


'''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      :      Tuple            =					[
    """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      :      Optional[int]            =					[
    """mlp.dense_4h_to_h.weight""",
    """self_attention.dense.weight""",
]


def 				lowerCamelCase__				(	A       :      Dict				,       A       :      str					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      str					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      Any				,       A       :      Tuple				,       A       :      Dict				,       A       :      Optional[Any]				,       A       :      Union[str, Any]					):




						'''simple docstring'''






						if bloom_config_file == "":
												UpperCAmelCase					    =     BloomConfig()
						else:
												UpperCAmelCase					    =     BloomConfig.from_json_file(A					)

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

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

												UpperCAmelCase					    =     None

												UpperCAmelCase					    =     BloomConfig()

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

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

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

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

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

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

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

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

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

																		UpperCAmelCase					    =     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:
																								UpperCAmelCase					    =     set(other_keys.missing_keys					)
																		else:
																								UpperCAmelCase					    =     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					)
												UpperCAmelCase					    =     pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
												UpperCAmelCase					    =     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:
																		UpperCAmelCase					    =     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__":
	_lowercase      :      Optional[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""",
	)
	_lowercase      :      List[str]            =					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 .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)





