
'''simple docstring'''

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()

snake_case									=    {
    """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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=False      ,			lowerCamelCase_=True			):







 """simple docstring"""


 if model_type not in MODEL_CLASSES:
  raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()			)}.'''			)

 lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__     :   Union[str, Any]     		=	MODEL_CLASSES[model_type]

 # Initialise TF model
 if config_file in aws_config_map:
  lowerCAmelCase__     :   int     		=	cached_file(lowerCamelCase_      ,			lowerCamelCase_      ,			force_download=not use_cached_models			)
 lowerCAmelCase__     :   int     		=	config_class.from_json_file(lowerCamelCase_			)
 lowerCAmelCase__     :   List[Any]     		=	True
 lowerCAmelCase__     :   Union[str, Any]     		=	True
 print(f'''Building TensorFlow model from configuration: {config}'''			)
 lowerCAmelCase__     :   Optional[int]     		=	model_class(lowerCamelCase_			)

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

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

  lowerCAmelCase__     :   Union[str, Any]     		=	torch.load(lowerCamelCase_      ,			map_location="cpu"			)
  lowerCAmelCase__     :   Any     		=	pt_model_class.from_pretrained(
      pretrained_model_name_or_path=lowerCamelCase_      ,			config=lowerCamelCase_      ,			state_dict=lowerCamelCase_			)

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

  lowerCAmelCase__     :   Any     		=	pto[0].numpy()
  lowerCAmelCase__     :   str     		=	tfo[0].numpy()
  lowerCAmelCase__     :   List[str]     		=	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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=None      ,			lowerCamelCase_=None      ,			lowerCamelCase_=False      ,			lowerCamelCase_=False      ,			lowerCamelCase_=False      ,			lowerCamelCase_=False      ,			):







 """simple docstring"""


 if args_model_type is None:
  lowerCAmelCase__     :   Optional[Any]     		=	list(MODEL_CLASSES.keys()			)
 else:
  lowerCAmelCase__     :   Union[str, Any]     		=	[args_model_type]

 for j, model_type in enumerate(lowerCamelCase_      ,			start=1			):
  print("=" * 1_0_0			)
  print(f''' Converting model type {j}/{len(lowerCamelCase_			)}: {model_type}'''			)
  print("=" * 1_0_0			)
  if model_type not in MODEL_CLASSES:
   raise ValueError(f'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()			)}.'''			)

  lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	MODEL_CLASSES[model_type]

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

  for i, (model_shortcut_name, config_shortcut_name) in enumerate(
      zip(lowerCamelCase_      ,			lowerCamelCase_			)      ,			start=1			):
   print("-" * 1_0_0			)
   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
    lowerCAmelCase__     :   List[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("-" * 1_0_0			)

   if config_shortcut_name in aws_config_map:
    lowerCAmelCase__     :   Tuple     		=	cached_file(lowerCamelCase_      ,			lowerCamelCase_      ,			force_download=not use_cached_models			)
   else:
    lowerCAmelCase__     :   int     		=	config_shortcut_name

   if model_shortcut_name in aws_model_maps:
    lowerCAmelCase__     :   List[str]     		=	cached_file(lowerCamelCase_      ,			lowerCamelCase_      ,			force_download=not use_cached_models			)
   else:
    lowerCAmelCase__     :   int     		=	model_shortcut_name

   if os.path.isfile(lowerCamelCase_			):
    lowerCAmelCase__     :   Dict     		=	"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__":
     snake_case									=    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.""")
     snake_case									=    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,
     )







'''simple docstring'''

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


snake_case									=    get_tests_dir("""fixtures/spiece.model""")




@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase       (     UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				List[str]	=			AlbertTokenizer
 A_				:				List[Any]	=			AlbertTokenizerFast
 A_				:				List[str]	=			True
 A_				:				Any	=			True
 A_				:				List[Any]	=			True


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  super().setUp()

  # We have a SentencePiece fixture for testing
  lowerCAmelCase__     :   int     		=	AlbertTokenizer(a__    )
  tokenizer.save_pretrained(self.tmpdirname    )


 def    _A	(    self  : Tuple			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	"this is a test"
  lowerCAmelCase__     :   Optional[Any]     		=	"this is a test"
  return input_text, output_text


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	"<pad>"
  lowerCAmelCase__     :   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    _A	(    self  : Tuple    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	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    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

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


 def    _A	(    self  : int    ):
  '''simple docstring'''

  if not self.test_rust_tokenizer:
   return

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

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

  lowerCAmelCase__     :   Dict     		=	tokenizer.tokenize(a__    )
  lowerCAmelCase__     :   str     		=	rust_tokenizer.tokenize(a__    )
  self.assertListEqual(a__			,							a__    )

  lowerCAmelCase__     :   List[Any]     		=	tokenizer.encode(a__			,							add_special_tokens=a__    )
  lowerCAmelCase__     :   Tuple     		=	rust_tokenizer.encode(a__			,							add_special_tokens=a__    )
  self.assertListEqual(a__			,							a__    )

  lowerCAmelCase__     :   Optional[int]     		=	self.get_rust_tokenizer()
  lowerCAmelCase__     :   str     		=	tokenizer.encode(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	rust_tokenizer.encode(a__    )
  self.assertListEqual(a__			,							a__    )


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	AlbertTokenizer(a__			,							keep_accents=a__    )

  lowerCAmelCase__     :   Optional[Any]     		=	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]    )

  lowerCAmelCase__     :   Optional[int]     		=	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", "."]    )
  lowerCAmelCase__     :   Tuple     		=	tokenizer.convert_tokens_to_ids(a__    )
  self.assertListEqual(a__			,							[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

  lowerCAmelCase__     :   Optional[int]     		=	tokenizer.convert_ids_to_tokens(a__    )
  self.assertListEqual(
      a__			,							["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]			,							)


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	AlbertTokenizer(a__    )

  lowerCAmelCase__     :   List[str]     		=	tokenizer.encode("sequence builders"    )
  lowerCAmelCase__     :   Optional[int]     		=	tokenizer.encode("multi-sequence build"    )

  lowerCAmelCase__     :   Tuple     		=	tokenizer.build_inputs_with_special_tokens(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	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    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   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"			,							)





