





'''simple docstring'''


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 						__a(SCREAMING_SNAKE_CASE_	:       Namespace     ):



    '''simple docstring'''





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


_SCREAMING_SNAKE_CASE									=				"\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_		(		__magic_name__		):




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




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  *_lowerCAmelCase      ,  )     ->				Dict:
     _lowerCAmelCase						      =   logging.get_logger("transformers-cli/converting"		)

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





 def 				_snake_case (						self		)     ->				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(_lowerCAmelCase		)

         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(_lowerCAmelCase		)

         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(_lowerCAmelCase		)

         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(_lowerCAmelCase		)

         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(_lowerCAmelCase		)

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

         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(_lowerCAmelCase		)

         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 json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     BlenderbotSmallTokenizer
						_lowerCamelCase:     List[Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							super().setUp()

							A     = ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
							A     = dict(zip(A_	,range(len(A_   )   )   )   )

							A     = ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
							A     = {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['merges_file']   )
							with open(self.vocab_file	,'w'	,encoding='utf-8'   ) as fp:
								fp.write(json.dumps(A_   ) + '\n'   )
							with open(self.merges_file	,'w'	,encoding='utf-8'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,**A_  :   Union[str, Any]   )  -> Optional[int]:
							kwargs.update(self.special_tokens_map   )
							return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = 'adapt act apte'
							A     = 'adapt act apte'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[str]:
							A     = BlenderbotSmallTokenizer(self.vocab_file	,self.merges_file	,**self.special_tokens_map   )
							A     = 'adapt act apte'
							A     = ['adapt', 'act', 'ap@@', 'te']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

							A     = [0, 1, 2, 3, 4, 5]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							assert tok('sam'   ).input_ids == [1384]
							A     = 'I am a small frog.'
							A     = tok([src_text]	,padding=A_	,truncation=A_   )['input_ids']
							A     = tok.batch_decode(A_	,skip_special_tokens=A_	,clean_up_tokenization_spaces=A_   )[0]
							assert src_text != decoded  # I wish it did!
							assert decoded == "i am a small frog ."




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> int:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							A     = 'I am a small frog .'
							A     = '.'
							A     = tok(A_   )['input_ids']
							A     = tok(A_   )['input_ids']

							assert encoded[-1] == encoded_dot[0]