
'''simple docstring'''






import json
import os
from typing import Optional, Tuple

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


A         =   logging.get_logger(__name__)

A         =   {'vocab_file': 'vocab.json'}

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

A         =   {'mgp-str': 27}




class      __snake_case		( UpperCAmelCase__):
					_lowerCAmelCase        =						VOCAB_FILES_NAMES
					_lowerCAmelCase        =						PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					def __init__(  self,     A,     A="[GO]",     A="[GO]",     A="[s]",     A="[GO]",     **A   ):

									"""simple docstring"""


									super().__init__(
									    unk_token=_UpperCAmelCase,     bos_token=_UpperCAmelCase,     eos_token=_UpperCAmelCase,     pad_token=_UpperCAmelCase,     **_UpperCAmelCase,     )

									with open(_UpperCAmelCase,     encoding='utf-8'   ) as vocab_handle:
													lowerCamelCase       :      Any          =			json.load(_UpperCAmelCase   )
									lowerCamelCase       :      Tuple          =			{v: k for k, v in self.vocab.items()}
					@property
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									return len(self.vocab   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									return dict(self.vocab,     **self.added_tokens_encoder   )
					def      UpperCAmelCase_				(  self,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      Optional[int]          =			[]
									for s in text:
													char_tokens.extend(_UpperCAmelCase   )
									return char_tokens
					def      UpperCAmelCase_				(  self,     A   ):

									"""simple docstring"""


									return self.vocab.get(_UpperCAmelCase,     self.vocab.get(self.unk_token   )   )
					def      UpperCAmelCase_				(  self,     A   ):

									"""simple docstring"""


									return self.decoder.get(_UpperCAmelCase   )




					def      UpperCAmelCase_				(  self,     A,     A = None   ):

									"""simple docstring"""


									if not os.path.isdir(_UpperCAmelCase   ):
													logger.error('Vocabulary path ({}) should be a directory'.format(_UpperCAmelCase   )   )
													return
									lowerCamelCase       :      Optional[Any]          =			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,)




from typing import TYPE_CHECKING

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


A			:						Union[str, Any]        =  {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  ['ConvNextFeatureExtractor']
	A			:						Optional[Any]        =  ['ConvNextImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  [
	    'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConvNextForImageClassification',
	    'ConvNextModel',
	    'ConvNextPreTrainedModel',
	    'ConvNextBackbone',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'TFConvNextForImageClassification',
	    'TFConvNextModel',
	    'TFConvNextPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_convnext import ConvNextFeatureExtractor
		from .image_processing_convnext import ConvNextImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnext import (
		    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextBackbone,
		    ConvNextForImageClassification,
		    ConvNextModel,
		    ConvNextPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
	import sys

	A			:						int        =  _LazyModule(__name__, globals()['__file__'], _import_structure)

