



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowercase_						=       2
class 			_snake_case     :




				def __init__(  self   :  str,   *,  # begin keyword-only arguments
				    __lowercase   :  str="<s>",   __lowercase   :  Dict="<pad>",   __lowercase   :  Optional[Any]="</s>",   __lowercase   :  int="<unk>",   __lowercase   :  Tuple=None,   ):
							lowercase__					,    lowercase__					,    lowercase__					,    lowercase__   					=			bos, unk, pad, eos
							lowercase__   					=			[]
							lowercase__   					=			[]
							lowercase__   					=			{}
							lowercase__   					=			self.add_symbol(__lowercase  )
							lowercase__   					=			self.add_symbol(__lowercase  )
							lowercase__   					=			self.add_symbol(__lowercase  )
							lowercase__   					=			self.add_symbol(__lowercase  )
							if extra_special_symbols:
										for s in extra_special_symbols:
													self.add_symbol(__lowercase  )
							lowercase__   					=			len(self.symbols  )




				def __eq__(  self   :  List[Any],   __lowercase   :  Any  ):
							return self.indices == other.indices




				def __getitem__(  self   :  List[str],   __lowercase   :  Dict  ):
							if idx < len(self.symbols  ):
										return self.symbols[idx]
							return self.unk_word




				def __len__(  self   :  Optional[int]  ):
							return len(self.symbols  )




				def __contains__(  self   :  List[Any],   __lowercase   :  Dict  ):
							return sym in self.indices




				@classmethod
				def 							A__ (  cls   :  Optional[int],   __lowercase   :  Union[str, Any]  ):
							lowercase__   					=			cls()
							d.add_from_file(__lowercase  )
							return d




				def 							A__ (  self   :  List[Any],   __lowercase   :  Union[str, Any],   __lowercase   :  Any=1,   __lowercase   :  List[str]=False  ):
							if word in self.indices and not overwrite:
										lowercase__   					=			self.indices[word]
										lowercase__   					=			self.count[idx] + n
										return idx
							else:
										lowercase__   					=			len(self.symbols  )
										lowercase__   					=			idx
										self.symbols.append(__lowercase  )
										self.count.append(__lowercase  )
										return idx




				def 							A__ (  self   :  int,   __lowercase   :  Optional[Any]  ):
							return 0




				def 							A__ (  self   :  Optional[int],   __lowercase   :  Union[str, Any]  ):
							if isinstance(__lowercase,   __lowercase  ):
										try:
													with open(__lowercase,   "r",   encoding="utf-8"  ) as fd:
																self.add_from_file(__lowercase  )
										except FileNotFoundError as fnfe:
													raise fnfe
										except UnicodeError:
													raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(__lowercase  )  )
										return

							lowercase__   					=			f.readlines()
							lowercase__   					=			self._load_meta(__lowercase  )

							for line in lines[indices_start_line:]:
										try:
													lowercase__					,    lowercase__   					=			line.rstrip().rsplit(" ",   1  )
													if field == "#fairseq:overwrite":
																lowercase__   					=			True
																lowercase__					,    lowercase__   					=			line.rsplit(" ",   1  )
													else:
																lowercase__   					=			False
													lowercase__   					=			int(__lowercase  )
													lowercase__   					=			line
													if word in self and not overwrite:
																raise RuntimeError(
																    "Duplicate word found when loading Dictionary: '{}'. "
																    "Duplicate words can overwrite earlier ones by adding the "
																    "#fairseq:overwrite flag at the end of the corresponding row "
																    "in the dictionary file. If using the Camembert model, please "
																    "download an updated copy of the model file.".format(__lowercase  )  )
													self.add_symbol(__lowercase,   n=__lowercase,   overwrite=__lowercase  )
										except ValueError:
													raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"  )



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
			# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
			lowercase__   					=			dict((re.sub(r"@@$"  ,					""  ,					SCREAMING_SNAKE_CASE_					), v) if k.endswith("@@"					) else (re.sub(r"$"  ,					"</w>"  ,					SCREAMING_SNAKE_CASE_					), v) for k, v in d.items()					)
			lowercase__   					=			"<s> <pad> </s> <unk>".split()
			# restore the special tokens
			for k in keep_keys:
						del da[f'''{k}</w>''']
						lowercase__   					=			d[k]  # restore
			return da



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			# prep
			if not os.path.exists(SCREAMING_SNAKE_CASE_					):
						raise ValueError(f'''path {biogpt_checkpoint_path} does not exist!'''					)
			os.makedirs(SCREAMING_SNAKE_CASE_  ,					exist_ok=SCREAMING_SNAKE_CASE_					)
			print(f'''Writing results to {pytorch_dump_folder_path}'''					)

			# handle various types of models

			lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					"checkpoint.pt"					)
			if not os.path.isfile(SCREAMING_SNAKE_CASE_					):
						raise ValueError(f'''path to the file {checkpoint_file} does not exist!'''					)
			lowercase__   					=			torch.load(SCREAMING_SNAKE_CASE_  ,					map_location="cpu"					)

			lowercase__   					=			chkpt["cfg"]["model"]

			# dicts
			lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					"dict.txt"					)
			if not os.path.isfile(SCREAMING_SNAKE_CASE_					):
						raise ValueError(f'''path to the file {dict_file} does not exist!'''					)
			lowercase__   					=			Dictionary.load(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			rewrite_dict_keys(src_dict.indices					)
			lowercase__   					=			len(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					VOCAB_FILES_NAMES["vocab_file"]					)
			print(f'''Generating {src_vocab_file} of {src_vocab_size} records'''					)
			with open(SCREAMING_SNAKE_CASE_  ,					"w"  ,					encoding="utf-8"					) as f:
						f.write(json.dumps(SCREAMING_SNAKE_CASE_  ,					ensure_ascii=SCREAMING_SNAKE_CASE_  ,					indent=SCREAMING_SNAKE_CASE_					)					)

			# merges_file (bpecodes)
			lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					"bpecodes"					)
			if not os.path.isfile(SCREAMING_SNAKE_CASE_					):
						raise ValueError(f'''path to the file {bpecodes_file} does not exist!'''					)

			lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					VOCAB_FILES_NAMES["merges_file"]					)
			shutil.copyfile(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			# model config
			lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					"config.json"					)

			lowercase__   					=			{
			    "activation_dropout": args["activation_dropout"],
			    "architectures": ["BioGptForCausalLM"],
			    "attention_probs_dropout_prob": args["attention_dropout"],
			    "bos_token_id": 0,
			    "eos_token_id": 2,
			    "hidden_act": args["activation_fn"],
			    "hidden_dropout_prob": args["dropout"],
			    "hidden_size": args["decoder_embed_dim"],
			    "initializer_range": 0.02,
			    "intermediate_size": args["decoder_ffn_embed_dim"],
			    "layer_norm_eps": 1e-12,
			    "layerdrop": args["decoder_layerdrop"],
			    "max_position_embeddings": args["max_target_positions"],
			    "model_type": "biogpt",
			    "num_attention_heads": args["decoder_attention_heads"],
			    "num_hidden_layers": args["decoder_layers"],
			    "pad_token_id": 1,
			    "scale_embedding": not args["no_scale_embedding"],
			    "tie_word_embeddings": args["share_decoder_input_output_embed"],
			    "vocab_size": src_vocab_size,
			}

			# good hparam defaults to start with

			print(f'''Generating {biogpt_model_config_file}'''					)
			with open(SCREAMING_SNAKE_CASE_  ,					"w"  ,					encoding="utf-8"					) as f:
						f.write(json.dumps(SCREAMING_SNAKE_CASE_  ,					ensure_ascii=SCREAMING_SNAKE_CASE_  ,					indent=SCREAMING_SNAKE_CASE_					)					)

			# tokenizer config
			lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			{
			    "bos_token": "<s>",
			    "eos_token": "</s>",
			    "model_max_length": 1024,
			    "pad_token": "<pad>",
			    "special_tokens_map_file": None,
			    "tokenizer_class": "BioGptTokenizer",
			    "unk_token": "<unk>",
			}

			print(f'''Generating {biogpt_tokenizer_config_file}'''					)
			with open(SCREAMING_SNAKE_CASE_  ,					"w"  ,					encoding="utf-8"					) as f:
						f.write(json.dumps(SCREAMING_SNAKE_CASE_  ,					ensure_ascii=SCREAMING_SNAKE_CASE_  ,					indent=SCREAMING_SNAKE_CASE_					)					)

			# model
			lowercase__   					=			chkpt["model"]

			# remove unneeded keys
			lowercase__   					=			[
			    "decoder.version",
			]
			for k in ignore_keys:
						model_state_dict.pop(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			list(model_state_dict.keys()					)
			for layer_name in layer_names:
						if layer_name.endswith("output_projection.weight"					):
									lowercase__   					=			model_state_dict.pop(SCREAMING_SNAKE_CASE_					)
						else:
									lowercase__   					=			model_state_dict.pop(SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			BioGptConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			BioGptForCausalLM(SCREAMING_SNAKE_CASE_					)

			# check that it loads ok
			model_new.load_state_dict(SCREAMING_SNAKE_CASE_					)

			# save
			lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			print(f'''Generating {pytorch_weights_dump_path}'''					)
			torch.save(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			print("Conversion is done!"					)


if __name__ == "__main__":
							lowercase_						=       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--biogpt_checkpoint_path""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
							        """ bpecodes, etc."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowercase_						=       parser.parse_args()
							convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









import argparse
import json
import subprocess



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[]

			lowercase__   					=			(
			    f'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
			    " https://api.github.com/repos/huggingface/transformers/actions/runners"
			)
			lowercase__   					=			subprocess.run(SCREAMING_SNAKE_CASE_  ,					shell=SCREAMING_SNAKE_CASE_  ,					stdout=subprocess.PIPE					)
			lowercase__   					=			output.stdout.decode("utf-8"					)
			lowercase__   					=			json.loads(SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			status["runners"]
			for runner in runners:
						if runner["name"] in target_runners:
									if runner["status"] == "offline":
												offline_runners.append(SCREAMING_SNAKE_CASE_					)

    # save the result so we can report them on Slack
			with open("offline_runners.txt"  ,					"w"					) as fp:
						fp.write(json.dumps(SCREAMING_SNAKE_CASE_					)					)

			if len(SCREAMING_SNAKE_CASE_					) > 0:
						lowercase__   					=			"\n".join([x["name"] for x in offline_runners]					)
						raise ValueError(f'''The following runners are offline:\n{failed}'''					)


if __name__ == "__main__":



							def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
										return values.split(","					)

							lowercase_						=       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--target_runners""",
							    default=None,
							    type=list_str,
							    required=True,
							    help="""Comma-separated list of runners to check status.""",
							)

							parser.add_argument(
							    """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
							)
							lowercase_						=       parser.parse_args()




							get_runner_status(args.target_runners, args.token)




