'''simple docstring'''

import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    None
       SCREAMING_SNAKE_CASE							=    None

       @property
       def _lowerCAmelCase(						self )  ->  Any:
        return self.feat_extract_tester.prepare_feat_extract_dict()

       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    Optional[int]  =					self.feature_extraction_class(**self.feat_extract_dict )
        self.assertTrue(hasattr(__lowerCAmelCase	,   '''feature_size''' ) )
        self.assertTrue(hasattr(__lowerCAmelCase	,   '''sampling_rate''' ) )
        self.assertTrue(hasattr(__lowerCAmelCase	,   '''padding_value''' ) )

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Dict  =					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase__				:    Any  =					self.feature_extraction_class(**self.feat_extract_dict )
        lowercase__				:    Optional[Any]  =					feat_extract.model_input_names[0]

        lowercase__				:    List[str]  =					BatchFeature({input_name: speech_inputs} )

        self.assertTrue(all(len(__lowerCAmelCase ) == len(__lowerCAmelCase ) for x, y in zip(__lowerCAmelCase	,   processed_features[input_name] ) ) )

        lowercase__				:    int  =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					BatchFeature({input_name: speech_inputs}	,   tensor_type='''np''' )

        lowercase__				:    Dict  =					processed_features[input_name]

        if len(batch_features_input.shape ) < 3:
         lowercase__				:    int  =					batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )

       @require_torch
       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[Any]  =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase )
        lowercase__				:    int  =					self.feature_extraction_class(**self.feat_extract_dict )
        lowercase__				:    str  =					feat_extract.model_input_names[0]

        lowercase__				:    Dict  =					BatchFeature({input_name: speech_inputs}	,   tensor_type='''pt''' )

        lowercase__				:    Dict  =					processed_features[input_name]

        if len(batch_features_input.shape ) < 3:
         lowercase__				:    List[str]  =					batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )

       @require_tf
       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Tuple  =					self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase )
        lowercase__				:    int  =					self.feature_extraction_class(**self.feat_extract_dict )
        lowercase__				:    str  =					feat_extract.model_input_names[0]

        lowercase__				:    List[str]  =					BatchFeature({input_name: speech_inputs}	,   tensor_type='''tf''' )

        lowercase__				:    Optional[Any]  =					processed_features[input_name]

        if len(batch_features_input.shape ) < 3:
         lowercase__				:    Optional[int]  =					batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase=False )  ->  List[Any]:
        def _inputs_have_equal_length(__lowerCAmelCase ):
         lowercase__				:    Dict  =					len(input[0] )
         for input_slice in input[1:]:
          if len(__lowerCAmelCase ) != length:
           return False
         return True

        def _inputs_are_equal(__lowerCAmelCase	,   __lowerCAmelCase ):
         if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          return False

         for input_slice_a, input_slice_a in zip(__lowerCAmelCase	,   __lowerCAmelCase ):
          if not np.allclose(np.asarray(__lowerCAmelCase )	,   np.asarray(__lowerCAmelCase )	,   atol=1E-3 ):
           return False
         return True

        lowercase__				:    Optional[int]  =					self.feature_extraction_class(**self.feat_extract_dict )
        lowercase__				:    Optional[int]  =					self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					feat_extract.model_input_names[0]

        lowercase__				:    Any  =					BatchFeature({input_name: speech_inputs} )

        lowercase__				:    Dict  =					self.feat_extract_tester.seq_length_diff
        lowercase__				:    str  =					self.feat_extract_tester.max_seq_length + pad_diff
        lowercase__				:    Optional[Any]  =					self.feat_extract_tester.min_seq_length
        lowercase__				:    int  =					self.feat_extract_tester.batch_size
        lowercase__				:    int  =					self.feat_extract_tester.feature_size

        # test padding for List[int] + numpy
        lowercase__				:    str  =					feat_extract.pad(__lowerCAmelCase	,   padding=__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					input_a[input_name]

        lowercase__				:    Dict  =					feat_extract.pad(__lowerCAmelCase	,   padding='''longest''' )
        lowercase__				:    Dict  =					input_a[input_name]

        lowercase__				:    Optional[Any]  =					feat_extract.pad(__lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[-1] ) )
        lowercase__				:    Optional[int]  =					input_a[input_name]

        lowercase__				:    List[str]  =					feat_extract.pad(__lowerCAmelCase	,   padding='''longest'''	,   return_tensors='''np''' )
        lowercase__				:    Union[str, Any]  =					input_a[input_name]

        # max_length parameter has to be provided when setting `padding="max_length"`
        with self.assertRaises(__lowerCAmelCase ):
         feat_extract.pad(__lowerCAmelCase	,   padding='''max_length''' )[input_name]

        lowercase__				:    List[str]  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=__lowerCAmelCase	,   return_tensors='''np''' )
        lowercase__				:    Any  =					input_a[input_name]

        self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase ) )
        self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase ) )
        self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase ) )
        self.assertTrue(_inputs_are_equal(__lowerCAmelCase	,   __lowerCAmelCase ) )
        self.assertTrue(len(input_a[0] ) == pad_min_length )
        self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
        self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
        self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )

        if feature_size > 1:
         self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )

        # test padding for `pad_to_multiple_of` for List[int] + numpy
        lowercase__				:    Dict  =					feat_extract.pad(__lowerCAmelCase	,   pad_to_multiple_of=10 )
        lowercase__				:    str  =					input_a[input_name]

        lowercase__				:    List[str]  =					feat_extract.pad(__lowerCAmelCase	,   padding='''longest'''	,   pad_to_multiple_of=10 )
        lowercase__				:    List[Any]  =					input_a[input_name]

        lowercase__				:    Optional[Any]  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   pad_to_multiple_of=10	,   max_length=__lowerCAmelCase )
        lowercase__				:    int  =					input_a[input_name]

        lowercase__				:    int  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   pad_to_multiple_of=10	,   max_length=__lowerCAmelCase	,   return_tensors='''np'''	,   )
        lowercase__				:    str  =					input_a[input_name]

        self.assertTrue(all(len(__lowerCAmelCase ) % 10 == 0 for x in input_a ) )
        self.assertTrue(_inputs_are_equal(__lowerCAmelCase	,   __lowerCAmelCase ) )

        lowercase__				:    int  =					pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
        self.assertTrue(all(len(__lowerCAmelCase ) == expected_mult_pad_length for x in input_a ) )
        self.assertEqual(input_a.shape[:2]	,   (batch_size, expected_mult_pad_length) )

        if feature_size > 1:
         self.assertTrue(input_a.shape[2] == feature_size )

        # Check padding value is correct
        lowercase__				:    Union[str, Any]  =					(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
        self.assertTrue(
            abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
            < 1E-3 )
        self.assertTrue(
            abs(
                np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
            < 1E-3 )
        self.assertTrue(
            abs(
                np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
                - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
            < 1E-3 )
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
        self.assertTrue(
            abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
            < 1E-3 )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase=False )  ->  Union[str, Any]:
        def _inputs_have_equal_length(__lowerCAmelCase ):
         lowercase__				:    Any  =					len(input[0] )
         for input_slice in input[1:]:
          if len(__lowerCAmelCase ) != length:
           return False
         return True

        def _inputs_are_equal(__lowerCAmelCase	,   __lowerCAmelCase ):
         if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          return False

         for input_slice_a, input_slice_a in zip(__lowerCAmelCase	,   __lowerCAmelCase ):
          if not np.allclose(np.asarray(__lowerCAmelCase )	,   np.asarray(__lowerCAmelCase )	,   atol=1E-3 ):
           return False
         return True

        lowercase__				:    List[str]  =					self.feature_extraction_class(**self.feat_extract_dict )
        lowercase__				:    Union[str, Any]  =					self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					feat_extract.model_input_names[0]

        lowercase__				:    int  =					BatchFeature({input_name: speech_inputs} )

        # truncate to smallest
        lowercase__				:    Dict  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[0] )	,   truncation=__lowerCAmelCase )
        lowercase__				:    str  =					input_a[input_name]

        lowercase__				:    Optional[Any]  =					feat_extract.pad(__lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[0] ) )
        lowercase__				:    Tuple  =					input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase ) )
        self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase ) )

        # truncate to smallest with np
        lowercase__				:    Optional[Any]  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[0] )	,   return_tensors='''np'''	,   truncation=__lowerCAmelCase	,   )
        lowercase__				:    Optional[Any]  =					input_a[input_name]

        lowercase__				:    Tuple  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[0] )	,   return_tensors='''np''' )
        lowercase__				:    Optional[Any]  =					input_a[input_name]

        self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase ) )
        self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase ) )

        # truncate to middle
        lowercase__				:    List[Any]  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[1] )	,   truncation=__lowerCAmelCase	,   return_tensors='''np'''	,   )
        lowercase__				:    Tuple  =					input_a[input_name]

        lowercase__				:    Optional[Any]  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[1] )	,   truncation=__lowerCAmelCase )
        lowercase__				:    Any  =					input_a[input_name]

        lowercase__				:    str  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[1] )	,   return_tensors='''np''' )
        lowercase__				:    Dict  =					input_a[input_name]

        self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
        self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase ) )
        self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase ) )
        self.assertTrue(_inputs_are_equal(__lowerCAmelCase	,   __lowerCAmelCase ) )

        # since truncation forces padding to be smaller than longest input
        # function can't return `np.ndarray`, but has to return list
        self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase ) )
        self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(__lowerCAmelCase ):
         feat_extract.pad(__lowerCAmelCase	,   truncation=__lowerCAmelCase )[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(__lowerCAmelCase ):
         feat_extract.pad(__lowerCAmelCase	,   padding='''longest'''	,   truncation=__lowerCAmelCase )[input_name]

        # padding has to be max_length when setting `truncation=True`
        with self.assertRaises(__lowerCAmelCase ):
         feat_extract.pad(__lowerCAmelCase	,   padding='''longest'''	,   truncation=__lowerCAmelCase )[input_name]

        # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
        with self.assertRaises(__lowerCAmelCase ):
         feat_extract.pad(__lowerCAmelCase	,   padding='''max_length'''	,   truncation=__lowerCAmelCase )[input_name]

        # test truncation for `pad_to_multiple_of` for List[int] + numpy
        lowercase__				:    Union[str, Any]  =					12
        lowercase__				:    Tuple  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[0] )	,   pad_to_multiple_of=__lowerCAmelCase	,   truncation=__lowerCAmelCase	,   )
        lowercase__				:    Optional[int]  =					input_a[input_name]

        lowercase__				:    str  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=len(speech_inputs[0] )	,   pad_to_multiple_of=__lowerCAmelCase	,   )
        lowercase__				:    int  =					input_a[input_name]

        # retrieve expected_length as multiple of pad_to_multiple_of
        lowercase__				:    List[str]  =					len(speech_inputs[0] )
        if expected_length % pad_to_multiple_of != 0:
         lowercase__				:    List[Any]  =					((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

        self.assertTrue(len(input_a[0] ) == expected_length )
        self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase ) )
        self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self )  ->  List[str]:
        self._check_padding(numpify=__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  int:
        self._check_padding(numpify=__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        self._check_truncation(numpify=__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        self._check_truncation(numpify=__lowerCAmelCase )

       @require_torch
       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Any  =					self.feature_extraction_class(**self.feat_extract_dict )
        lowercase__				:    Any  =					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase__				:    str  =					feat_extract.model_input_names[0]

        lowercase__				:    Any  =					BatchFeature({input_name: speech_inputs} )

        lowercase__				:    str  =					feat_extract.pad(__lowerCAmelCase	,   padding='''longest'''	,   return_tensors='''np''' )[input_name]
        lowercase__				:    Optional[Any]  =					feat_extract.pad(__lowerCAmelCase	,   padding='''longest'''	,   return_tensors='''pt''' )[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )

       @require_tf
       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    List[str]  =					self.feature_extraction_class(**self.feat_extract_dict )
        lowercase__				:    Tuple  =					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase__				:    Union[str, Any]  =					feat_extract.model_input_names[0]

        lowercase__				:    List[str]  =					BatchFeature({input_name: speech_inputs} )

        lowercase__				:    Dict  =					feat_extract.pad(__lowerCAmelCase	,   padding='''longest'''	,   return_tensors='''np''' )[input_name]
        lowercase__				:    Union[str, Any]  =					feat_extract.pad(__lowerCAmelCase	,   padding='''longest'''	,   return_tensors='''tf''' )[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )

       def _lowerCAmelCase(						self )  ->  int:
        lowercase__				:    int  =					self.feat_extract_dict
        lowercase__				:    Optional[int]  =					True
        lowercase__				:    Dict  =					self.feature_extraction_class(**__lowerCAmelCase )
        lowercase__				:    int  =					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase__				:    Any  =					[len(__lowerCAmelCase ) for x in speech_inputs]
        lowercase__				:    Dict  =					feat_extract.model_input_names[0]

        lowercase__				:    int  =					BatchFeature({input_name: speech_inputs} )

        lowercase__				:    Optional[Any]  =					feat_extract.pad(__lowerCAmelCase	,   padding='''longest'''	,   return_tensors='''np''' )
        self.assertIn('''attention_mask'''	,   __lowerCAmelCase )
        self.assertListEqual(list(processed.attention_mask.shape )	,   list(processed[input_name].shape[:2] ) )
        self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()	,   __lowerCAmelCase )


       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Dict  =					self.feat_extract_dict
        lowercase__				:    Any  =					True
        lowercase__				:    str  =					self.feature_extraction_class(**__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					self.feat_extract_tester.prepare_inputs_for_common()
        lowercase__				:    str  =					[len(__lowerCAmelCase ) for x in speech_inputs]
        lowercase__				:    Tuple  =					feat_extract.model_input_names[0]

        lowercase__				:    List[Any]  =					BatchFeature({input_name: speech_inputs} )
        lowercase__				:    Optional[int]  =					min(__lowerCAmelCase )

        lowercase__				:    List[Any]  =					feat_extract.pad(
            __lowerCAmelCase	,   padding='''max_length'''	,   max_length=__lowerCAmelCase	,   truncation=__lowerCAmelCase	,   return_tensors='''np''' )
        self.assertIn('''attention_mask'''	,   __lowerCAmelCase )
        self.assertListEqual(
            list(processed_pad.attention_mask.shape )	,   [processed_pad[input_name].shape[0], max_length] )
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()	,   [max_length for x in speech_inputs] )


'''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()]

