from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



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







    return ConvertCommand(
        args.model_type  ,      args.tf_checkpoint  ,      args.pytorch_dump_output  ,      args.config  ,      args.finetuning_task_name     )


__lowercase										= '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @staticmethod
    def       UpperCamelCase__       (     __lowercase)	->     Optional[Any]:
        __UpperCamelCase					:Any        =		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=__lowercase			,			required=__lowercase			,			help='''Model\'s type.''')
        train_parser.add_argument(
            '''--tf_checkpoint'''			,			type=__lowercase			,			required=__lowercase			,			help='''TensorFlow checkpoint path or folder.''')
        train_parser.add_argument(
            '''--pytorch_dump_output'''			,			type=__lowercase			,			required=__lowercase			,			help='''Path to the PyTorch saved model output.''')
        train_parser.add_argument('''--config'''			,			type=__lowercase			,			default=''''''			,			help='''Configuration file path or folder.''')
        train_parser.add_argument(
            '''--finetuning_task_name'''			,			type=__lowercase			,			default=__lowercase			,			help='''Optional fine-tuning task name if the TF model was a finetuned model.'''			,			)
        train_parser.set_defaults(func=__lowercase)







    def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			*__lowercase			,			)	->     List[Any]:
        __UpperCamelCase					:Tuple        =		logging.get_logger('''transformers-cli/converting''')

        self._logger.info(f"""Loading model {model_type}""")
        __UpperCamelCase					:Dict        =		model_type
        __UpperCamelCase					:List[str]        =		tf_checkpoint
        __UpperCamelCase					:str        =		pytorch_dump_output
        __UpperCamelCase					:Any        =		config
        __UpperCamelCase					:Any        =		finetuning_task_name







    def       UpperCamelCase__       (     self)	->     List[str]:
        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(__lowercase)

            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(__lowercase)

            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(__lowercase)

            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(__lowercase)

            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(__lowercase)

            if "ckpt" in self._tf_checkpoint.lower():
                __UpperCamelCase					:Optional[Any]        =		self._tf_checkpoint
                __UpperCamelCase					:Tuple        =		''''''
            else:
                __UpperCamelCase					:Optional[int]        =		self._tf_checkpoint
                __UpperCamelCase					:Dict        =		''''''
            convert_transfo_xl_checkpoint_to_pytorch(
                __lowercase			,			self._config			,			self._pytorch_dump_output			,			__lowercase)
        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(__lowercase)

            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(__lowercase)

            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]''')






from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__lowercase										= logging.get_logger(__name__)

__lowercase										= {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase										= {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__lowercase										= {'''allegro/herbert-base-cased''': 514}
__lowercase										= {}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Tuple      =				VOCAB_FILES_NAMES
    a__     :						Dict      =				PRETRAINED_VOCAB_FILES_MAP
    a__     :						Tuple      =				PRETRAINED_INIT_CONFIGURATION
    a__     :						Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__     :						Union[str, Any]      =				HerbertTokenizer







    def __init__(     self			,			__lowercase=None			,			__lowercase=None			,			__lowercase=None			,			__lowercase="<s>"			,			__lowercase="<unk>"			,			__lowercase="<pad>"			,			__lowercase="<mask>"			,			__lowercase="</s>"			,			**__lowercase			,			)	->     Optional[Any]:
        super().__init__(
            __lowercase			,			__lowercase			,			tokenizer_file=__lowercase			,			cls_token=__lowercase			,			unk_token=__lowercase			,			pad_token=__lowercase			,			mask_token=__lowercase			,			sep_token=__lowercase			,			**__lowercase			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:List[str]        =		[self.cls_token_id]
        __UpperCamelCase					:Tuple        =		[self.sep_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			,			__lowercase			,			__lowercase = None			,			__lowercase = False)	->     List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowercase			,			token_ids_a=__lowercase			,			already_has_special_tokens=__lowercase)

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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:Optional[Any]        =		[self.sep_token_id]
        __UpperCamelCase					:int        =		[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			,			__lowercase			,			__lowercase = None)	->     Tuple[str]:
        __UpperCamelCase					:Optional[int]        =		self._tokenizer.model.save(__lowercase			,			name=__lowercase)
        return tuple(__lowercase)





