






from ....configuration_utils import PretrainedConfig
from ....utils import logging


A_		:		List[str]		     =			logging.get_logger(__name__)

# TODO: upload to AWS
A_		:		List[Any]		     =			{
    'yjernite/retribert-base-uncased': (
        'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
    ),
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Optional[int]									='''retribert'''





       def __init__( self  ,      _lowerCamelCase=3_0_5_2_2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=8  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=3_0_7_2  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1e-12  ,      _lowerCamelCase=True  ,      _lowerCamelCase=1_2_8  ,      _lowerCamelCase=0  ,      **_lowerCamelCase  ,      ):
         super().__init__(pad_token_id=_lowerCamelCase  ,      **_lowerCamelCase							)

         UpperCamelCase_: List[Any]   				=    vocab_size
         UpperCamelCase_: Any   				=    hidden_size
         UpperCamelCase_: Dict   				=    num_hidden_layers
         UpperCamelCase_: Tuple   				=    num_attention_heads
         UpperCamelCase_: List[str]   				=    hidden_act
         UpperCamelCase_: str   				=    intermediate_size
         UpperCamelCase_: Optional[int]   				=    hidden_dropout_prob
         UpperCamelCase_: Optional[int]   				=    attention_probs_dropout_prob
         UpperCamelCase_: Optional[Any]   				=    max_position_embeddings
         UpperCamelCase_: Dict   				=    type_vocab_size
         UpperCamelCase_: Optional[Any]   				=    initializer_range
         UpperCamelCase_: Union[str, Any]   				=    layer_norm_eps
         UpperCamelCase_: Optional[Any]   				=    share_encoders
         UpperCamelCase_: str   				=    projection_dim







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_		:		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',
]

A_		:		Optional[int]		     =			[
    'mlp.dense_4h_to_h.weight',
    'self_attention.dense.weight',
]

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[str]:
  UpperCamelCase_: 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
  UpperCamelCase_: Tuple   				=    int(re.match(R'.*layer_(\d*).*'     ,   UpperCAmelCase__  )[1]  )
  layer_number -= 3
  return F'''h.{layer_number}.''' + key

def   snake_case (UpperCAmelCase__  )	->    List[str]:
  if dtype == torch.bool:
    return 1 / 8
  UpperCamelCase_: Optional[Any]   				=    re.search(R'[^\d](\d+)$'     ,   str(UpperCAmelCase__  )  )
  if bit_search is None:
    raise ValueError(F'''`dtype` is not a valid dtype: {dtype}.'''  )
  UpperCamelCase_: List[Any]   				=    int(bit_search.groups()[0]  )
  return bit_size // 8

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Any:
  # Construct model
  if bloom_config_file == "":
    UpperCamelCase_: List[str]   				=    BloomConfig()
  else:
    UpperCamelCase_: List[str]   				=    BloomConfig.from_json_file(UpperCAmelCase__  )

  if shard_model:
    UpperCamelCase_: str   				=    os.listdir(UpperCAmelCase__  )
    UpperCamelCase_: List[str]   				=    sorted(filter(lambda UpperCAmelCase__  : s.startswith('layer'  ) and "model_00" in s     ,   UpperCAmelCase__  )  )

    UpperCamelCase_: Optional[int]   				=    {'weight_map': {}, 'metadata': {}}
    UpperCamelCase_: str   				=    0

    UpperCamelCase_: Optional[Any]   				=    None

    UpperCamelCase_: int   				=    BloomConfig()

    for j, file in enumerate(UpperCAmelCase__  ):
      print('Processing file: {}'.format(UpperCAmelCase__  )  )
      UpperCamelCase_: Tuple   				=    None

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

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

        if tensors is None:
          UpperCamelCase_: Dict   				=    temp
        else:
          for key in tensors.keys():
            if any(key.endswith(UpperCAmelCase__  ) 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_: List[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_: Dict   				=    torch.cat([tensors[key], temp[key]]     ,   dim=UpperCAmelCase__  )

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

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

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

    UpperCamelCase_: Tuple   				=    os.listdir(UpperCAmelCase__  )
    UpperCamelCase_: Tuple   				=    sorted(filter(lambda UpperCAmelCase__  : s.startswith('layer'  ) and "model_00" in s     ,   UpperCAmelCase__  )  )

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

        # Rename keys in the transformers names
        UpperCamelCase_: Dict   				=    list(temp.keys()  )
        for key in keys:
          UpperCamelCase_: Any   				=    temp.pop(UpperCAmelCase__  )

        if tensors is None:
          UpperCamelCase_: Any   				=    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(UpperCAmelCase__  ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH  ):
              tensors[key] += temp[key]
            else:
              # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
              UpperCamelCase_: 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=UpperCAmelCase__  )

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

      UpperCamelCase_: Any   				=    model.load_state_dict(UpperCAmelCase__     ,   strict=UpperCAmelCase__  )
      assert not other_keys.unexpected_keys, F'''The keys {other_keys.unexpected_keys} are unexpected'''
      if missing_keys is None:
        UpperCamelCase_: Any   				=    set(other_keys.missing_keys  )
      else:
        UpperCamelCase_: int   				=    missing_keys.intersection(set(other_keys.missing_keys  )  )

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

    # Save pytorch-model
    os.makedirs(UpperCAmelCase__     ,   exist_ok=UpperCAmelCase__  )
    UpperCamelCase_: Optional[Any]   				=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
    UpperCamelCase_: 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_: Tuple   				=    model.to(config.torch_dtype  )
    torch.save(model.state_dict()     ,   UpperCAmelCase__  )
    print(F'''Save configuration file to {pytorch_config_dump_path}'''  )
    with open(UpperCAmelCase__     ,   'w'     ,   encoding='utf-8'  ) as f:
      f.write(config.to_json_string()  )


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