
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


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

__A				=[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								lowerCamelCase_						=			k.replace(lowerCamelCase__	, lowerCamelCase__    )

				if k.startswith("encoder"    ):
								lowerCamelCase_						=			k.replace(".attn"	, ".self_attn"    )
								lowerCamelCase_						=			k.replace("norm1"	, "self_attn_layer_norm"    )
								lowerCamelCase_						=			k.replace("norm2"	, "final_layer_norm"    )
				elif k.startswith("decoder"    ):
								lowerCamelCase_						=			k.replace("norm1"	, "self_attn_layer_norm"    )
								lowerCamelCase_						=			k.replace("norm2"	, "encoder_attn_layer_norm"    )
								lowerCamelCase_						=			k.replace("norm3"	, "final_layer_norm"    )
				return k





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			[
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								lowerCamelCase_						=			sd.pop(lowerCamelCase__    )
								lowerCamelCase_						=			k.replace("layernorm_embedding"	, "layer_norm"    )
								assert new_k not in sd
								lowerCamelCase_						=			v


__A				=['''START''']





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			torch.load(lowerCamelCase__	, map_location="cpu"    )
				lowerCamelCase_						=			model["model"]
				lowerCamelCase_						=			BlenderbotConfig.from_json_file(lowerCamelCase__    )
				lowerCamelCase_						=			BlenderbotForConditionalGeneration(lowerCamelCase__    )
				lowerCamelCase_						=			m.model.state_dict().keys()
				lowerCamelCase_						=			[]
				lowerCamelCase_						=			{}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								lowerCamelCase_						=			rename_state_dict_key(lowerCamelCase__    )
								if new_k not in valid_keys:
												failures.append([k, new_k]    )
								else:
												lowerCamelCase_						=			v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCamelCase__    )
				m.model.load_state_dict(lowerCamelCase__	, strict=lowerCamelCase__    )
				m.half()
				m.save_pretrained(lowerCamelCase__    )


if __name__ == "__main__":
			__A				=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
			parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
			parser.add_argument(
			    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
			)
			__A				=parser.parse_args()
			convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




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"]}''')
