


"""simple docstring"""




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

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


if is_sentencepiece_available():
				from .tokenization_albert import AlbertTokenizer
else:
				lowercase_			     =  None

lowercase_			     =  logging.get_logger(__name__)
lowercase_			     =  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowercase_			     =  {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

lowercase_			     =  {
    "albert-base-v1": 5_1_2,
    "albert-large-v1": 5_1_2,
    "albert-xlarge-v1": 5_1_2,
    "albert-xxlarge-v1": 5_1_2,
    "albert-base-v2": 5_1_2,
    "albert-large-v2": 5_1_2,
    "albert-xlarge-v2": 5_1_2,
    "albert-xxlarge-v2": 5_1_2,
}

lowercase_			     =  "▁"
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				VOCAB_FILES_NAMES
		__UpperCAmelCase		:			List[Any]										=				PRETRAINED_VOCAB_FILES_MAP
		__UpperCAmelCase		:			str										=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCAmelCase		:			int										=				AlbertTokenizer

		def __init__(  self							, _a=None							, _a=None							, _a=True							, _a=True							, _a=False							, _a="[CLS]"							, _a="[SEP]"							, _a="<unk>"							, _a="[SEP]"							, _a="<pad>"							, _a="[CLS]"							, _a="[MASK]"							, **_a							, ):
				# Mask token behave like a normal word, i.e. include the space before it and
				# is included in the raw text, there should be a match in a non-normalized sentence.
				__a           =			(
				    AddedToken(_a							, lstrip=_a							, rstrip=_a							, normalized=_a      )
				    if isinstance(_a							, _a      )
				    else mask_token
				)

				super().__init__(
				    _a							, tokenizer_file=_a							, do_lower_case=_a							, remove_space=_a							, keep_accents=_a							, bos_token=_a							, eos_token=_a							, unk_token=_a							, sep_token=_a							, pad_token=_a							, cls_token=_a							, mask_token=_a							, **_a							, )

				__a           =			do_lower_case
				__a           =			remove_space
				__a           =			keep_accents
				__a           =			vocab_file
				__a           =			False if not self.vocab_file else True

		def 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				__a           =			[self.sep_token_id]
				__a           =			[self.cls_token_id]
				if token_ids_a is None:
						return cls + token_ids_a + sep
				return cls + token_ids_a + sep + token_ids_a + sep

		def 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				__a           =			[self.sep_token_id]
				__a           =			[self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep      ) * [0]
				return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]





		def 			__UpperCAmelCase       (  self							, _a							, _a = 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(_a      ):
						logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
						return
				__a           =			os.path.join(
				    _a							, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

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

				return (out_vocab_file,)










"""simple docstring"""




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def        lowercase							(		lowerCAmelCase__ :     Namespace			) ->      Tuple:
		return ConvertCommand(
		    args.model_type    ,					args.tf_checkpoint    ,					args.pytorch_dump_output    ,					args.config    ,					args.finetuning_task_name			)


lowercase_			     =  "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"




class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		@staticmethod
		def 			__UpperCAmelCase       (  _a      ):
				__a           =			parser.add_parser(
				    '''convert'''							, help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''							, )
				train_parser.add_argument('''--model_type'''							, type=_a							, required=_a							, help='''Model\'s type.'''      )
				train_parser.add_argument(
				    '''--tf_checkpoint'''							, type=_a							, required=_a							, help='''TensorFlow checkpoint path or folder.'''      )
				train_parser.add_argument(
				    '''--pytorch_dump_output'''							, type=_a							, required=_a							, help='''Path to the PyTorch saved model output.'''      )
				train_parser.add_argument('''--config'''							, type=_a							, default=''''''							, help='''Configuration file path or folder.'''      )
				train_parser.add_argument(
				    '''--finetuning_task_name'''							, type=_a							, default=_a							, help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							, )
				train_parser.set_defaults(func=_a      )

		def __init__(  self							, _a							, _a							, _a							, _a							, _a							, *_a							, ):
				__a           =			logging.get_logger('''transformers-cli/converting'''      )

				self._logger.info(f'''Loading model {model_type}'''      )
				__a           =			model_type
				__a           =			tf_checkpoint
				__a           =			pytorch_dump_output
				__a           =			config
				__a           =			finetuning_task_name





		def 			__UpperCAmelCase       (  self      ):
				if self._model_type == "albert":
						try:
								from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "bert":
						try:
								from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "funnel":
						try:
								from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "t5":
						try:
								from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
								raise ImportError(_a      )

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "transfo_xl":
						try:
								from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
								    convert_transfo_xl_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						if "ckpt" in self._tf_checkpoint.lower():
								__a           =			self._tf_checkpoint
								__a           =			''''''
						else:
								__a           =			self._tf_checkpoint
								__a           =			''''''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _a							, self._config							, self._pytorch_dump_output							, _a      )
				elif self._model_type == "gpt2":
						try:
								from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
								    convert_gpta_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				elif self._model_type == "xlnet":
						try:
								from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
								    convert_xlnet_checkpoint_to_pytorch,
								)
						except ImportError:
								raise ImportError(_a      )

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint							, self._config							, self._pytorch_dump_output							, self._finetuning_task_name      )
				elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							, self._pytorch_dump_output      )
				elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							, self._config							, self._pytorch_dump_output      )
				else:
						raise ValueError(
						    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''      )






