

import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
    from transformers.models.mobilevitva.modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        make_divisible,
    )


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor
class        _A						(     a__):

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   self.config_class(**self.inputs_dict    )
        self.parent.assertTrue(hasattr(__lowerCAmelCase       ,    'width_multiplier'    )    )

class        _A						:

  def __init__( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=13       ,    _SCREAMING_SNAKE_CASE=64       ,    _SCREAMING_SNAKE_CASE=2       ,    _SCREAMING_SNAKE_CASE=3       ,    _SCREAMING_SNAKE_CASE="swish"       ,    _SCREAMING_SNAKE_CASE=3       ,    _SCREAMING_SNAKE_CASE=32       ,    _SCREAMING_SNAKE_CASE=0.1       ,    _SCREAMING_SNAKE_CASE=0.02       ,    _SCREAMING_SNAKE_CASE=True       ,    _SCREAMING_SNAKE_CASE=True       ,    _SCREAMING_SNAKE_CASE=10       ,    _SCREAMING_SNAKE_CASE=None       ,    _SCREAMING_SNAKE_CASE=0.25       ,    _SCREAMING_SNAKE_CASE=0.0       ,    _SCREAMING_SNAKE_CASE=0.0       ,    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[str]							=   parent
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   batch_size
        SCREAMING_SNAKE_CASE_ :     List[str]							=   image_size
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   patch_size
        SCREAMING_SNAKE_CASE_ :     Tuple							=   num_channels
        SCREAMING_SNAKE_CASE_ :     List[str]							=   make_divisible(512 * width_multiplier       ,    divisor=8    )
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   hidden_act
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   conv_kernel_size
        SCREAMING_SNAKE_CASE_ :     Dict							=   output_stride
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   classifier_dropout_prob
        SCREAMING_SNAKE_CASE_ :     str							=   use_labels
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   is_training
        SCREAMING_SNAKE_CASE_ :     Tuple							=   num_labels
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   initializer_range
        SCREAMING_SNAKE_CASE_ :     Tuple							=   scope
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   width_multiplier
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   ffn_dropout
        SCREAMING_SNAKE_CASE_ :     int							=   attn_dropout

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Any							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        SCREAMING_SNAKE_CASE_ :     Any							=   None
        SCREAMING_SNAKE_CASE_ :     Tuple							=   None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_ :     str							=   ids_tensor([self.batch_size]       ,    self.num_labels    )
              SCREAMING_SNAKE_CASE_ :     int							=   ids_tensor([self.batch_size, self.image_size, self.image_size]       ,    self.num_labels    )

        SCREAMING_SNAKE_CASE_ :     Dict							=   self.get_config()

        return config, pixel_values, labels, pixel_labels

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        return MobileViTVaConfig(
            image_size=self.image_size       ,    patch_size=self.patch_size       ,    num_channels=self.num_channels       ,    hidden_act=self.hidden_act       ,    conv_kernel_size=self.conv_kernel_size       ,    output_stride=self.output_stride       ,    classifier_dropout_prob=self.classifier_dropout_prob       ,    initializer_range=self.initializer_range       ,    width_multiplier=self.width_multiplier       ,    ffn_dropout=self.ffn_dropout_prob       ,    attn_dropout=self.attn_dropout_prob       ,    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   MobileViTVaModel(config=__lowerCAmelCase    )
        model.to(__lowerCAmelCase    )
        model.eval()
        SCREAMING_SNAKE_CASE_ :     str							=   model(__lowerCAmelCase    )
        self.parent.assertEqual(
            result.last_hidden_state.shape       ,    (
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )       ,    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   self.num_labels
        SCREAMING_SNAKE_CASE_ :     Dict							=   MobileViTVaForImageClassification(__lowerCAmelCase    )
        model.to(__lowerCAmelCase    )
        model.eval()
        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   model(__lowerCAmelCase       ,    labels=__lowerCAmelCase    )
        self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     str							=   self.num_labels
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   MobileViTVaForSemanticSegmentation(__lowerCAmelCase    )
        model.to(__lowerCAmelCase    )
        model.eval()
        SCREAMING_SNAKE_CASE_ :     int							=   model(__lowerCAmelCase    )
        self.parent.assertEqual(
            result.logits.shape       ,    (
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )       ,    )
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   model(__lowerCAmelCase       ,    labels=__lowerCAmelCase    )
        self.parent.assertEqual(
            result.logits.shape       ,    (
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )       ,    )



  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_ :     List[str]							=   config_and_inputs
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   {'''pixel_values''': pixel_values}
        return config, inputs_dict

@require_torch
class        _A						(     a__      ,   a__      ,   unittest.TestCase):
  SCREAMING_SNAKE_CASE			:					Any				=     (
      (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
      if is_torch_available()
      else ()
  )

  SCREAMING_SNAKE_CASE			:					Union[str, Any]				=     (
      {
          '''feature-extraction''': MobileViTVaModel,
          '''image-classification''': MobileViTVaForImageClassification,
          '''image-segmentation''': MobileViTVaForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE			:					List[Any]				=     False
  SCREAMING_SNAKE_CASE			:					Optional[int]				=     False
  SCREAMING_SNAKE_CASE			:					Optional[int]				=     False
  SCREAMING_SNAKE_CASE			:					Optional[int]				=     False

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Tuple							=   MobileViTVaModelTester(self    )
        SCREAMING_SNAKE_CASE_ :     Any							=   MobileViTVaConfigTester(self       ,    config_class=__lowerCAmelCase       ,    has_text_modality=__lowerCAmelCase    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        self.config_tester.run_common_tests()

  @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'    )
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'    )
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  @unittest.skip(reason='MobileViTV2 does not output attentions'    )
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  @require_torch_multi_gpu
  @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'    )
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        pass

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[str]							=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_ :     str							=   model_class(__lowerCAmelCase    )
              SCREAMING_SNAKE_CASE_ :     List[Any]							=   inspect.signature(model.forward    )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_ :     str							=   [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_ :     Dict							=   ['''pixel_values''']
              self.assertListEqual(arg_names[:1]       ,    __lowerCAmelCase    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        def check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):
              SCREAMING_SNAKE_CASE_ :     Optional[int]							=   model_class(__lowerCAmelCase    )
              model.to(__lowerCAmelCase    )
              model.eval()

              with torch.no_grad():
                    SCREAMING_SNAKE_CASE_ :     List[str]							=   model(**self._prepare_for_class(__lowerCAmelCase       ,    __lowerCAmelCase    )    )

              SCREAMING_SNAKE_CASE_ :     List[Any]							=   outputs.hidden_states

              SCREAMING_SNAKE_CASE_ :     Optional[int]							=   5
              self.assertEqual(len(__lowerCAmelCase    )       ,    __lowerCAmelCase    )

              # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
              # with the width and height being successively divided by 2.
              SCREAMING_SNAKE_CASE_ :     str							=   2
              for i in range(len(__lowerCAmelCase    )    ):
                    self.assertListEqual(
                        list(hidden_states[i].shape[-2:]    )       ,    [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]       ,    )
                    divisor *= 2

              self.assertEqual(self.model_tester.output_stride       ,    divisor // 2    )

        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_ :     List[str]							=   True
              check_hidden_states_output(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase    )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_ :     Tuple							=   True

              check_hidden_states_output(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Tuple							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[str]							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase    )



  @slow
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_ :     Dict							=   MobileViTVaModel.from_pretrained(__lowerCAmelCase    )
              self.assertIsNotNone(__lowerCAmelCase    )

def       A_     (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Tuple							=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
      return image



@require_torch
@require_vision
class        _A						(     unittest.TestCase):

  @cached_property
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        return (
            MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'    )
            if is_vision_available()
            else None
        )

  @slow
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Dict							=   MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'    ).to(
            __lowerCAmelCase    )

        SCREAMING_SNAKE_CASE_ :     List[Any]							=   self.default_image_processor
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   prepare_img()
        SCREAMING_SNAKE_CASE_ :     int							=   image_processor(images=__lowerCAmelCase       ,    return_tensors='pt'    ).to(__lowerCAmelCase    )

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_ :     List[str]							=   model(**__lowerCAmelCase    )

        # verify the logits
        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   torch.Size((1, 1000)    )
        self.assertEqual(outputs.logits.shape       ,    __lowerCAmelCase    )

        SCREAMING_SNAKE_CASE_ :     int							=   torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]    ).to(__lowerCAmelCase    )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,    __lowerCAmelCase       ,    atol=1e-4    )    )

  @slow
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Dict							=   MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )
        SCREAMING_SNAKE_CASE_ :     int							=   model.to(__lowerCAmelCase    )

        SCREAMING_SNAKE_CASE_ :     Any							=   MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )

        SCREAMING_SNAKE_CASE_ :     str							=   prepare_img()
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   image_processor(images=__lowerCAmelCase       ,    return_tensors='pt'    ).to(__lowerCAmelCase    )

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_ :     str							=   model(**__lowerCAmelCase    )
        SCREAMING_SNAKE_CASE_ :     Tuple							=   outputs.logits

        # verify the logits
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   torch.Size((1, 21, 32, 32)    )
        self.assertEqual(logits.shape       ,    __lowerCAmelCase    )

        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   torch.tensor(
            [
                [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
            ]       ,    device=__lowerCAmelCase       ,    )

        self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,    __lowerCAmelCase       ,    atol=1e-4    )    )



  @slow
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )
        SCREAMING_SNAKE_CASE_ :     List[str]							=   model.to(__lowerCAmelCase    )

        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'    )

        SCREAMING_SNAKE_CASE_ :     int							=   prepare_img()
        SCREAMING_SNAKE_CASE_ :     List[str]							=   image_processor(images=__lowerCAmelCase       ,    return_tensors='pt'    ).to(__lowerCAmelCase    )

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   model(**__lowerCAmelCase    )

        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   outputs.logits.detach().cpu()

        SCREAMING_SNAKE_CASE_ :     Any							=   image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase       ,    target_sizes=[(50, 60)]    )
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   torch.Size((50, 60)    )
        self.assertEqual(segmentation[0].shape       ,    __lowerCAmelCase    )

        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase    )
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   torch.Size((32, 32)    )
        self.assertEqual(segmentation[0].shape       ,    __lowerCAmelCase    )







'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  __a: Tuple          =   None


__a: Tuple          =   logging.get_logger(__name__)

__a: Optional[Any]          =   {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__a: Optional[Any]          =   {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__a: Tuple          =   {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE							=    PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE							=    ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE							=    TaTokenizer

       SCREAMING_SNAKE_CASE							=    []

       def __init__(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase="</s>"	,   __lowerCAmelCase="<unk>"	,   __lowerCAmelCase="<pad>"	,   __lowerCAmelCase=100	,   __lowerCAmelCase=None	,   **__lowerCAmelCase	,   )  ->  Union[str, Any]:
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
         lowercase__				:    Union[str, Any]  =					[F"""<extra_id_{i}>""" for i in range(__lowerCAmelCase )]
        elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra special tokens
         lowercase__				:    Dict  =					len(set(filter(lambda __lowerCAmelCase : bool('''extra_id_''' in str(__lowerCAmelCase ) )	,   __lowerCAmelCase ) ) )
         if extra_tokens != extra_ids:
          raise ValueError(
              F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
              ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
              ''' tokens''' )

        super().__init__(
            __lowerCAmelCase	,   tokenizer_file=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   extra_ids=__lowerCAmelCase	,   additional_special_tokens=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

        lowercase__				:    Union[str, Any]  =					vocab_file
        lowercase__				:    Optional[int]  =					False if not self.vocab_file else True
        lowercase__				:    Any  =					extra_ids

       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Union[str, Any]:
        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         lowercase__				:    Any  =					TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
         if init_max_model_length is not None and init_max_model_length != max_model_length:
          return init_max_model_length
         elif init_max_model_length is None:
          warnings.warn(
              '''This tokenizer was incorrectly instantiated with a model max length of'''
              F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
              ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
              ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
              F""" {pretrained_model_name_or_path} automatically truncating your input to"""
              F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
              F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
              ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
              ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''	,   __lowerCAmelCase	,   )

        return max_model_length

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  Tuple[str]:
        if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.''' )

        if not os.path.isdir(__lowerCAmelCase ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
         return
        lowercase__				:    List[Any]  =					os.path.join(
            __lowerCAmelCase	,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

        if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ):
         copyfile(self.vocab_file	,   __lowerCAmelCase )
         logger.info(F"""Copy vocab file to {out_vocab_file}""" )

        return (out_vocab_file,)

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    Any  =					token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
        else:
         lowercase__				:    Dict  =					token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    Optional[int]  =					[self.eos_token_id]

        if token_ids_a is None:
         return len(token_ids_a + eos ) * [0]
        return len(token_ids_a + eos + token_ids_a + eos ) * [0]

       def _lowerCAmelCase(						self )  ->  List[Any]:
        return list(
            set(filter(lambda __lowerCAmelCase : bool(re.search(r'''<extra_id_\d+>'''	,   __lowerCAmelCase ) ) is not None	,   self.additional_special_tokens ) ) )


       def _lowerCAmelCase(						self )  ->  Tuple:
        return [self.convert_tokens_to_ids(__lowerCAmelCase ) for token in self.get_sentinel_tokens()]

