






"""simple docstring"""
import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer


class _lowerCAmelCase						(    tf.keras.layers.Layer       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None							)		->							Union[str, Any]:
										super().__init__()
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    pad_token_id
										_SCREAMING_SNAKE_CASE   :		int       		=    max_length
										_SCREAMING_SNAKE_CASE   :		Any       		=    vocab
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    merges
										_SCREAMING_SNAKE_CASE   :		str       		=    BytePairTokenizer(lowerCAmelCase_   ,				lowerCAmelCase_   ,				sequence_length=lowerCAmelCase_							)







						@classmethod
						def 			A      (    cls   ,				lowerCAmelCase_   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    [' '.join(lowerCAmelCase_							) for m in tokenizer.bpe_ranks.keys()]
										_SCREAMING_SNAKE_CASE   :		str       		=    tokenizer.get_vocab()
										return cls(lowerCAmelCase_   ,				lowerCAmelCase_   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)







						@classmethod
						def 			A      (    cls   ,				lowerCAmelCase_   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    GPTaTokenizer.from_pretrained(lowerCAmelCase_   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)
										return cls.from_tokenizer(lowerCAmelCase_   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)







						@classmethod
						def 			A      (    cls   ,				lowerCAmelCase_							)		->							Optional[Any]:
										return cls(**lowerCAmelCase_							)







						def 			A      (    self							)		->							str:
										return {
										    "vocab": self.vocab,
										    "merges": self.merges,
										    "max_length": self.max_length,
										    "pad_token_id": self.pad_token_id,
										}







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None							)		->							Union[str, Any]:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.tf_tokenizer(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.ones_like(lowerCAmelCase_							)

										if self.pad_token_id is not None:
														# pad the tokens up to max length
														_SCREAMING_SNAKE_CASE   :		str       		=    max_length if max_length is not None else self.max_length

														if max_length is not None:
																		_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    pad_model_inputs(
																		    lowerCAmelCase_   ,				max_seq_length=lowerCAmelCase_   ,				pad_value=self.pad_token_id							)

										return {"attention_mask": attention_mask, "input_ids": input_ids}









"""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)

