


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:
       __lowerCamelCase        =     None


__lowerCamelCase        =     logging.get_logger(__name__)

__lowerCamelCase        =     {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase        =     {
    '''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
__lowerCamelCase        =     {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[str]							    =     VOCAB_FILES_NAMES
       lowerCamelCase__:     Any							    =     PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase__:     Any							    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase__:     Tuple							    =     ["""input_ids""", """attention_mask"""]
       lowerCamelCase__:     List[str]							    =     TaTokenizer

       lowerCamelCase__:     List[int]							    =     []

       def __init__(   self      :     str      ,							lowerCamelCase_      :     Optional[Any]=None      ,							lowerCamelCase_      :     Tuple=None      ,							lowerCamelCase_      :     List[Any]="</s>"      ,							lowerCamelCase_      :     Union[str, Any]="<unk>"      ,							lowerCamelCase_      :     List[str]="<pad>"      ,							lowerCamelCase_      :     Optional[Any]=1_0_0      ,							lowerCamelCase_      :     str=None      ,							**lowerCamelCase_      :     Union[str, Any]      ,							):
            # Add extra_ids to the special token list
            if extra_ids > 0 and additional_special_tokens is None:
                 a_    :     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
                 a_    :     Union[str, Any]										=    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_      ,							)

            a_    :     str										=    vocab_file
            a_    :     Optional[Any]										=    False if not self.vocab_file else True
            a_    :     Tuple										=    extra_ids

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int    ):
            if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                 a_    :     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 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Optional[str] = None    ):
            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
            a_    :     int										=    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 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     List[int]      ,							lowerCamelCase_      :     Optional[List[int]] = None    ):
            a_    :     Tuple										=    token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a
            else:
                 a_    :     List[str]										=    token_ids_a + [self.eos_token_id]
                 return self.prefix_tokens + token_ids_a + token_ids_a

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     List[int]      ,							lowerCamelCase_      :     Optional[List[int]] = None    ):
            a_    :     str										=    [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 						UpperCAmelCase(   self      :     List[str]    ):
            return list(
                set(filter(lambda lowerCamelCase_    : bool(re.search(R"""<extra_id_\d+>"""      ,							lowerCamelCase_    )    ) is not None      ,							self.additional_special_tokens    )    )    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            return [self.convert_tokens_to_ids(lowerCamelCase_    ) for token in self.get_sentinel_tokens()]







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase        =     {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     ['''ViTFeatureExtractor''']
       __lowerCamelCase        =     ['''ViTImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTForImageClassification''',
           '''ViTForMaskedImageModeling''',
           '''ViTModel''',
           '''ViTPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''TFViTForImageClassification''',
           '''TFViTModel''',
           '''TFViTPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''FlaxViTForImageClassification''',
           '''FlaxViTModel''',
           '''FlaxViTPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vit import ViTFeatureExtractor
              from .image_processing_vit import ViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit import (
                  VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTForImageClassification,
                  ViTForMaskedImageModeling,
                  ViTModel,
                  ViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
       import sys

       __lowerCamelCase        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



