






"""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()

lowercase    			=       [
    '''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''',
]

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






def _lowerCAmelCase	(  __lowerCamelCase:Any ,						__lowerCamelCase:List[str]   ):

						'''simple docstring'''






						__magic_name__            =     {
						    "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
						__magic_name__            =     int(re.match(r".*layer_(\d*).*" ,						__lowerCamelCase   )[1]   )
						layer_number -= 3
						return f'''h.{layer_number}.''' + key






def _lowerCAmelCase	(  __lowerCamelCase:List[str]   ):

						'''simple docstring'''






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






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any] ,						__lowerCamelCase:Optional[int] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						if bloom_config_file == "":
												__magic_name__            =     BloomConfig()
						else:
												__magic_name__            =     BloomConfig.from_json_file(__lowerCamelCase   )

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

												__magic_name__            =     {"weight_map": {}, "metadata": {}}
												__magic_name__            =     0

												__magic_name__            =     None

												__magic_name__            =     BloomConfig()

												for j, file in enumerate(__lowerCamelCase   ):
																		print("Processing file: {}".format(__lowerCamelCase   )   )
																		__magic_name__            =     None

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

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

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

												__magic_name__            =     BloomConfig()
												__magic_name__            =     pytorch_dump_folder_path + "/" + CONFIG_NAME
												__magic_name__            =     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:
																		__magic_name__            =     json.dumps(__lowerCamelCase ,						indent=2 ,						sort_keys=__lowerCamelCase   ) + "\n"
																		f.write(__lowerCamelCase   )
						else:
												__magic_name__            =     BloomModel(__lowerCamelCase   )

												__magic_name__            =     os.listdir(__lowerCamelCase   )
												__magic_name__            =     sorted(filter(lambda __lowerCamelCase   : s.startswith("layer"   ) and "model_00" in s ,						__lowerCamelCase   )   )

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

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

																								if tensors is None:
																														__magic_name__            =     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
																																										__magic_name__            =     1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN   ) else 0
																																										# We concatenate these weights accross TP ranks
																																										__magic_name__            =     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   ):
																														__magic_name__            =     tensors[key] / pretraining_tp

																		__magic_name__            =     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:
																								__magic_name__            =     set(other_keys.missing_keys   )
																		else:
																								__magic_name__            =     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   )
												__magic_name__            =     pytorch_dump_folder_path + "/" + WEIGHTS_NAME
												__magic_name__            =     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:
																		__magic_name__            =     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__":
			lowercase    			=       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''',
			)
			lowercase    			=       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 _lowerCAmelCase	(  __lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     n ** (1 / 3)
						return (val * val * val) == n


if __name__ == "__main__":
			print(perfect_cube(27))
			print(perfect_cube(4))



