






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		UpperCamelCase			    =     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			    =     parser.parse_args()

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

		UpperCamelCase			    =     model.state_dict()
		UpperCamelCase			    =     {}

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

    # Transformer Blocks #
		UpperCamelCase			    =     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			    =     state_dict[
										    f"{prefix}.h.{teacher_idx}.{layer}.{w}"
										]
						UpperCamelCase			    =     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			    =     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			    =     state_dict[f"{layer}"]
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								UpperCamelCase			    =     state_dict[f"lm_head.dense.{w}"]
								UpperCamelCase			    =     state_dict[f"lm_head.layer_norm.{w}"]
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						UpperCamelCase			    =     state_dict[f"{prefix}.ln_f.{w}"]
				UpperCamelCase			    =     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)









from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  prime_factors(snake_case__		)
						if is_square_free(snake_case__		):
												return -1 if len(snake_case__		) % 2 else 1
						return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

