


"""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()

lowerCamelCase_   :   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_   :   int									=  [
    """mlp.dense_4h_to_h.weight""",
    """self_attention.dense.weight""",
]
def _A						(						lowercase       ,    lowercase		):







    """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*).*'''       ,    lowercase		)[1]		)
    layer_number -= 3
    return f'''h.{layer_number}.''' + key
def _A						(						lowercase		):







    """simple docstring"""
    if dtype == torch.bool:
        return 1 / 8
    a 		=re.search(R'''[^\d](\d+)$'''       ,    str(lowercase		)		)
    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						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    # Construct model
    if bloom_config_file == "":
        a 		=BloomConfig()
    else:
        a 		=BloomConfig.from_json_file(lowercase		)

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

        a 		={'''weight_map''': {}, '''metadata''': {}}
        a 		=0

        a 		=None

        a 		=BloomConfig()

        for j, file in enumerate(lowercase		):
            print('''Processing file: {}'''.format(lowercase		)		)
            a 		=None

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

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

                if tensors is None:
                    a 		=temp
                else:
                    for key in tensors.keys():
                        if any(key.endswith(lowercase		) 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=lowercase		)

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

        a 		=BloomConfig()
        a 		=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
        a 		=total_size
        with open(lowercase       ,    '''w'''       ,    encoding='''utf-8'''		) as f:
            f.write(config.to_json_string()		)
        with open(os.path.join(lowercase       ,    WEIGHTS_NAME + '''.index.json'''		)       ,    '''w'''       ,    encoding='''utf-8'''		) as f:
            a 		=json.dumps(lowercase       ,    indent=2       ,    sort_keys=lowercase		) + '''\n'''
            f.write(lowercase		)
    else:
        a 		=BloomModel(lowercase		)

        a 		=os.listdir(lowercase		)
        a 		=sorted(filter(lambda lowercase		: s.startswith('''layer'''		) and "model_00" in s       ,    lowercase		)		)

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

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

                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(lowercase		) 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=lowercase		)

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

            a 		=model.load_state_dict(lowercase       ,    strict=lowercase		)
            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(lowercase       ,    exist_ok=lowercase		)
        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()       ,    lowercase		)
        print(f'''Save configuration file to {pytorch_config_dump_path}'''		)
        with open(lowercase       ,    '''w'''       ,    encoding='''utf-8'''		) as f:
            f.write(config.to_json_string()		)


if __name__ == "__main__":
       lowerCamelCase_   :   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_   :   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,
       )






"""simple docstring"""





from __future__ import annotations


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return [ord(SCREAMING_SNAKE_CASE			) - 96 for elem in plain]


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						list[int]			):







      '''simple docstring'''




      return "".join(chr(elem + 96			) for elem in encoded			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       encode(input("""-> """			).strip().lower()			)
      print("""Encoded: """							,						SCREAMING_SNAKE_CASE			)
      print("""Decoded:"""							,						decode(SCREAMING_SNAKE_CASE			)			)


if __name__ == "__main__":
       main()






