






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase        =				get_tests_dir("fixtures/test_sentencepiece.model")
UpperCamelCase        =				get_tests_dir("fixtures/test_sentencepiece_bpe.model")

UpperCamelCase        =				"pt" if is_torch_available() else "tf"

@require_sentencepiece
@require_tokenizers
class        lowerCAmelCase_				(      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   Dict        =			CamembertTokenizer
				_snake_case				:   Optional[int]        =			CamembertTokenizerFast
				_snake_case				:   Tuple        =			True
				_snake_case				:   Dict        =			True







				def 				__a   ( self     :Optional[int]     ):
					super().setUp()

					# We have a SentencePiece fixture for testing
					UpperCamelCase__ :Union[str, Any]						=      CamembertTokenizer(lowerCamelCase__     )
					tokenizer.save_pretrained(self.tmpdirname     )







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :List[str]						=      """<pad>"""
					UpperCamelCase__ :Dict						=      1

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







				def 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :Dict						=      list(self.get_tokenizer().get_vocab().keys()     )

					self.assertEqual(vocab_keys[0]  ,     """<s>NOTUSED"""     )
					self.assertEqual(vocab_keys[1]  ,     """<pad>"""     )
					self.assertEqual(vocab_keys[-1]  ,     """<mask>"""     )
					self.assertEqual(len(lowerCamelCase__     )  ,     10_04     )







				def 				__a   ( self     :Optional[Any]     ):
					self.assertEqual(self.get_tokenizer().vocab_size  ,     10_05     )







				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :List[str]						=      CamembertTokenizer(lowerCamelCase__     )
					tokenizer.save_pretrained(self.tmpdirname     )
					UpperCamelCase__ :Union[str, Any]						=      CamembertTokenizerFast.from_pretrained(self.tmpdirname     )

					UpperCamelCase__ :Any						=      """I was born in 92000, and this is falsé."""

					UpperCamelCase__ :List[Any]						=      tokenizer.encode(lowerCamelCase__     )
					UpperCamelCase__ :Dict						=      rust_tokenizer.encode(lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )

					UpperCamelCase__ :str						=      tokenizer.encode(lowerCamelCase__  ,     add_special_tokens=lowerCamelCase__     )
					UpperCamelCase__ :Union[str, Any]						=      rust_tokenizer.encode(lowerCamelCase__  ,     add_special_tokens=lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )

					# <unk> tokens are not the same for `rust` than for `slow`.
					# Because spm gives back raw token instead of `unk` in EncodeAsPieces
					# tokens = tokenizer.tokenize(sequence)
					UpperCamelCase__ :Any						=      tokenizer.convert_ids_to_tokens(lowerCamelCase__     )
					UpperCamelCase__ :str						=      rust_tokenizer.tokenize(lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )







				def 				__a   ( self     :Union[str, Any]     ):
					if not self.test_rust_tokenizer:
						return

					UpperCamelCase__ :Dict						=      self.get_tokenizer()
					UpperCamelCase__ :str						=      self.get_rust_tokenizer()

					UpperCamelCase__ :str						=      """I was born in 92000, and this is falsé."""

					UpperCamelCase__ :Optional[Any]						=      tokenizer.tokenize(lowerCamelCase__     )
					UpperCamelCase__ :List[Any]						=      rust_tokenizer.tokenize(lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )

					UpperCamelCase__ :str						=      tokenizer.encode(lowerCamelCase__  ,     add_special_tokens=lowerCamelCase__     )
					UpperCamelCase__ :Union[str, Any]						=      rust_tokenizer.encode(lowerCamelCase__  ,     add_special_tokens=lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )

					UpperCamelCase__ :Tuple						=      self.get_rust_tokenizer()
					UpperCamelCase__ :Union[str, Any]						=      tokenizer.encode(lowerCamelCase__     )
					UpperCamelCase__ :Optional[Any]						=      rust_tokenizer.encode(lowerCamelCase__     )
					self.assertListEqual(lowerCamelCase__  ,     lowerCamelCase__     )







				@slow
				def 				__a   ( self     :List[str]     ):
					# fmt: off
					UpperCamelCase__ :Any						=      {"""input_ids""": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], """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, 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]]}  # noqa: E501
					# fmt: on

					# camembert is a french model. So we also use french texts.
					UpperCamelCase__ :Tuple						=      [
					    """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=lowerCamelCase__  ,     model_name="""camembert-base"""  ,     revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""  ,     sequences=lowerCamelCase__  ,     )







import argparse

import torch

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


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  MobileBertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  MobileBertForPreTraining(__lowercase)
   # Load weights from tf checkpoint
   UpperCamelCase_						=  load_tf_weights_in_mobilebert(__lowercase     ,   __lowercase     ,   __lowercase)
   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						int													=   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."""
 )
 snake_case__   :						Optional[Any]													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





