



"""simple docstring"""






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__      =    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''')
			lowerCAmelCase__      =    parser.parse_args()

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

			lowerCAmelCase__      =    model.state_dict()
			lowerCAmelCase__      =    {}

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

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







"""simple docstring"""






from itertools import permutations






def 	snake_case_  (				A_ :		tuple						):


       '''simple docstring'''





       if num[3] % 2 != 0:
              return False

       if (num[2] + num[3] + num[4]) % 3 != 0:
              return False

       if num[5] % 5 != 0:
              return False

       _lowerCamelCase						:    Any         =			[7, 11, 13, 17]
       for i, test in enumerate(A_						):
              if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
                     return False
       return True






def 	snake_case_  (				A_ :		int = 10						):


       '''simple docstring'''





       return sum(
           int(''''''.join(map(A_,    A_						)						)						)
           for num in permutations(range(A_						)						)
           if is_substring_divisible(A_						)						)


if __name__ == "__main__":
   print(F"""{solution() = }""")


