





"""simple docstring"""




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


a						: int     	=			logging.get_logger(__name__)
logging.set_verbosity_info()






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    str					) ->Any:






	'''simple docstring'''






	if "xprophetnet" in prophetnet_checkpoint_path:
		a      :		Optional[Any]	=				XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowercase					)
		a,       a      :		Optional[Any]	=				XLMProphetNetForConditionalGeneration.from_pretrained(
		    _lowercase    ,       output_loading_info=_lowercase					)
	else:
		a      :		Optional[Any]	=				ProphetNetForConditionalGenerationOld.from_pretrained(_lowercase					)
		a,       a      :		Tuple	=				ProphetNetForConditionalGeneration.from_pretrained(
		    _lowercase    ,       output_loading_info=_lowercase					)

	a      :		Tuple	=				["key_proj", "value_proj", "query_proj"]

	a      :		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"]:
		a      :		Optional[int]	=				key.split("."					)

		if attributes[0] == "lm_head":
			a      :		List[Any]	=				prophet
			a      :		Any	=				prophet_old
		else:
			a      :		Dict	=				prophet.prophetnet
			a      :		int	=				prophet_old.model

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

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

				elif attribute == "key_proj":
					a      :		List[str]	=				nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
					a      :		List[Any]	=				nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
				elif attribute == "value_proj":
					a      :		List[Any]	=				nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
					a      :		Any	=				nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
				a      :		List[str]	=				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."
				a      :		Tuple	=				nn.Parameter(old_model.embed_positions.weight[:512, :]					)
				a      :		Tuple	=				True
				break

			if attribute.isdigit():
				a      :		Union[str, Any]	=				model[int(_lowercase					)]
				a      :		int	=				old_model[int(_lowercase					)]
			else:
				a      :		List[Any]	=				getattr(_lowercase    ,       _lowercase					)

				if old_attribute == "":
					a      :		Union[str, Any]	=				old_model
				else:
					if not hasattr(_lowercase    ,       _lowercase					):
						raise ValueError(F"""{old_model} does not have {old_attribute}"""					)
					a      :		Dict	=				getattr(_lowercase    ,       _lowercase					)

		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(_lowercase					)


if __name__ == "__main__":
		a						: Any     	=			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.'''
		)
		a						: Optional[int]     	=			parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_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						: Union[str, Any]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






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

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

	logger.info("Start encoding"					)
	logger.info(F"""{len(_lowercase					)} examples to process."""					)

	a      :		Optional[Any]	=				[]
	a      :		Optional[Any]	=				0
	a      :		int	=				1_0000
	a      :		Dict	=				time.time()
	for text in data:
		a      :		List[Any]	=				F"""{bos} {text.strip()} {sep}"""
		a      :		Optional[int]	=				tokenizer.encode(_lowercase    ,       add_special_tokens=_lowercase					)
		rslt.append(_lowercase					)

		iter += 1
		if iter % interval == 0:
			a      :		Optional[Any]	=				time.time()
			logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""					)
			a      :		Optional[Any]	=				time.time()
	logger.info("Finished binarization"					)
	logger.info(F"""{len(_lowercase					)} examples processed."""					)

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


if __name__ == "__main__":
		main()


