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

def a__    (       A_,     A_					):






  '''simple docstring'''





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

def a__    (       A_					):






  '''simple docstring'''





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

def a__    (       A_,     A_,     A_,     A_,     A_					):






  '''simple docstring'''





  if bloom_config_file == "":
    __magic_name__				     =       BloomConfig()
  else:
    __magic_name__				     =       BloomConfig.from_json_file(A_					)

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

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

    __magic_name__				     =       None

    __magic_name__				     =       BloomConfig()

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

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

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

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

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

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

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

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

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

      __magic_name__				     =       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:
        __magic_name__				     =       set(other_keys.missing_keys					)
      else:
        __magic_name__				     =       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_					)
    __magic_name__				     =       pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
    __magic_name__				     =       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:
      __magic_name__				     =       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			:     Dict           =   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			:     Optional[int]           =   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

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		UpperCAmelCase_      (  __lowercase		):





     def __lt__( self       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    )				->  List[Any]:
        return self[-1] < other[-1]





     def __eq__( self       :      str    ,      UpperCAmelCase__       :      List[str]    )				->  Tuple:
        return self[-1] == other[-1]





def 	a_					( lowerCamelCase	:     list				):
   lowerCAmelCase     =			[]
   # sort into stacks
   for element in collection:
      lowerCAmelCase     =			Stack([element]				)
      lowerCAmelCase     =			bisect_left(lowerCamelCase	, lowerCamelCase				)
      if i != len(lowerCamelCase				):
         stacks[i].append(lowerCamelCase				)
      else:
         stacks.append(lowerCamelCase				)

    # use a heap-based merge to merge stack efficiently
   lowerCAmelCase     =			merge(*(reversed(lowerCamelCase				) for stack in stacks)				)
   return collection


if __name__ == "__main__":
       __snake_case    						=input("""Enter numbers separated by a comma:\n""").strip()
       __snake_case    						=[int(item) for item in user_input.split(""",""")]
       print(patience_sort(unsorted))





