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
)
__lowercase										= logging.getLogger(__name__)



def lowerCamelCase  (       ):
    '''simple docstring'''







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

    logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""     )
    if args.tokenizer_type == "bert":
        __UpperCamelCase					:Tuple        =		BertTokenizer.from_pretrained(args.tokenizer_name     )
        __UpperCamelCase					:List[str]        =		tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
        __UpperCamelCase					:Optional[int]        =		tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
    elif args.tokenizer_type == "roberta":
        __UpperCamelCase					:str        =		RobertaTokenizer.from_pretrained(args.tokenizer_name     )
        __UpperCamelCase					:Union[str, Any]        =		tokenizer.special_tokens_map['''cls_token''']  # `<s>`
        __UpperCamelCase					:Any        =		tokenizer.special_tokens_map['''sep_token''']  # `</s>`
    elif args.tokenizer_type == "gpt2":
        __UpperCamelCase					:List[str]        =		GPTaTokenizer.from_pretrained(args.tokenizer_name     )
        __UpperCamelCase					:List[Any]        =		tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
        __UpperCamelCase					:Dict        =		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:
        __UpperCamelCase					:Dict        =		fp.readlines()

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

    __UpperCamelCase					:List[str]        =		[]
    __UpperCamelCase					:Optional[int]        =		0
    __UpperCamelCase					:Optional[int]        =		10_000
    __UpperCamelCase					:Any        =		time.time()
    for text in data:
        __UpperCamelCase					:Optional[Any]        =		f"""{bos} {text.strip()} {sep}"""
        __UpperCamelCase					:Dict        =		tokenizer.encode(SCREAMING_SNAKE_CASE  ,      add_special_tokens=SCREAMING_SNAKE_CASE     )
        rslt.append(SCREAMING_SNAKE_CASE     )

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

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


if __name__ == "__main__":
    main()






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


__lowercase										= logging.get_logger(__name__)
logging.set_verbosity_info()



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if "xprophetnet" in prophetnet_checkpoint_path:
        __UpperCamelCase					:Dict        =		XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:int        =		XLMProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		['''key_proj''', '''value_proj''', '''query_proj''']

    __UpperCamelCase					:Optional[Any]        =		{
        '''self_attn''': '''ngram_self_attn''',
        '''cross_attn''': '''encoder_attn''',
        '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
        '''feed_forward_layer_norm''': '''final_layer_norm''',
        '''feed_forward''': '''''',
        '''intermediate''': '''fc1''',
        '''output''': '''fc2''',
        '''key_proj''': '''k_proj''',
        '''query_proj''': '''q_proj''',
        '''value_proj''': '''v_proj''',
        '''word_embeddings''': '''embed_tokens''',
        '''embeddings_layer_norm''': '''emb_layer_norm''',
        '''relative_pos_embeddings''': '''relative_linear''',
        '''ngram_embeddings''': '''ngram_input_embed''',
        '''position_embeddings''': '''embed_positions''',
    }

    for key in loading_info["missing_keys"]:
        __UpperCamelCase					:Tuple        =		key.split('''.'''     )

        if attributes[0] == "lm_head":
            __UpperCamelCase					:Union[str, Any]        =		prophet
            __UpperCamelCase					:Any        =		prophet_old
        else:
            __UpperCamelCase					:Any        =		prophet.prophetnet
            __UpperCamelCase					:int        =		prophet_old.model

        __UpperCamelCase					:Optional[Any]        =		False
        for attribute in attributes:
            if attribute in mapping:
                __UpperCamelCase					:str        =		mapping[attribute]
                if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ) and len(SCREAMING_SNAKE_CASE     ) > 0:
                    __UpperCamelCase					:Optional[int]        =		attribute
            elif hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                __UpperCamelCase					:List[Any]        =		attribute

            if attribute == "weight":
                assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                __UpperCamelCase					:Tuple        =		old_model.weight
                logger.info(f"""{attribute} is initialized."""     )
                __UpperCamelCase					:Union[str, Any]        =		True
                break
            elif attribute == "bias":
                assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                __UpperCamelCase					:Union[str, Any]        =		old_model.bias
                logger.info(f"""{attribute} is initialized"""     )
                __UpperCamelCase					:List[Any]        =		True
                break
            elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE  ,      '''in_proj_weight'''     ):
                __UpperCamelCase					:str        =		old_model.in_proj_weight.shape[0] // 3
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
                param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                if attribute == "query_proj":
                    __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.in_proj_weight[:embed_dim, :]     )
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_bias[:embed_dim]     )

                elif attribute == "key_proj":
                    __UpperCamelCase					:List[Any]        =		nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]     )
                elif attribute == "value_proj":
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]     )
                __UpperCamelCase					:Optional[int]        =		True
                break
            elif attribute == "position_embeddings":
                assert (
                    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                ), "Hidden size has to match"
                assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.embed_positions.weight[:512, :]     )
                __UpperCamelCase					:List[Any]        =		True
                break

            if attribute.isdigit():
                __UpperCamelCase					:List[Any]        =		model[int(SCREAMING_SNAKE_CASE     )]
                __UpperCamelCase					:Optional[int]        =		old_model[int(SCREAMING_SNAKE_CASE     )]
            else:
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

                if old_attribute == "":
                    __UpperCamelCase					:Any        =		old_model
                else:
                    if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                        raise ValueError(f"""{old_model} does not have {old_attribute}"""     )
                    __UpperCamelCase					:Any        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

        if not is_key_init:
            raise ValueError(f"""{key} was not correctly initialized!"""     )

    print(f"""Saving model to {pytorch_dump_folder_path}"""     )
    prophet.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __lowercase										= parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





