
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						(							snake_case__:					List[Any]						,   snake_case__:					Tuple					):


				'''simple docstring'''
				lowercase_       =					{
				    '''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
				lowercase_       =					int(re.match(r'''.*layer_(\d*).*'''						,   UpperCAmelCase_					)[1]					)
				layer_number -= 3
				return F'''h.{layer_number}.''' + key




def 				a						(							snake_case__:					List[str]					):


				'''simple docstring'''





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




def 				a						(							snake_case__:					List[Any]						,   snake_case__:					Any						,   snake_case__:					Any						,   snake_case__:					Any						,   snake_case__:					Any					):


				'''simple docstring'''





				if bloom_config_file == "":
								lowercase_       =					BloomConfig()
				else:
								lowercase_       =					BloomConfig.from_json_file(UpperCAmelCase_					)

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

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

								lowercase_       =					None

								lowercase_       =					BloomConfig()

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

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

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

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

								lowercase_       =					BloomConfig()
								lowercase_       =					pytorch_dump_folder_path + '''/''' + CONFIG_NAME
								lowercase_       =					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:
												lowercase_       =					json.dumps(UpperCAmelCase_						,   indent=2						,   sort_keys=UpperCAmelCase_					) + '''\n'''
												f.write(UpperCAmelCase_					)
				else:
								lowercase_       =					BloomModel(UpperCAmelCase_					)

								lowercase_       =					os.listdir(UpperCAmelCase_					)
								lowercase_       =					sorted(filter(lambda snake_case__					: s.startswith('''layer'''					) and "model_00" in s						,   UpperCAmelCase_					)					)

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

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

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

												lowercase_       =					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:
																lowercase_       =					set(other_keys.missing_keys					)
												else:
																lowercase_       =					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_					)
								lowercase_       =					pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
								lowercase_       =					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:
												lowercase_       =					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  			=					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,
						)








from maths.prime_factors import prime_factors
def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            a						:Dict 							=       F'''Input value of [number={number}] must be an integer'''
            raise TypeError(UpperCAmelCase_						)
      if number < 1:
            raise ValueError('''Input must be a positive integer'''						)
      return -1 if len(prime_factors(UpperCAmelCase_						)						) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





