





import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
    import numpy as np
    import torch

    from . import (
        AlbertForPreTraining,
        BartForConditionalGeneration,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        CamembertForMaskedLM,
        CTRLLMHeadModel,
        DistilBertForMaskedLM,
        DistilBertForQuestionAnswering,
        DPRContextEncoder,
        DPRQuestionEncoder,
        DPRReader,
        ElectraForPreTraining,
        FlaubertWithLMHeadModel,
        GPTaLMHeadModel,
        LayoutLMForMaskedLM,
        LxmertForPreTraining,
        LxmertVisualFeatureEncoder,
        OpenAIGPTLMHeadModel,
        RobertaForMaskedLM,
        RobertaForSequenceClassification,
        TaForConditionalGeneration,
        TransfoXLLMHeadModel,
        XLMRobertaForMaskedLM,
        XLMWithLMHeadModel,
        XLNetLMHeadModel,
    )


logging.set_verbosity_info()

__lowerCamelCase	:					Tuple			  =					{
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=False							,       _lowerCAmelCase=True					)						->			Union[str, Any]:
 if model_type not in MODEL_CLASSES:
  raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()					)}."""					)

 UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : str	     =       MODEL_CLASSES[model_type]

 # Initialise TF model
 if config_file in aws_config_map:
  UpperCamelCase     : Union[str, Any]	     =       cached_file(_lowerCAmelCase							,       _lowerCAmelCase							,       force_download=not use_cached_models					)
 UpperCamelCase     : Union[str, Any]	     =       config_class.from_json_file(_lowerCAmelCase					)
 UpperCamelCase     : List[Any]	     =       True
 UpperCamelCase     : Optional[Any]	     =       True
 print(F"""Building TensorFlow model from configuration: {config}"""					)
 UpperCamelCase     : Tuple	     =       model_class(_lowerCAmelCase					)

 # Load weights from tf checkpoint
 if pytorch_checkpoint_path in aws_config_map.keys():
  UpperCamelCase     : int	     =       cached_file(
      _lowerCAmelCase							,       _lowerCAmelCase							,       force_download=not use_cached_models					)
 # Load PyTorch checkpoint in tf2 model:
 UpperCamelCase     : Union[str, Any]	     =       load_pytorch_checkpoint_in_tfa_model(_lowerCAmelCase							,       _lowerCAmelCase					)

 if compare_with_pt_model:
  UpperCamelCase     : Optional[int]	     =       tf_model(tf_model.dummy_inputs							,       training=_lowerCAmelCase					)  # build the network

  UpperCamelCase     : Optional[int]	     =       torch.load(_lowerCAmelCase							,       map_location="cpu"					)
  UpperCamelCase     : List[Any]	     =       pt_model_class.from_pretrained(
      pretrained_model_name_or_path=_lowerCAmelCase							,       config=_lowerCAmelCase							,       state_dict=_lowerCAmelCase					)

  with torch.no_grad():
   UpperCamelCase     : Any	     =       pt_model(**pt_model.dummy_inputs					)

  UpperCamelCase     : Optional[int]	     =       pto[0].numpy()
  UpperCamelCase     : Optional[Any]	     =       tfo[0].numpy()
  UpperCamelCase     : Optional[Any]	     =       np.amax(np.abs(np_pt - np_tf					)					)
  print(F"""Max absolute difference between models outputs {diff}"""					)
  assert diff <= 2e-2, F"""Error, model absolute difference is >2e-2: {diff}"""

 # Save pytorch-model
 print(F"""Save TensorFlow model to {tf_dump_path}"""					)
 tf_model.save_weights(_lowerCAmelCase							,       save_format="h5"					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       _lowerCAmelCase=False							,       _lowerCAmelCase=False							,       _lowerCAmelCase=False							,       _lowerCAmelCase=False							,       )						->			List[Any]:
 if args_model_type is None:
  UpperCamelCase     : Optional[Any]	     =       list(MODEL_CLASSES.keys()					)
 else:
  UpperCamelCase     : str	     =       [args_model_type]

 for j, model_type in enumerate(_lowerCAmelCase							,       start=1					):
  print("=" * 100					)
  print(F""" Converting model type {j}/{len(_lowerCAmelCase					)}: {model_type}"""					)
  print("=" * 100					)
  if model_type not in MODEL_CLASSES:
   raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()					)}."""					)

  UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : int	     =       MODEL_CLASSES[model_type]

  if model_shortcut_names_or_path is None:
   UpperCamelCase     : Union[str, Any]	     =       list(aws_model_maps.keys()					)
  if config_shortcut_names_or_path is None:
   UpperCamelCase     : Optional[int]	     =       model_shortcut_names_or_path

  for i, (model_shortcut_name, config_shortcut_name) in enumerate(
      zip(_lowerCAmelCase							,       _lowerCAmelCase					)							,       start=1					):
   print("-" * 100					)
   if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
    if not only_convert_finetuned_models:
     print(F"""    Skipping finetuned checkpoint {model_shortcut_name}"""					)
     continue
    UpperCamelCase     : Optional[Any]	     =       model_shortcut_name
   elif only_convert_finetuned_models:
    print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""					)
    continue
   print(
       F"""    Converting checkpoint {i}/{len(_lowerCAmelCase					)}: {model_shortcut_name} - model_type {model_type}"""					)
   print("-" * 100					)

   if config_shortcut_name in aws_config_map:
    UpperCamelCase     : str	     =       cached_file(_lowerCAmelCase							,       _lowerCAmelCase							,       force_download=not use_cached_models					)
   else:
    UpperCamelCase     : Union[str, Any]	     =       config_shortcut_name

   if model_shortcut_name in aws_model_maps:
    UpperCamelCase     : Union[str, Any]	     =       cached_file(_lowerCAmelCase							,       _lowerCAmelCase							,       force_download=not use_cached_models					)
   else:
    UpperCamelCase     : List[str]	     =       model_shortcut_name

   if os.path.isfile(_lowerCAmelCase					):
    UpperCamelCase     : Optional[Any]	     =       "converted_model"

   convert_pt_checkpoint_to_tf(
       model_type=_lowerCAmelCase							,       pytorch_checkpoint_path=_lowerCAmelCase							,       config_file=_lowerCAmelCase							,       tf_dump_path=os.path.join(_lowerCAmelCase							,       model_shortcut_name + "-tf_model.h5"					)							,       compare_with_pt_model=_lowerCAmelCase							,       )
   if remove_cached_files:
    os.remove(_lowerCAmelCase					)
    os.remove(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Union[str, Any]			  =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
    )
    parser.add_argument(
        """--model_type""",
        default=None,
        type=str,
        help=(
            f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
            """convert all the models from AWS."""
        ),
    )
    parser.add_argument(
        """--pytorch_checkpoint_path""",
        default=None,
        type=str,
        help=(
            """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
            """If not given, will download and convert all the checkpoints from AWS."""
        ),
    )
    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        help=(
            """The config json file corresponding to the pre-trained model. \n"""
            """This specifies the model architecture. If not given and """
            """--pytorch_checkpoint_path is not given or is a shortcut name """
            """use the configuration associated to the shortcut name on the AWS"""
        ),
    )
    parser.add_argument(
        """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
    )
    parser.add_argument(
        """--use_cached_models""",
        action="""store_true""",
        help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
    )
    parser.add_argument(
        """--remove_cached_files""",
        action="""store_true""",
        help="""Remove pytorch models after conversion (save memory when converting in batches).""",
    )
    parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
    __lowerCamelCase	:					int			  =					parser.parse_args()

    # if args.pytorch_checkpoint_path is not None:
    #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
    #                                 args.pytorch_checkpoint_path,
    #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
    #                                 args.tf_dump_path,
    #                                 compare_with_pt_model=args.compare_with_pt_model,
    #                                 use_cached_models=args.use_cached_models)
    # else:
    convert_all_pt_checkpoints_to_tf(
        args.model_type.lower() if args.model_type is not None else None,
        args.tf_dump_path,
        model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
        if args.pytorch_checkpoint_path is not None
        else None,
        config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
        compare_with_pt_model=args.compare_with_pt_model,
        use_cached_models=args.use_cached_models,
        remove_cached_files=args.remove_cached_files,
        only_convert_finetuned_models=args.only_convert_finetuned_models,
    )







import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase	:					Dict			  =					get_tests_dir("""fixtures/spiece.model""")



@require_sentencepiece
@require_tokenizers
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :int						   =       AlbertTokenizer
      _UpperCAmelCase     :int						   =       AlbertTokenizerFast
      _UpperCAmelCase     :int						   =       True
      _UpperCAmelCase     :List[str]						   =       True
      _UpperCAmelCase     :Optional[Any]						   =       True





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase     : List[str]	     =       AlbertTokenizer(A_       )
       tokenizer.save_pretrained(self.tmpdirname       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       "this is a test"
       UpperCamelCase     : List[Any]	     =       "this is a test"
       return input_text, output_text





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       "<pad>"
       UpperCamelCase     : Dict	     =       0

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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       list(self.get_tokenizer().get_vocab().keys()       )

       self.assertEqual(vocab_keys[0]	,							"<pad>"       )
       self.assertEqual(vocab_keys[1]	,							"<unk>"       )
       self.assertEqual(vocab_keys[-1]	,							"▁eloquent"       )
       self.assertEqual(len(A_       )	,							3_0000       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.assertEqual(self.get_tokenizer().vocab_size	,							3_0000       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       if not self.test_rust_tokenizer:
        return

       UpperCamelCase     : Any	     =       self.get_tokenizer()
       UpperCamelCase     : Optional[int]	     =       self.get_rust_tokenizer()

       UpperCamelCase     : List[Any]	     =       "I was born in 92000, and this is falsé."

       UpperCamelCase     : Optional[Any]	     =       tokenizer.tokenize(A_       )
       UpperCamelCase     : Optional[int]	     =       rust_tokenizer.tokenize(A_       )
       self.assertListEqual(A_	,							A_       )

       UpperCamelCase     : Optional[Any]	     =       tokenizer.encode(A_	,							add_special_tokens=A_       )
       UpperCamelCase     : Optional[int]	     =       rust_tokenizer.encode(A_	,							add_special_tokens=A_       )
       self.assertListEqual(A_	,							A_       )

       UpperCamelCase     : List[Any]	     =       self.get_rust_tokenizer()
       UpperCamelCase     : Union[str, Any]	     =       tokenizer.encode(A_       )
       UpperCamelCase     : Optional[int]	     =       rust_tokenizer.encode(A_       )
       self.assertListEqual(A_	,							A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       AlbertTokenizer(A_	,							keep_accents=A_       )

       UpperCamelCase     : Optional[int]	     =       tokenizer.tokenize("This is a test"       )
       self.assertListEqual(A_	,							["▁this", "▁is", "▁a", "▁test"]       )

       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_       )	,							[48, 25, 21, 1289]       )

       UpperCamelCase     : Optional[Any]	     =       tokenizer.tokenize("I was born in 92000, and this is falsé."       )
       self.assertListEqual(
           A_	,							["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]       )
       UpperCamelCase     : Optional[int]	     =       tokenizer.convert_tokens_to_ids(A_       )
       self.assertListEqual(A_	,							[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]       )

       UpperCamelCase     : List[str]	     =       tokenizer.convert_ids_to_tokens(A_       )
       self.assertListEqual(
           A_	,							["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       AlbertTokenizer(A_       )

       UpperCamelCase     : Dict	     =       tokenizer.encode("sequence builders"       )
       UpperCamelCase     : Tuple	     =       tokenizer.encode("multi-sequence build"       )

       UpperCamelCase     : str	     =       tokenizer.build_inputs_with_special_tokens(A_       )
       UpperCamelCase     : int	     =       tokenizer.build_inputs_with_special_tokens(A_	,							A_       )

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       {"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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0]], "input_ids": [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 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, 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, 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, 0, 0, 0, 0, 0]], "token_type_ids": [[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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_	,							model_name="albert-base-v2"	,							revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"	,							)
