
"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 							A_     (__a				):

     '''simple docstring'''





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


UpperCamelCase_					:						Tuple				= '''
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				(					_lowercase      ):




   """simple docstring"""







   @staticmethod
   def      lowerCamelCase__     (				_snake_case						:					ArgumentParser	)      -> Union[str, Any]:







        """simple docstring"""
        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=_snake_case       ,   required=_snake_case       ,   help="Model's type."	)
        train_parser.add_argument(
            "--tf_checkpoint"       ,   type=_snake_case       ,   required=_snake_case       ,   help="TensorFlow checkpoint path or folder."	)
        train_parser.add_argument(
            "--pytorch_dump_output"       ,   type=_snake_case       ,   required=_snake_case       ,   help="Path to the PyTorch saved model output."	)
        train_parser.add_argument("--config"       ,   type=_snake_case       ,   default=""       ,   help="Configuration file path or folder."	)
        train_parser.add_argument(
            "--finetuning_task_name"       ,   type=_snake_case       ,   default=_snake_case       ,   help="Optional fine-tuning task name if the TF model was a finetuned model."       ,   )
        train_parser.set_defaults(func=_snake_case	)




   def __init__(				self						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   *_snake_case						:					Optional[int]       ,   )      -> Union[str, Any]:







        """simple docstring"""
        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      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







        """simple docstring"""
        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(_snake_case	)

             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(_snake_case	)

             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(_snake_case	)

             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(_snake_case	)

             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(_snake_case	)

             if "ckpt" in self._tf_checkpoint.lower():
                  A_				 =						self._tf_checkpoint
                  A_				 =						""
             else:
                  A_				 =						self._tf_checkpoint
                  A_				 =						""
             convert_transfo_xl_checkpoint_to_pytorch(
                 _snake_case       ,   self._config       ,   self._pytorch_dump_output       ,   _snake_case	)
        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(_snake_case	)

             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(_snake_case	)

             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]"	)






"""simple docstring"""





import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TransfoXLTokenizer
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Optional[int]:







								"""simple docstring"""
								super().setUp()

								A_				 =						[
								    "<unk>",
								    "[CLS]",
								    "[SEP]",
								    "want",
								    "unwanted",
								    "wa",
								    "un",
								    "running",
								    ",",
								    "low",
								    "l",
								]
								A_				 =						os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES["vocab_file"]	)
								with open(self.vocab_file       ,   "w"       ,   encoding="utf-8"	) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)




			def      lowerCamelCase__     (				self						:					str       ,   **_snake_case						:					Any	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,   **_snake_case	)




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Optional[Any]	)      -> Any:







								"""simple docstring"""
								A_				 =						"<unk> UNwanted , running"
								A_				 =						"<unk> unwanted, running"
								return input_text, output_text




			def      lowerCamelCase__     (				self						:					Dict	)      -> int:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(vocab_file=self.vocab_file       ,   lower_case=_snake_case	)

								A_				 =						tokenizer.tokenize("<unk> UNwanted , running"	)
								self.assertListEqual(_snake_case       ,   ["<unk>", "unwanted", ",", "running"]	)

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case	)       ,   [0, 4, 8, 7]	)




			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(lower_case=_snake_case	)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "	)       ,   ["hello", "!", "how", "are", "you", "?"]	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> str:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(lower_case=_snake_case	)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "	)       ,   ["HeLLo", "!", "how", "Are", "yoU", "?"]	)




			def      lowerCamelCase__     (				self						:					Dict	)      -> Tuple:







								"""simple docstring"""
								A_				 =						TransfoXLTokenizer(lower_case=_snake_case	)
								A_				 =						"Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
								A_				 =						[
								    "Hello",
								    "(",
								    "bracket",
								    ")",
								    "and",
								    "side",
								    "@-@",
								    "scrolled",
								    "[",
								    "and",
								    "]",
								    "Henry",
								    "'s",
								    "$",
								    "5",
								    "@,@",
								    "000",
								    "with",
								    "3",
								    "@.@",
								    "34",
								    "m",
								    ".",
								    "What",
								    "'s",
								    "up",
								    "!",
								    "?",
								]

								self.assertListEqual(tokenizer.tokenize(_snake_case	)       ,   _snake_case	)

								self.assertEqual(tokenizer.convert_tokens_to_string(_snake_case	)       ,   _snake_case	)






			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.get_tokenizer()
								A_				 =						len(_snake_case	)

								tokenizer.add_tokens(["new1", "new2"]	)
								tokenizer.move_added_token("new1"       ,   1	)

								# Check that moved token is not copied (duplicate)
								self.assertEqual(len(_snake_case	)       ,   original_len + 2	)
								# Check that token is moved to specified id
								self.assertEqual(tokenizer.encode("new1"	)       ,   [1]	)
								self.assertEqual(tokenizer.decode([1]	)       ,   "new1"	)




