
import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME


def 						UpperCAmelCase			(	a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= [
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "decoder.output_projection.weight",
				    "_float_tensor",
				    "encoder.embed_positions._float_tensor",
				    "decoder.embed_positions._float_tensor",
				]
				for k in ignore_keys:
								state_dict.pop(a_		,   a_						)


def 						UpperCAmelCase			(	a_						)		->	Any:







				"""simple docstring"""



				__A			,							__A   			= emb.weight.shape
				__A   			= nn.Linear(a_		,   a_		,   bias=a_						)
				__A   			= emb.weight.data
				return lin_layer


def 						UpperCAmelCase			(	a_		,   a_=None						)		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= {}
				for old_key in state_dict.keys():
								__A   			= old_key
								if "moe_layer.experts." in key:
												if expert_idx is not None:
																__A   			= key.replace("moe_layer.experts.0"		,   F'''ffn.experts.expert_{expert_idx}'''						)
												else:
																__A   			= key.replace("moe_layer.experts."		,   "ffn.experts.expert_"						)
								if "gate" in key:
												__A   			= key.replace(".moe_layer.gate.wg"		,   ".ffn.router.classifier"						)
								if "fc2" and "experts" not in key:
												__A   			= key.replace(".fc2."		,   ".ffn.fc2."						)
								if "fc1" and "experts" not in key:
												__A   			= key.replace(".fc1."		,   ".ffn.fc1."						)
								if ".encoder_attn." in key:
												__A   			= key.replace(".encoder_attn."		,   ".cross_attention."						)
								if "encoder_attn_layer_norm" in key:
												__A   			= key.replace("encoder_attn_layer_norm"		,   "cross_attention_layer_norm"						)
								if "final_layer_norm" in key:
												__A   			= key.replace("final_layer_norm"		,   "ff_layer_norm"						)
								__A   			= state_dict[old_key]
				return new_dict


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = WEIGHTS_NAME						)		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= []
				__A   			= 0
				os.makedirs(a_		,   exist_ok=a_						)

				for expert in range(a_						):
								__A   			= switch_checkpoint_path + F'''-rank-{expert}.pt'''
								if os.path.isfile(a_						):
												__A   			= torch.load(a_						)["model"]
												remove_ignore_keys_(a_						)
												__A   			= rename_fairseq_keys(a_		,   a_						)
												__A   			= os.path.join(
												    a_		,   weights_name.replace(".bin"		,   F'''-{len(a_						)+1:05d}-of-???.bin'''						)						)
												torch.save(a_		,   a_						)
												sharded_state_dicts.append(expert_state.keys()						)
												total_size += sum([value.numel() for key, value in expert_state.items()]						) * dtype_byte_size(
												    expert_state[list(a_						)[0]].dtype						)

    # Add the last block
				__A   			= os.path.join(a_		,   weights_name.replace(".bin"		,   F'''-{len(a_						)+1:05d}-of-???.bin'''						)						)
				__A   			= torch.load(switch_checkpoint_path + "-shared.pt"						)["model"]
				remove_ignore_keys_(a_						)
				__A   			= rename_fairseq_keys(a_		,   a_						)
				__A   			= shared_weights["decoder.embed_tokens.weight"]
				sharded_state_dicts.append(shared_weights.keys()						)

				# If we only have the shared weights (dummy model/experts saved on the same file)
				if len(a_						) == 1:
								__A   			= os.path.join(a_		,   a_						)
								torch.save(a_		,   a_						)
								return {weights_name: sharded_state_dicts[0]}, None
				else:
								torch.save(a_		,   a_						)
				# Otherwise, let's build the index
				__A   			= {}
				for idx, shard in enumerate(a_						):
								__A   			= weights_name.replace(".bin"		,   F'''-{idx+1:05d}-of-{len(a_						):05d}.bin'''						)
								__A   			= os.path.join(a_		,   weights_name.replace(".bin"		,   F'''-{idx+1:05d}-of-???.bin'''						)						)
								os.rename(a_		,   os.path.join(a_		,   a_						)						)
								for key in shard:
												__A   			= shard_file

    # Add the metadata
				__A   			= {"total_size": total_size}
				__A   			= {"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(a_		,   a_						)		,   "w"		,   encoding="utf-8"						) as f:
								__A   			= json.dumps(a_		,   indent=2		,   sort_keys=a_						) + "\n"
								f.write(a_						)

				return metadata, index


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Dict							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--nllb_moe_checkpoint_path',
					    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
					    type=str,
					    required=False,
					    help='Path to a directory containing a folder per layer. Follows the original Google format.',
					)
					parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
					    type=str,
					    required=False,
					    help='Path to the output pytorch model.',
					)
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				parser.parse_args()
					SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE							:Dict							 =				shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    128,
					    args.dtype,
					)

					SCREAMING_SNAKE_CASE							:Dict							 =				NllbMoeConfig.from_pretrained(
					    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
					)
					config.save_pretrained(args.pytorch_dump_folder_path)
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print('Done')
					model.save_pretrained(args.pytorch_dump_folder_path)


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 						UpperCAmelCase			(	a_						)		->	str:







				"""simple docstring"""



				__A   			= {}

				__A   			= job["started_at"]
				__A   			= job["completed_at"]

				__A   			= date_parser.parse(a_						)
				__A   			= date_parser.parse(a_						)

				__A   			= round((end_datetime - start_datetime).total_seconds() / 60.0						)

				__A   			= start
				__A   			= end
				__A   			= duration_in_min

				return job_info


def 						UpperCAmelCase			(	a_		,   a_=None						)		->	str:







				"""simple docstring"""



				__A   			= None
				if token is not None:
								__A   			= {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

				__A   			= F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
				__A   			= requests.get(a_		,   headers=a_						).json()
				__A   			= {}

				try:
								job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)
								__A   			= math.ceil((result["total_count"] - 1_0_0) / 1_0_0						)

								for i in range(a_						):
												__A   			= requests.get(url + F'''&page={i + 2}'''		,   headers=a_						).json()
												job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)

								return job_time
				except Exception:
								print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''						)

				return {}


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

					for k, v in job_time.items():
										print(f'''{k}: {v["duration"]}''')
