






"""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_   : 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""",
]

UpperCAmelCase_   : List[Any]   			= [
    """mlp.dense_4h_to_h.weight""",
    """self_attention.dense.weight""",
]



def 					_A			(__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{
        '''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
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		int(re.match(R'''.*layer_(\d*).*'''    ,				__a							)[1]							)
    layer_number -= 3
    return f'h.{layer_number}.' + key



def 					_A			(__a							) ->       int:
    """simple docstring"""






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



def 					_A			(__a    ,				__a    ,				__a    ,				__a    ,				__a							) ->       Optional[Any]:
    """simple docstring"""






    if bloom_config_file == "":
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		BloomConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  str    						=		BloomConfig.from_json_file(__a							)

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

        SCREAMING_SNAKE_CASE_      :  List[str]    						=		{'''weight_map''': {}, '''metadata''': {}}
        SCREAMING_SNAKE_CASE_      :  Any    						=		0

        SCREAMING_SNAKE_CASE_      :  Dict    						=		None

        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		BloomConfig()

        for j, file in enumerate(__a							):
            print('''Processing file: {}'''.format(__a							)							)
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		None

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

                # Rename keys in the transformers names
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		list(temp.keys()							)
                for key in keys:
                    SCREAMING_SNAKE_CASE_      :  Dict    						=		temp.pop(__a							)

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

        SCREAMING_SNAKE_CASE_      :  List[str]    						=		BloomConfig()
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		pytorch_dump_folder_path + '''/''' + CONFIG_NAME
        SCREAMING_SNAKE_CASE_      :  Dict    						=		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:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		json.dumps(__a    ,				indent=2    ,				sort_keys=__a							) + '''\n'''
            f.write(__a							)
    else:
        SCREAMING_SNAKE_CASE_      :  str    						=		BloomModel(__a							)

        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		os.listdir(__a							)
        SCREAMING_SNAKE_CASE_      :  int    						=		sorted(filter(lambda __a							: s.startswith('''layer'''							) and "model_00" in s    ,				__a							)							)

        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		None
        for i, file in enumerate(__a							):
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		None
            for i in range(__a							):
                # load all TP files
                SCREAMING_SNAKE_CASE_      :  List[Any]    						=		file.replace('''model_00'''    ,				f'model_0{i}'							)
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.load(os.path.join(__a    ,				__a							)    ,				map_location='''cpu'''							)

                # Rename keys in the transformers names
                SCREAMING_SNAKE_CASE_      :  str    						=		list(temp.keys()							)
                for key in keys:
                    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		temp.pop(__a							)

                if tensors is None:
                    SCREAMING_SNAKE_CASE_      :  List[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(__a							) for end in WEIGHTS_TO_AVERAGE_ENDSWITH							):
                            tensors[key] += temp[key]
                        else:
                            # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                            SCREAMING_SNAKE_CASE_      :  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
                            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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							):
                    SCREAMING_SNAKE_CASE_      :  str    						=		tensors[key] / pretraining_tp

            SCREAMING_SNAKE_CASE_      :  Tuple    						=		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:
                SCREAMING_SNAKE_CASE_      :  List[Any]    						=		set(other_keys.missing_keys							)
            else:
                SCREAMING_SNAKE_CASE_      :  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(__a    ,				exist_ok=__a							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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:
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		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_   : 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""",
  )
  UpperCAmelCase_   : List[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,
  )










"""simple docstring"""






from __future__ import annotations




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  int = 0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		key
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  int    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''encrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.encrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''decrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.decrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")


