






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

				__A      =				model.state_dict()
				__A      =				{}

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

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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):  # noqa: E741

					'''simple docstring'''







					while r - l > 1:
										_A								= (l + r) // 2
										if v[m] >= key:
															_A								= m
										else:
															_A								= m  # noqa: E741
					return r




def      __A					(     _lowercase ):

					'''simple docstring'''







					if len(_lowercase ) == 0:
										return 0

					_A								= [0] * len(_lowercase )
					_A								= 1

					_A								= v[0]

					for i in range(1					,			len(_lowercase ) ):
										if v[i] < tail[0]:
															_A								= v[i]
										elif v[i] > tail[length - 1]:
															_A								= v[i]
															length += 1
										else:
															_A								= v[i]

					return length


if __name__ == "__main__":
				import doctest

				doctest.testmod()



