






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   [
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''decoder.output_projection.weight''',
				    '''_float_tensor''',
				    '''encoder.embed_positions._float_tensor''',
				    '''decoder.embed_positions._float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(snake_case,	snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   list(s_dict.keys()			)
				for key in keys:
								if "transformer_layers" in key:
												__magic_name__		:Tuple   =   s_dict.pop(snake_case			)
								elif "subsample" in key:
												__magic_name__		:Optional[int]   =   s_dict.pop(snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Optional[int]   =   emb.weight.shape
				__magic_name__		:Optional[Any]   =   nn.Linear(snake_case,	snake_case,	bias=snake_case			)
				__magic_name__		:List[str]   =   emb.weight.data
				return lin_layer

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   torch.load(snake_case,	map_location='''cpu'''			)
				__magic_name__		:List[Any]   =   mam_aaa['''args''']
				__magic_name__		:List[str]   =   mam_aaa['''model''']
				__magic_name__		:Optional[Any]   =   state_dict['''decoder.output_projection.weight''']

				remove_ignore_keys_(snake_case			)
				rename_keys(snake_case			)

				__magic_name__		:List[Any]   =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

				__magic_name__		:Union[str, Any]   =   args.share_decoder_input_output_embed

				__magic_name__		:Any   =   [int(snake_case			) for i in args.conv_kernel_sizes.split(''','''			)]
				__magic_name__		:List[Any]   =   SpeechaTextConfig(
				    vocab_size=snake_case,	max_source_positions=args.max_source_positions,	max_target_positions=args.max_target_positions,	encoder_layers=args.encoder_layers,	decoder_layers=args.decoder_layers,	encoder_attention_heads=args.encoder_attention_heads,	decoder_attention_heads=args.decoder_attention_heads,	encoder_ffn_dim=args.encoder_ffn_embed_dim,	decoder_ffn_dim=args.decoder_ffn_embed_dim,	d_model=args.encoder_embed_dim,	dropout=args.dropout,	attention_dropout=args.attention_dropout,	activation_dropout=args.activation_dropout,	activation_function='''relu''',	num_conv_layers=len(snake_case			),	conv_channels=args.conv_channels,	conv_kernel_sizes=snake_case,	input_feat_per_channel=args.input_feat_per_channel,	input_channels=args.input_channels,	tie_word_embeddings=snake_case,	num_beams=5,	max_length=2_0_0,	use_cache=snake_case,	decoder_start_token_id=2,	early_stopping=snake_case,	)

				__magic_name__		:str   =   SpeechaTextForConditionalGeneration(snake_case			)
				__magic_name__  ,					__magic_name__		:List[Any]   =   model.model.load_state_dict(snake_case,	strict=snake_case			)
				if len(snake_case			) > 0 and not set(snake_case			) <= {
				    "encoder.embed_positions.weights",
				    "decoder.embed_positions.weights",
				}:
								raise ValueError(
								    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
								    f''' but all the following weights are missing {missing}'''			)

				if tie_embeds:
								__magic_name__		:Tuple   =   make_linear_from_emb(model.model.decoder.embed_tokens			)
				else:
								__magic_name__		:Optional[Any]   =   lm_head_weights

				model.save_pretrained(snake_case			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Dict				=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					SCREAMING_SNAKE_CASE__   :      str				=		parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)













'''simple docstring'''






import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
A__							:  List[str]    =logging.getLogger(__name__)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				argparse.ArgumentParser(
          description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""    )
      parser.add_argument("""--file_path"""				,      type=lowerCAmelCase				,      default="""data/dump.txt"""				,      help="""The path to the data."""    )
      parser.add_argument("""--tokenizer_type"""				,      type=lowerCAmelCase				,      default="""bert"""				,      choices=["""bert""", """roberta""", """gpt2"""]    )
      parser.add_argument("""--tokenizer_name"""				,      type=lowerCAmelCase				,      default="""bert-base-uncased"""				,      help="""The tokenizer to use."""    )
      parser.add_argument("""--dump_file"""				,      type=lowerCAmelCase				,      default="""data/dump"""				,      help="""The dump file prefix."""    )
      _lowerCAmelCase					=				parser.parse_args()

      logger.info(f"Loading Tokenizer ({args.tokenizer_name})"    )
      if args.tokenizer_type == "bert":
            _lowerCAmelCase					=				BertTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            _lowerCAmelCase					=				RobertaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            _lowerCAmelCase					=				GPTaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

      logger.info(f"Loading text from {args.file_path}"    )
      with open(args.file_path				,      """r"""				,      encoding="""utf8"""    ) as fp:
            _lowerCAmelCase					=				fp.readlines()

      logger.info("""Start encoding"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples to process."    )

      _lowerCAmelCase					=				[]
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				1_00_00
      _lowerCAmelCase					=				time.time()
      for text in data:
            _lowerCAmelCase					=				f"{bos} {text.strip()} {sep}"
            _lowerCAmelCase					=				tokenizer.encode(lowerCAmelCase				,      add_special_tokens=lowerCAmelCase    )
            rslt.append(lowerCAmelCase    )

            iter += 1
            if iter % interval == 0:
                  _lowerCAmelCase					=				time.time()
                  logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"    )
                  _lowerCAmelCase					=				time.time()
      logger.info("""Finished binarization"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples processed."    )

      _lowerCAmelCase					=				f"{args.dump_file}.{args.tokenizer_name}.pickle"
      _lowerCAmelCase					=				tokenizer.vocab_size
      if vocab_size < (1 << 16):
            _lowerCAmelCase					=				[np.uintaa(lowerCAmelCase    ) for d in rslt]
      else:
            _lowerCAmelCase					=				[np.intaa(lowerCAmelCase    ) for d in rslt]
      random.shuffle(rslt_    )
      logger.info(f"Dump to {dp_file}"    )
      with open(lowerCAmelCase				,      """wb"""    ) as handle:
            pickle.dump(rslt_				,      lowerCAmelCase				,      protocol=pickle.HIGHEST_PROTOCOL    )


if __name__ == "__main__":
      main()






