






'''simple docstring'''
def 			_a(	UpperCamelCase__					:    Any,						UpperCamelCase__					:    Optional[Any],						UpperCamelCase__					:    Tuple,						UpperCamelCase__					:    str,						UpperCamelCase__					:    List[Any],						UpperCamelCase__					:    Optional[int]				):




      '''simple docstring'''






      if index == r:
            for j in range(UpperCamelCase__				):
                  print(data[j],						end=''' '''				)
            print(''' '''				)
            return
      #  When no more elements are there to put in data[]
      if i >= n:
            return
      # current is included, put next at next location
      SCREAMING_SNAKE_CASE__  :					Optional[Any]						=arr[i]
      combination_util(UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__,						index + 1,						UpperCamelCase__,						i + 1				)
      # current is excluded, replace it with
      # next (Note that i+1 is passed, but
      # index is not changed)
      combination_util(UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__,						i + 1				)
      # The main function that prints all combinations
      # of size r in arr[] of size n. This function
      # mainly uses combinationUtil()





def 			_a(	UpperCamelCase__					:    int,						UpperCamelCase__					:    Optional[Any],						UpperCamelCase__					:    Any				):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					List[Any]						=[0] * r
      # Print all combination using temporary array 'data[]'
      combination_util(UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__,						0,						UpperCamelCase__,						0				)


if __name__ == "__main__":
  # Driver code to check the function above
  a_											=			[1_0, 2_0, 3_0, 4_0, 5_0]
  print_combination(arr, len(arr), 3)
  # This code is contributed by Ambuj sahu







"""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)

