



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      BarthezTokenizer
     _A      =      BarthezTokenizerFast
     _A      =      True
     _A      =      True







     def     _a   (self     ):






           '''simple docstring'''



           super().setUp()

           lowerCamelCase             =		BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"     )
           tokenizer.save_pretrained(self.tmpdirname     )
           tokenizer.save_pretrained(self.tmpdirname					,      legacy_format=__a     )
           lowerCamelCase             =		tokenizer







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"<pad>"
           lowerCamelCase             =		1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a     )					,      __a     )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a     )					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		list(self.get_tokenizer().get_vocab().keys()     )

           self.assertEqual(vocab_keys[0]					,      "<s>"     )
           self.assertEqual(vocab_keys[1]					,      "<pad>"     )
           self.assertEqual(vocab_keys[-1]					,      "<mask>"     )
           self.assertEqual(len(__a     )					,      10_11_22     )







     def     _a   (self     ):






           '''simple docstring'''



           self.assertEqual(self.get_tokenizer().vocab_size					,      10_11_22     )







     @require_torch
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		["A long paragraph for summarization.", "Another paragraph for summarization."]
           lowerCamelCase             =		[0, 57, 30_18, 7_03_07, 91, 2]

           lowerCamelCase             =		self.tokenizer(
               __a					,      max_length=len(__a     )					,      padding=__a					,      truncation=__a					,      return_tensors="pt"     )
           self.assertIsInstance(__a					,      __a     )

           self.assertEqual((2, 6)					,      batch.input_ids.shape     )
           self.assertEqual((2, 6)					,      batch.attention_mask.shape     )
           lowerCamelCase             =		batch.input_ids.tolist()[0]
           self.assertListEqual(__a					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           if not self.test_rust_tokenizer:
                 return

           lowerCamelCase             =		self.get_tokenizer()
           lowerCamelCase             =		self.get_rust_tokenizer()

           lowerCamelCase             =		"I was born in 92000, and this is falsé."

           lowerCamelCase             =		tokenizer.tokenize(__a     )
           lowerCamelCase             =		rust_tokenizer.tokenize(__a     )
           self.assertListEqual(__a					,      __a     )

           lowerCamelCase             =		tokenizer.encode(__a					,      add_special_tokens=__a     )
           lowerCamelCase             =		rust_tokenizer.encode(__a					,      add_special_tokens=__a     )
           self.assertListEqual(__a					,      __a     )

           lowerCamelCase             =		self.get_rust_tokenizer()
           lowerCamelCase             =		tokenizer.encode(__a     )
           lowerCamelCase             =		rust_tokenizer.encode(__a     )
           self.assertListEqual(__a					,      __a     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		{"input_ids": [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
           # fmt: on

           # moussaKam/mbarthez is a french model. So we also use french texts.
           lowerCamelCase             =		[
               "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
               "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
               "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
               "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
               "telles que la traduction et la synthèse de texte.",
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=__a					,      model_name="moussaKam/mbarthez"					,      revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"					,      sequences=__a					,      )




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  Any						  =		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(
         '--mobilebert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained MobileBERT 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.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)