
"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_pegasus import PegasusTokenizer
else:
							__magic_name__       =			None


__magic_name__       =			logging.get_logger(__name__)


__magic_name__       =			"▁"

__magic_name__       =			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__magic_name__       =			{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

__magic_name__       =			{
    "google/pegasus-xsum": 512,
}
class 						SCREAMING_SNAKE_CASE_ (						__A ):




		"""simple docstring"""





		__lowercase							:  Any						   =		VOCAB_FILES_NAMES
		__lowercase							:  List[str]						   =		PRETRAINED_VOCAB_FILES_MAP
		__lowercase							:  List[str]						   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase							:  Any						   =		PegasusTokenizer
		__lowercase							:  Tuple						   =		['input_ids', 'attention_mask']




		def __init__(     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__="<pad>"							,  lowerCAmelCase__="</s>"							,  lowerCAmelCase__="<unk>"							,  lowerCAmelCase__="<mask_2>"							,  lowerCAmelCase__="<mask_1>"							,  lowerCAmelCase__=None							,  lowerCAmelCase__=1_0_3							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = offset

				if additional_special_tokens is not None:
						if not isinstance(__UpperCAmelCase							,  __UpperCAmelCase):
								raise TypeError(
								    f"additional_special_tokens should be of type {type(__UpperCAmelCase)}, but is"
								    f" {type(__UpperCAmelCase)}")

						__SCREAMING_SNAKE_CASE            = (
						    ([mask_token_sent] + additional_special_tokens)
						    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
						    else additional_special_tokens
						)
						# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
						additional_special_tokens_extended += [
						    f"<unk_{i}>" for i in range(len(__UpperCAmelCase)							,  self.offset - 1)
						]

						if len(set(__UpperCAmelCase)) != len(__UpperCAmelCase):
								raise ValueError(
								    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
								    f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.")
						__SCREAMING_SNAKE_CASE            = additional_special_tokens_extended
				else:
						__SCREAMING_SNAKE_CASE            = [mask_token_sent] if mask_token_sent is not None else []
						additional_special_tokens += [f"<unk_{i}>" for i in range(2							,  self.offset)]

				super().__init__(
				    __UpperCAmelCase							,  tokenizer_file=__UpperCAmelCase							,  pad_token=__UpperCAmelCase							,  eos_token=__UpperCAmelCase							,  unk_token=__UpperCAmelCase							,  mask_token=__UpperCAmelCase							,  mask_token_sent=__UpperCAmelCase							,  offset=__UpperCAmelCase							,  additional_special_tokens=__UpperCAmelCase							,  **__UpperCAmelCase							,  )
				__SCREAMING_SNAKE_CASE            = vocab_file
				__SCREAMING_SNAKE_CASE            = False if not self.vocab_file else True




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = set(self.all_special_ids)  # call it once instead of inside list comp
				all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

				if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
						raise ValueError(
						    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
						    f" {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}")

				return [1 if x in all_special_ids else 0 for x in seq]




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False):
				if already_has_special_tokens:
						return self._special_token_mask(__UpperCAmelCase)
				elif token_ids_a is None:
						return self._special_token_mask(__UpperCAmelCase) + [1]
				else:
						return self._special_token_mask(token_ids_a + token_ids_a) + [1]




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=None):
				if token_ids_a is None:
						return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None):
				if not self.can_save_slow_tokenizer:
						raise ValueError(
						    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
						    """tokenizer.""")

				if not os.path.isdir(__UpperCAmelCase):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
				__SCREAMING_SNAKE_CASE            = os.path.join(
				    __UpperCAmelCase							,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

				if os.path.abspath(self.vocab_file) != os.path.abspath(__UpperCAmelCase):
						copyfile(self.vocab_file							,  __UpperCAmelCase)

				return (out_vocab_file,)





"""simple docstring"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()				,			UpperCamelCase__ )


if __name__ == "__main__":
				__lowerCamelCase							 =     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--albert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained ALBERT model. \n"
				        "This specifies the model architecture."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





