





"""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"""






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


A 			=  logging.get_logger(__name__)
logging.set_verbosity_info()

def     __A      (			a_						:str   ,       a_						:str)		->		List[Any]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								__a :						List[str]			       =  XLMProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						int			       =  XLMProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)
				else:
								__a :						Optional[int]			       =  ProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						Optional[Any]			       =  ProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)

				__a :						Tuple			       =  ['''key_proj''', '''value_proj''', '''query_proj''']

				__a :						List[str]			       =  {
				    '''self_attn''': '''ngram_self_attn''',
				    '''cross_attn''': '''encoder_attn''',
				    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
				    '''feed_forward_layer_norm''': '''final_layer_norm''',
				    '''feed_forward''': '''''',
				    '''intermediate''': '''fc1''',
				    '''output''': '''fc2''',
				    '''key_proj''': '''k_proj''',
				    '''query_proj''': '''q_proj''',
				    '''value_proj''': '''v_proj''',
				    '''word_embeddings''': '''embed_tokens''',
				    '''embeddings_layer_norm''': '''emb_layer_norm''',
				    '''relative_pos_embeddings''': '''relative_linear''',
				    '''ngram_embeddings''': '''ngram_input_embed''',
				    '''position_embeddings''': '''embed_positions''',
				}

				for key in loading_info["missing_keys"]:
								__a :						List[str]			       =  key.split('''.''')

								if attributes[0] == "lm_head":
												__a :						Dict			       =  prophet
												__a :						Optional[Any]			       =  prophet_old
								else:
												__a :						Union[str, Any]			       =  prophet.prophetnet
												__a :						int			       =  prophet_old.model

								__a :						Optional[int]			       =  False
								for attribute in attributes:
												if attribute in mapping:
																__a :						str			       =  mapping[attribute]
																if not hasattr(a_   ,       a_) and len(a_) > 0:
																				__a :						List[str]			       =  attribute
												elif hasattr(a_   ,       a_):
																__a :						int			       =  attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																__a :						Optional[int]			       =  old_model.weight
																logger.info(F"""{attribute} is initialized.""")
																__a :						List[str]			       =  True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																__a :						Dict			       =  old_model.bias
																logger.info(F"""{attribute} is initialized""")
																__a :						Tuple			       =  True
																break
												elif attribute in special_keys and hasattr(a_   ,       '''in_proj_weight'''):
																__a :						List[Any]			       =  old_model.in_proj_weight.shape[0] // 3
																__a :						Tuple			       =  getattr(a_   ,       a_)
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				__a :						Any			       =  nn.Parameter(old_model.in_proj_weight[:embed_dim, :])
																				__a :						List[Any]			       =  nn.Parameter(old_model.in_proj_bias[:embed_dim])

																elif attribute == "key_proj":
																				__a :						Union[str, Any]			       =  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
																				__a :						int			       =  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
																elif attribute == "value_proj":
																				__a :						Dict			       =  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
																				__a :						List[Any]			       =  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
																__a :						int			       =  True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
																__a :						Optional[Any]			       =  nn.Parameter(old_model.embed_positions.weight[:5_12, :])
																__a :						List[Any]			       =  True
																break

												if attribute.isdigit():
																__a :						Optional[Any]			       =  model[int(a_)]
																__a :						str			       =  old_model[int(a_)]
												else:
																__a :						Optional[int]			       =  getattr(a_   ,       a_)

																if old_attribute == "":
																				__a :						str			       =  old_model
																else:
																				if not hasattr(a_   ,       a_):
																								raise ValueError(F"""{old_model} does not have {old_attribute}""")
																				__a :						Union[str, Any]			       =  getattr(a_   ,       a_)

								if not is_key_init:
												raise ValueError(F"""{key} was not correctly initialized!""")

				print(F"""Saving model to {pytorch_dump_folder_path}""")
				prophet.save_pretrained(a_)


if __name__ == "__main__":
		A 			=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		A 			=  parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)