






'''simple docstring'''
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:
						__lowerCAmelCase							=None


__lowerCAmelCase							=logging.get_logger(__name__)


__lowerCAmelCase							="▁"

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

__lowerCAmelCase							={
    "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"
        )
    },
}

__lowerCAmelCase							={
    "google/reformer-crime-and-punishment": 52_4288,
}


class    _snake_case   (				a__					):

					"""simple docstring"""





					_UpperCamelCase		 =      VOCAB_FILES_NAMES
					_UpperCamelCase		 =      PRETRAINED_VOCAB_FILES_MAP
					_UpperCamelCase		 =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_UpperCamelCase		 =      ["input_ids", "attention_mask"]
					_UpperCamelCase		 =      ReformerTokenizer




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

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




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

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

										return (out_vocab_file,)









'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase							={"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RegNetForImageClassification",
						    "RegNetModel",
						    "RegNetPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRegNetForImageClassification",
						    "TFRegNetModel",
						    "TFRegNetPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "FlaxRegNetForImageClassification",
						    "FlaxRegNetModel",
						    "FlaxRegNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_regnet import (
												    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RegNetForImageClassification,
												    RegNetModel,
												    RegNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_regnet import (
												    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRegNetForImageClassification,
												    TFRegNetModel,
												    TFRegNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_regnet import (
												    FlaxRegNetForImageClassification,
												    FlaxRegNetModel,
												    FlaxRegNetPreTrainedModel,
												)


else:
						import sys

						__lowerCAmelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure)

