'''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		:				str	       =		[
    "mlp.dense_4h_to_h.weight",
    "self_attention.dense.weight",
]




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[int]   ,   UpperCAmelCase__ :      Optional[int]				)    ->   str:
							lowercase_      :    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
							lowercase_      :    Dict       		= int(re.match(R""".*layer_(\d*).*"""   ,   UpperCAmelCase__				)[1]				)
							layer_number -= 3
							return F'''h.{layer_number}.''' + key




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any				)    ->   Union[str, Any]:
							if dtype == torch.bool:
														return 1 / 8
							lowercase_      :    Tuple       		= re.search(R"""[^\d](\d+)$"""   ,   str(UpperCAmelCase__				)				)
							if bit_search is None:
														raise ValueError(F'''`dtype` is not a valid dtype: {dtype}.'''				)
							lowercase_      :    Optional[int]       		= int(bit_search.groups()[0]				)
							return bit_size // 8




def 							lowerCamelCase			(   UpperCAmelCase__ :      Dict   ,   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      Optional[int]   ,   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      Optional[int]				)    ->   Optional[Any]:
							# Construct model
							if bloom_config_file == "":
														lowercase_      :    Union[str, Any]       		= BloomConfig()
							else:
														lowercase_      :    int       		= BloomConfig.from_json_file(UpperCAmelCase__				)

							if shard_model:
														lowercase_      :    Optional[Any]       		= os.listdir(UpperCAmelCase__				)
														lowercase_      :    Union[str, Any]       		= sorted(filter(lambda UpperCAmelCase__				: s.startswith("""layer"""				) and "model_00" in s   ,   UpperCAmelCase__				)				)

														lowercase_      :    Union[str, Any]       		= {"""weight_map""": {}, """metadata""": {}}
														lowercase_      :    Optional[int]       		= 0

														lowercase_      :    int       		= None

														lowercase_      :    str       		= BloomConfig()

														for j, file in enumerate(UpperCAmelCase__				):
																					print("""Processing file: {}""".format(UpperCAmelCase__				)				)
																					lowercase_      :    Any       		= None

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

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

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

														lowercase_      :    Dict       		= BloomConfig()
														lowercase_      :    int       		= pytorch_dump_folder_path + """/""" + CONFIG_NAME
														lowercase_      :    List[Any]       		= 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_      :    List[Any]       		= json.dumps(UpperCAmelCase__   ,   indent=2   ,   sort_keys=UpperCAmelCase__				) + """\n"""
																					f.write(UpperCAmelCase__				)
							else:
														lowercase_      :    Optional[int]       		= BloomModel(UpperCAmelCase__				)

														lowercase_      :    int       		= os.listdir(UpperCAmelCase__				)
														lowercase_      :    Optional[int]       		= sorted(filter(lambda UpperCAmelCase__				: s.startswith("""layer"""				) and "model_00" in s   ,   UpperCAmelCase__				)				)

														lowercase_      :    Union[str, Any]       		= None
														for i, file in enumerate(UpperCAmelCase__				):
																					lowercase_      :    Tuple       		= None
																					for i in range(UpperCAmelCase__				):
																												# load all TP files
																												lowercase_      :    List[str]       		= file.replace("""model_00"""   ,   F'''model_0{i}'''				)
																												lowercase_      :    List[Any]       		= torch.load(os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__				)   ,   map_location="""cpu"""				)

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

																												if tensors is None:
																																			lowercase_      :    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(UpperCAmelCase__				) for end in WEIGHTS_TO_AVERAGE_ENDSWITH				):
																																																	tensors[key] += temp[key]
																																										else:
																																																	# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																																	lowercase_      :    Dict       		= 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN				) else 0
																																																	# We concatenate these weights accross TP ranks
																																																	lowercase_      :    Optional[Any]       		= 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_      :    int       		= tensors[key] / pretraining_tp

																					lowercase_      :    Optional[int]       		= 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_      :    Union[str, Any]       		= set(other_keys.missing_keys				)
																					else:
																												lowercase_      :    str       		= 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_      :    Union[str, Any]       		= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
														lowercase_      :    Any       		= 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_      :    Union[str, Any]       		= 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__":
						_lowercase		:				Union[str, 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 collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


_lowercase		:				Optional[Any]	       =		[
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

_lowercase		:				List[Any]	       =		[
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]




def 							lowerCamelCase			(   )    ->   List[str]:
							lowercase_      :    str       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   bootstrap_aggregation=UpperCAmelCase__   ,   rouge_keys=["""rouge2""", """rougeL"""]				)
							assert isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				)
							lowercase_      :    int       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   bootstrap_aggregation=UpperCAmelCase__   ,   rouge_keys=["""rouge2"""]				)
							assert (
							    pd.DataFrame(no_aggregation["""rouge2"""]				).fmeasure.mean()
							    == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]				).fmeasure.mean()
							)




def 							lowerCamelCase			(   )    ->   Optional[Any]:
							lowercase_      :    Tuple       		= """rougeLsum"""
							lowercase_      :    Optional[Any]       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__   ,   rouge_keys=[k]				)[k]
							lowercase_      :    Optional[Any]       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__   ,   rouge_keys=[k]				)[k]
							assert score > score_no_sep




def 							lowerCamelCase			(   )    ->   List[Any]:
							lowercase_      :    Optional[int]       		= ["""rouge1""", """rouge2""", """rougeL"""]
							lowercase_      :    Tuple       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__   ,   rouge_keys=UpperCAmelCase__				)
							lowercase_      :    Tuple       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__   ,   rouge_keys=UpperCAmelCase__				)
							assert score_sep == score_no_sep




def 							lowerCamelCase			(   )    ->   Optional[Any]:
							lowercase_      :    Union[str, Any]       		= [
							    """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
							    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
							]
							lowercase_      :    List[str]       		= [
							    """Margot Frank, died in 1945, a month earlier than previously thought.""",
							    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
							    """ the final seconds on board Flight 9525.""",
							]
							assert calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__				) == calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   newline_sep=UpperCAmelCase__				)




def 							lowerCamelCase			(   )    ->   Union[str, Any]:
							lowercase_      :    Optional[Any]       		= [
							    """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
							]
							lowercase_      :    List[Any]       		= [
							    """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
							]

							lowercase_      :    Optional[int]       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   rouge_keys=["""rougeLsum"""]   ,   newline_sep=UpperCAmelCase__				)["""rougeLsum"""]
							lowercase_      :    List[str]       		= calculate_rouge(UpperCAmelCase__   ,   UpperCAmelCase__   ,   rouge_keys=["""rougeLsum"""]				)["""rougeLsum"""]
							assert new_score > prev_score




def 							lowerCamelCase			(   )    ->   Tuple:
							lowercase_      :    Optional[int]       		= Path("""examples/seq2seq/test_data/wmt_en_ro"""				)
							lowercase_      :    List[Any]       		= calculate_rouge_path(data_dir.joinpath("""test.source"""				)   ,   data_dir.joinpath("""test.target"""				)				)
							assert isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= calculate_rouge_path(
							    data_dir.joinpath("""test.source"""				)   ,   data_dir.joinpath("""test.target"""				)   ,   bootstrap_aggregation=UpperCAmelCase__				)
							assert isinstance(UpperCAmelCase__   ,   UpperCAmelCase__				)





