      """simple docstring"""






      A				  =		sorted(numsa + numsa				)
      A  ,			A				  =		divmod(len(_a				)     ,   2				)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of first array: ").split()]
  UpperCAmelCase													=[float(x) for x in input("Enter the elements of second array: ").split()]
  print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")




