
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
					_UpperCAmelCase					: str      =       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")
					_UpperCAmelCase					: List[str]      =       parser.parse_args()

					if args.model_type == "roberta":
										_UpperCAmelCase					: Optional[Any]      =       RobertaForMaskedLM.from_pretrained(args.model_name)
										_UpperCAmelCase					: Optional[int]      =       "roberta"
					elif args.model_type == "gpt2":
										_UpperCAmelCase					: Any      =       GPTaLMHeadModel.from_pretrained(args.model_name)
										_UpperCAmelCase					: List[str]      =       "transformer"

					_UpperCAmelCase					: Dict      =       model.state_dict()
					_UpperCAmelCase					: List[Any]      =       {}

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

    # Transformer Blocks #
					_UpperCAmelCase					: Optional[Any]      =       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"]:
																									_UpperCAmelCase					: List[Any]      =       state_dict[
																									    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																									]
															_UpperCAmelCase					: int      =       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"]:
																									_UpperCAmelCase					: Union[str, Any]      =       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"]:
															_UpperCAmelCase					: int      =       state_dict[F'''{layer}''']
										if args.vocab_transform:
															for w in ["weight", "bias"]:
																				_UpperCAmelCase					: List[str]      =       state_dict[F'''lm_head.dense.{w}''']
																				_UpperCAmelCase					: Dict      =       state_dict[F'''lm_head.layer_norm.{w}''']
					elif args.model_type == "gpt2":
										for w in ["weight", "bias"]:
															_UpperCAmelCase					: Tuple      =       state_dict[F'''{prefix}.ln_f.{w}''']
										_UpperCAmelCase					: Dict      =       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)


def       A       (					lowercase )   ->  list:
		'''simple docstring'''

		UpperCamelCase		=     len(lowercase )
		for i in range(1    ,       lowercase ):
				UpperCamelCase		=     collection[i]
				UpperCamelCase		=     0
				UpperCamelCase		=     i - 1

				while low <= high:
						UpperCamelCase		=     (low + high) // 2
						if val < collection[mid]:
								UpperCamelCase		=     mid - 1
						else:
								UpperCamelCase		=     mid + 1
				for j in range(lowercase    ,       lowercase    ,       -1 ):
						UpperCamelCase		=     collection[j - 1]
				UpperCamelCase		=     val
		return collection


if __name__ == "__main__":
					_UpperCAmelCase					: List[Any]      =       input("Enter numbers separated by a comma:\n").strip()
					_UpperCAmelCase					: Optional[int]      =       [int(item) for item in user_input.split(",")]
					print(binary_insertion_sort(unsorted))
