
"""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()

_snake_case          =						[
    "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",
]

_snake_case          =						[
    "mlp.dense_4h_to_h.weight",
    "self_attention.dense.weight",
]

def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



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

def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]						):






							"""simple docstring"""



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

def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							if bloom_config_file == "":
														_lowerCAmelCase  	=			BloomConfig()
							else:
														_lowerCAmelCase  	=			BloomConfig.from_json_file(a_						)

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

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

														_lowerCAmelCase  	=			None

														_lowerCAmelCase  	=			BloomConfig()

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

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

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

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

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

														_lowerCAmelCase  	=			os.listdir(a_						)
														_lowerCAmelCase  	=			sorted(filter(lambda SCREAMING_SNAKE_CASE						: s.startswith('layer'						) and "model_00" in s     ,  a_						)						)

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

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

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

																					_lowerCAmelCase  	=			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:
																												_lowerCAmelCase  	=			set(other_keys.missing_keys						)
																					else:
																												_lowerCAmelCase  	=			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_						)
														_lowerCAmelCase  	=			pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
														_lowerCAmelCase  	=			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:
																					_lowerCAmelCase  	=			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__":
	_snake_case          =						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''',
	)
	_snake_case          =						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
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_snake_case          =						getLogger(__name__)


_snake_case          =						'''cuda''' if torch.cuda.is_available() else '''cpu'''






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					int = 8     ,  SCREAMING_SNAKE_CASE:					str = DEFAULT_DEVICE     ,  SCREAMING_SNAKE_CASE:					Optional[int]=False     ,  SCREAMING_SNAKE_CASE:					int="summarization"     ,  SCREAMING_SNAKE_CASE:					List[Any]=None     ,  **SCREAMING_SNAKE_CASE:					Tuple     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			Path(SCREAMING_SNAKE_CASE						).open('w'     ,  encoding='utf-8'						)
							_lowerCAmelCase  	=			str(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
							if fpaa:
														_lowerCAmelCase  	=			model.half()

							_lowerCAmelCase  	=			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE						)
							logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

							_lowerCAmelCase  	=			time.time()
							# update config with task specific params
							use_task_specific_params(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							if prefix is None:
														_lowerCAmelCase  	=			prefix or getattr(model.config     ,  'prefix'     ,  ''						) or ''
							for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)						)						):
														_lowerCAmelCase  	=			[prefix + text for text in examples_chunk]
														_lowerCAmelCase  	=			tokenizer(SCREAMING_SNAKE_CASE     ,  return_tensors='pt'     ,  truncation=SCREAMING_SNAKE_CASE     ,  padding='longest'						).to(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			model.generate(
														    input_ids=batch.input_ids     ,  attention_mask=batch.attention_mask     ,  **SCREAMING_SNAKE_CASE     ,  )
														_lowerCAmelCase  	=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE     ,  skip_special_tokens=SCREAMING_SNAKE_CASE     ,  clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)
														for hypothesis in dec:
																					fout.write(hypothesis + '\n'						)
																					fout.flush()
							fout.close()
							_lowerCAmelCase  	=			int(time.time() - start_time						)  # seconds
							_lowerCAmelCase  	=			len(SCREAMING_SNAKE_CASE						)
							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs     ,  4						)}






def 	__snake_case  (    ):






							"""simple docstring"""



							return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any=True						):






							"""simple docstring"""



							_lowerCAmelCase  	=			argparse.ArgumentParser()
							parser.add_argument('model_name'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like facebook/bart-large-cnn,t5-base, etc.'						)
							parser.add_argument('input_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.source'						)
							parser.add_argument('save_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='where to save summaries'						)
							parser.add_argument('--reference_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.target'						)
							parser.add_argument('--score_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default='metrics.json'     ,  help='where to save metrics'						)
							parser.add_argument('--device'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='cuda, cuda:1, cpu etc.'						)
							parser.add_argument(
							    '--prefix'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='will be added to the begininng of src examples'						)
							parser.add_argument('--task'     ,  type=SCREAMING_SNAKE_CASE     ,  default='summarization'     ,  help='used for task_specific_params + metrics'						)
							parser.add_argument('--bs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=8     ,  required=SCREAMING_SNAKE_CASE     ,  help='batch size'						)
							parser.add_argument(
							    '--n_obs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=-1     ,  required=SCREAMING_SNAKE_CASE     ,  help='How many observations. Defaults to all.'						)
							parser.add_argument('--fp16'     ,  action='store_true'						)
							parser.add_argument('--dump-args'     ,  action='store_true'     ,  help='print the custom hparams with the results'						)
							parser.add_argument(
							    '--info'     ,  nargs='?'     ,  type=SCREAMING_SNAKE_CASE     ,  const=datetime_now()     ,  help=(
							        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
							        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
							    )     ,  )
							# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
							_lowerCAmelCase	,		_lowerCAmelCase  	=			parser.parse_known_args()
							_lowerCAmelCase  	=			parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE						)
							if parsed_args and verbose:
														print(f"""parsed the following generate kwargs: {parsed_args}"""						)
							_lowerCAmelCase  	=			[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
							if args.n_obs > 0:
														_lowerCAmelCase  	=			examples[: args.n_obs]
							Path(args.save_path						).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE						)

							if args.reference_path is None and Path(args.score_path						).exists():
														warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""						)

							if args.device == "cpu" and args.fpaa:
														# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
														raise ValueError('Can\'t mix --fp16 and --device cpu'						)

							_lowerCAmelCase  	=			generate_summaries_or_translations(
							    SCREAMING_SNAKE_CASE     ,  args.save_path     ,  args.model_name     ,  batch_size=args.bs     ,  device=args.device     ,  fpaa=args.fpaa     ,  task=args.task     ,  prefix=args.prefix     ,  **SCREAMING_SNAKE_CASE     ,  )

							if args.reference_path is None:
														return {}

							# Compute scores
							_lowerCAmelCase  	=			calculate_bleu if 'translation' in args.task else calculate_rouge
							_lowerCAmelCase  	=			[x.rstrip() for x in open(args.save_path						).readlines()]
							_lowerCAmelCase  	=			[x.rstrip() for x in open(args.reference_path						).readlines()][: len(SCREAMING_SNAKE_CASE						)]
							_lowerCAmelCase  	=			score_fn(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							scores.update(SCREAMING_SNAKE_CASE						)

							if args.dump_args:
														scores.update(SCREAMING_SNAKE_CASE						)
							if args.info:
														_lowerCAmelCase  	=			args.info

							if verbose:
														print(SCREAMING_SNAKE_CASE						)

							if args.score_path is not None:
														json.dump(SCREAMING_SNAKE_CASE     ,  open(args.score_path     ,  'w'						)						)

							return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)






