





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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Union[str, 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			:      Optional[int] 			=     int(re.match(r".*layer_(\d*).*"							,     _lowerCAmelCase  )[1]  )
 layer_number -= 3
 return F"""h.{layer_number}.""" + key



def 	A_       (  _lowerCAmelCase  )		->				Optional[int]:
 if dtype == torch.bool:
  return 1 / 8
 UpperCamelCase			:      Union[str, Any] 			=     re.search(r"[^\d](\d+)$"							,     str(_lowerCAmelCase  )  )
 if bit_search is None:
  raise ValueError(F"""`dtype` is not a valid dtype: {dtype}."""  )
 UpperCamelCase			:      Tuple 			=     int(bit_search.groups()[0]  )
 return bit_size // 8



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[int]:
 # Construct model
 if bloom_config_file == "":
  UpperCamelCase			:      int 			=     BloomConfig()
 else:
  UpperCamelCase			:      List[Any] 			=     BloomConfig.from_json_file(_lowerCAmelCase  )

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

  UpperCamelCase			:      str 			=     {"weight_map": {}, "metadata": {}}
  UpperCamelCase			:      str 			=     0

  UpperCamelCase			:      List[Any] 			=     None

  UpperCamelCase			:      Optional[Any] 			=     BloomConfig()

  for j, file in enumerate(_lowerCAmelCase  ):
   print("Processing file: {}".format(_lowerCAmelCase  )  )
   UpperCamelCase			:      Tuple 			=     None

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

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

    if tensors is None:
     UpperCamelCase			:      Union[str, Any] 			=     temp
    else:
     for key in tensors.keys():
      if any(key.endswith(_lowerCAmelCase  ) 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			:      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
       UpperCamelCase			:      Optional[int] 			=     torch.cat([tensors[key], temp[key]]							,     dim=_lowerCAmelCase  )

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

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

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

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

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

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

    if tensors is None:
     UpperCamelCase			:      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(_lowerCAmelCase  ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH  ):
       tensors[key] += temp[key]
      else:
       # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
       UpperCamelCase			:      List[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			:      Dict 			=     torch.cat([tensors[key], temp[key]]							,     dim=_lowerCAmelCase  )

            # Divide by the number of TP the weights we want to average
   for key in tensors.keys():
    if any(key.endswith(_lowerCAmelCase  ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH  ):
     UpperCamelCase			:      Union[str, Any] 			=     tensors[key] / pretraining_tp

   UpperCamelCase			:      Dict 			=     model.load_state_dict(_lowerCAmelCase							,     strict=_lowerCAmelCase  )
   assert not other_keys.unexpected_keys, F"""The keys {other_keys.unexpected_keys} are unexpected"""
   if missing_keys is None:
    UpperCamelCase			:      Tuple 			=     set(other_keys.missing_keys  )
   else:
    UpperCamelCase			:      Tuple 			=     missing_keys.intersection(set(other_keys.missing_keys  )  )

  assert not missing_keys, F"""The keys {missing_keys} are missing"""

  # Save pytorch-model
  os.makedirs(_lowerCAmelCase							,     exist_ok=_lowerCAmelCase  )
  UpperCamelCase			:      List[str] 			=     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			:      List[str] 			=     model.to(config.torch_dtype  )
  torch.save(model.state_dict()							,     _lowerCAmelCase  )
  print(F"""Save configuration file to {pytorch_config_dump_path}"""  )
  with open(_lowerCAmelCase							,     "w"							,     encoding="utf-8"  ) as f:
   f.write(config.to_json_string()  )


if __name__ == "__main__":
    __lowerCamelCase :  Optional[Any]										=  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 :  Union[str, Any]										=  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,
    )







from PIL import Image



def 	A_       (  _lowerCAmelCase  )		->				Image:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     image.size
 UpperCamelCase			:      Union[str, Any] 			=     0
 UpperCamelCase			:      List[str] 			=     image.load()
 for i in range(_lowerCAmelCase  ):
  for j in range(_lowerCAmelCase  ):
   UpperCamelCase			:      List[Any] 			=     pixels[j, i]
   mean += pixel
 mean //= width * height

 for j in range(_lowerCAmelCase  ):
  for i in range(_lowerCAmelCase  ):
   UpperCamelCase			:      Union[str, Any] 			=     255 if pixels[i, j] > mean else 0
 return image


if __name__ == "__main__":
    __lowerCamelCase :  Union[str, Any]										=  mean_threshold(Image.open("""path_to_image""").convert("""L"""))
    image.save("""output_image_path""")
