



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase_						=       argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							lowercase_						=       parser.parse_args()

							if args.model_type == "roberta":
														lowercase_						=       RobertaForMaskedLM.from_pretrained(args.model_name)
														lowercase_						=       """roberta"""
							elif args.model_type == "gpt2":
														lowercase_						=       GPTaLMHeadModel.from_pretrained(args.model_name)
														lowercase_						=       """transformer"""

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowercase_						=       state_dict[F'{prefix}.{param_name}']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowercase_						=       F'{prefix}.embeddings.{w}.weight'
																					lowercase_						=       state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowercase_						=       F'{prefix}.embeddings.LayerNorm.{w}'
																					lowercase_						=       state_dict[param_name]

    # Transformer Blocks #
							lowercase_						=       0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowercase_						=       state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowercase_						=       state_dict[F'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase_						=       state_dict[F'lm_head.dense.{w}']
																												lowercase_						=       state_dict[F'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase_						=       state_dict[F'{prefix}.ln_f.{w}']
														lowercase_						=       state_dict["""lm_head.weight"""]

							print(F'N layers selected for distillation: {std_idx}')
							print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

							print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
							torch.save(compressed_sd, args.dump_checkpoint)









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)




