
'''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:
					__lowercase							:				Dict					=       None


__lowercase							:				List[str]					=       logging.get_logger(__name__)


__lowercase							:				List[Any]					=       '''▁'''

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

__lowercase							:				str					=       {
    '''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'''
        )
    },
}

__lowercase							:				Dict					=       {
    '''google/reformer-crime-and-punishment''': 524288,
}

class 		__lowercase			(					_lowercase      ):
			lowerCamelCase     :						List[Any]		 =     VOCAB_FILES_NAMES
			lowerCamelCase     :						Optional[Any]		 =     PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase     :						Optional[Any]		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase     :						Tuple		 =     ["input_ids", "attention_mask"]
			lowerCamelCase     :						Optional[Any]		 =     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     ,				)

								lowerCamelCase_   : Optional[Any]       =						vocab_file
								lowerCamelCase_   : Tuple       =						False if not self.vocab_file else True






			def     UpperCAmelCase__	(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
								lowerCamelCase_   : List[Any]       =						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,)




'''simple docstring'''


from typing import TYPE_CHECKING

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


__lowercase							:				List[str]					=       {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SamModel''',
					    '''SamPreTrainedModel''',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				int					=       [
					    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSamModel''',
					    '''TFSamPreTrainedModel''',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''SamImageProcessor''']


if TYPE_CHECKING:
					from .configuration_sam import (
					    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SamConfig,
					    SamMaskDecoderConfig,
					    SamPromptEncoderConfig,
					    SamVisionConfig,
					)
					from .processing_sam import SamProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_sam import SamImageProcessor

else:
					import sys

					__lowercase							:				List[str]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


