"""simple docstring"""




import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {"vocab_file": "vocab.json"}

__lowerCamelCase							 =     {
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

__lowerCamelCase							 =     {"mgp-str": 27}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  int  							=						VOCAB_FILES_NAMES
						lowerCAmelCase__     :  Any  							=						PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__     :  Optional[int]  							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase="[GO]"						,__UpperCAmelCase="[GO]"						,__UpperCAmelCase="[s]"						,__UpperCAmelCase="[GO]"						,**__UpperCAmelCase   )    -> Optional[Any]:
													super().__init__(
													    unk_token=__UpperCAmelCase						,bos_token=__UpperCAmelCase						,eos_token=__UpperCAmelCase						,pad_token=__UpperCAmelCase						,**__UpperCAmelCase						,)

													with open(__UpperCAmelCase						,encoding='utf-8'   ) as vocab_handle:
																				A__		   =							json.load(__UpperCAmelCase   )
													A__		   =							{v: k for k, v in self.vocab.items()}





						@property
						def  snake_case__		(				self   )    -> List[str]:
													return len(self.vocab   )





						def  snake_case__		(				self   )    -> Tuple:
													return dict(self.vocab						,**self.added_tokens_encoder   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							[]
													for s in text:
																				char_tokens.extend(__UpperCAmelCase   )
													return char_tokens





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													return self.vocab.get(__UpperCAmelCase						,self.vocab.get(self.unk_token   )   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> List[Any]:
													return self.decoder.get(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None   )    -> Tuple[str]:
													if not os.path.isdir(__UpperCAmelCase   ):
																				logger.error('Vocabulary path ({}) should be a directory'.format(__UpperCAmelCase   )   )
																				return
													A__		   =							os.path.join(
													    __UpperCAmelCase						,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

													with open(__UpperCAmelCase						,'w'						,encoding='utf-8'   ) as f:
																				f.write(json.dumps(self.vocab						,indent=2						,sort_keys=__UpperCAmelCase						,ensure_ascii=__UpperCAmelCase   ) + '\n'   )

													return (vocab_file,)






"""simple docstring"""




# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["VivitImageProcessor"]


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VivitModel",
				    "VivitPreTrainedModel",
				    "VivitForVideoClassification",
				]


if TYPE_CHECKING:
				from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_vivit import VivitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vivit import (
								    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VivitForVideoClassification,
								    VivitModel,
								    VivitPreTrainedModel,
								)


else:
				import sys

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





