

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


UpperCamelCase__	:   Any								=     {
    "configuration_tvlt": ["TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP", "TvltConfig"],
    "feature_extraction_tvlt": ["TvltFeatureExtractor"],
    "processing_tvlt": ["TvltProcessor"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__	:   Tuple								=     [
           "TVLT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TvltModel",
           "TvltForPreTraining",
           "TvltForAudioVisualClassification",
           "TvltPreTrainedModel",
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__	:   List[Any]								=     ["TvltImageProcessor"]


if TYPE_CHECKING:
       from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
       from .processing_tvlt import TvltProcessor
       from .feature_extraction_tvlt import TvltFeatureExtractor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tvlt import (
                  TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TvltForAudioVisualClassification,
                  TvltForPreTraining,
                  TvltModel,
                  TvltPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_tvlt import TvltImageProcessor


else:
       import sys

       UpperCamelCase__	:   Optional[Any]								=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import os
from shutil import copyfile
from typing import Optional, Tuple

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


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       UpperCamelCase__	:   Optional[int]								=     None


UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)


UpperCamelCase__	:   Any								=     "▁"

UpperCamelCase__	:   Any								=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__	:   List[Any]								=     {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

UpperCamelCase__	:   Optional[Any]								=     {
    "google/reformer-crime-and-punishment": 524_288,
}



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						int    			=	VOCAB_FILES_NAMES
  __lowerCAmelCase  :						int    			=	PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase  :						Tuple    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase  :						List[str]    			=	['input_ids', 'attention_mask']
  __lowerCAmelCase  :						Dict    			=	ReformerTokenizer

  def __init__(       self    ,		_A=None    ,		_A=None    ,		_A="</s>"    ,		_A="<unk>"    ,		_A=[]    ,		**_A    ,		):
      super().__init__(
          _A    ,		tokenizer_file=_A    ,		eos_token=_A    ,		unk_token=_A    ,		additional_special_tokens=_A    ,		**_A    ,		)

      SCREAMING_SNAKE_CASE_						=    vocab_file
      SCREAMING_SNAKE_CASE_						=    False if not self.vocab_file else True


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = 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(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_						=    os.path.join(
          _A    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

      if os.path.abspath(self.vocab_file) != os.path.abspath(_A):
          copyfile(self.vocab_file    ,		_A)

      return (out_vocab_file,)


