





"""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()

A 			=  [
    '''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 			=  [
    '''mlp.dense_4h_to_h.weight''',
    '''self_attention.dense.weight''',
]

def     __A      (			a_						:str   ,       a_						:Optional[int])		->		List[str]:
				__a :						List[str]			       =  {
				    '''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 :						Any			       =  int(re.match(R'''.*layer_(\d*).*'''   ,       a_)[1])
				layer_number -= 3
				return F"""h.{layer_number}.""" + key

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

def     __A      (			a_						:str   ,       a_						:str   ,       a_						:Any   ,       a_						:Optional[Any]   ,       a_						:int)		->		int:
				# Construct model
				if bloom_config_file == "":
								__a :						int			       =  BloomConfig()
				else:
								__a :						Optional[int]			       =  BloomConfig.from_json_file(a_)

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

								__a :						List[Any]			       =  {'''weight_map''': {}, '''metadata''': {}}
								__a :						Optional[Any]			       =  0

								__a :						Union[str, Any]			       =  None

								__a :						Dict			       =  BloomConfig()

								for j, file in enumerate(a_):
												print('''Processing file: {}'''.format(a_))
												__a :						int			       =  None

												for i in range(a_):
																# load all TP files
																__a :						Tuple			       =  file.replace('''model_00'''   ,       F"""model_0{i}""")
																__a :						Optional[Any]			       =  torch.load(os.path.join(a_   ,       a_)   ,       map_location='''cpu''')

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

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

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

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

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

																# Rename keys in the transformers names
																__a :						str			       =  list(temp.keys())
																for key in keys:
																				__a :						Any			       =  temp.pop(a_)

																if tensors is None:
																				__a :						Dict			       =  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
																												__a :						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
																												__a :						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):
																				__a :						Tuple			       =  tensors[key] / pretraining_tp

												__a :						List[Any]			       =  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:
																__a :						Dict			       =  set(other_keys.missing_keys)
												else:
																__a :						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_)
								__a :						List[str]			       =  pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
								__a :						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:
												__a :						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__":
		A 			=  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 			=  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"""






def     __A      (			a_						:int = 2_00)		->		int:
				__a :						int			       =  [1, 2, 5, 10, 20, 50, 1_00, 2_00]
				__a :						List[Any]			       =  [0] * (pence + 1)
				__a :						Tuple			       =  1  # base case: 1 way to make 0 pence

				for coin in coins:
								for i in range(a_   ,       pence + 1   ,       1):
												number_of_ways[i] += number_of_ways[i - coin]
				return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73_682