


'''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 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Any          =     VOCAB_FILES_NAMES
			lowerCAmelCase :	List[str]          =     PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase :	Optional[int]          =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




			def __init__(		self  :     Any	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     List[Any]="[GO]"	,  lowerCamelCase__  :     List[str]="[GO]"	,  lowerCamelCase__  :     Optional[int]="[s]"	,  lowerCamelCase__  :     int="[GO]"	,  **lowerCamelCase__  :     str )   ->str:



					'''simple docstring'''







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

					with open(lowerCamelCase__	,  encoding="utf-8" ) as vocab_handle:
							_UpperCAmelCase		:       Tuple								=      json.load(lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      {v: k for k, v in self.vocab.items()}




			@property
			def 							lowerCAmelCase__    (		self  :     Dict )   ->List[Any]:



					'''simple docstring'''







					return len(self.vocab )




			def 							lowerCAmelCase__    (		self  :     Any )   ->Union[str, Any]:



					'''simple docstring'''







					return dict(self.vocab	,  **self.added_tokens_encoder )




			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Tuple )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      []
					for s in text:
							char_tokens.extend(lowerCamelCase__ )
					return char_tokens




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     Tuple )   ->List[Any]:



					'''simple docstring'''







					return self.vocab.get(lowerCamelCase__	,  self.vocab.get(self.unk_token ) )




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     List[Any] )   ->Tuple:



					'''simple docstring'''







					return self.decoder.get(lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     Optional[str] = None )   ->Tuple[str]:



					'''simple docstring'''







					if not os.path.isdir(lowerCamelCase__ ):
							logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase__ ) )
							return
					_UpperCAmelCase		:       Optional[int]								=      os.path.join(
					    lowerCamelCase__	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

					return (vocab_file,)







'''simple docstring'''


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
						import timm


if is_torch_available():
						from torch import Tensor






class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__	):
			lowerCAmelCase :	int          =     "pixel_values"
			lowerCAmelCase :	Dict          =     False
			lowerCAmelCase :	Union[str, Any]          =     TimmBackboneConfig




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     Dict	,  **lowerCamelCase__  :     str )   ->Dict:



					'''simple docstring'''







					requires_backends(self	,  "timm" )
					super().__init__(lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      config

					if config.backbone is None:
							raise ValueError("backbone is not set in the config. Please set it to a timm model name." )

					if config.backbone not in timm.list_models():
							raise ValueError(F"""backbone {config.backbone} is not supported by timm.""" )

					if hasattr(lowerCamelCase__	,  "out_features" ) and config.out_features is not None:
							raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )

					_UpperCAmelCase		:       Optional[Any]								=      getattr(lowerCamelCase__	,  "use_pretrained_backbone"	,  lowerCamelCase__ )
					if pretrained is None:
							raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )

					# We just take the final layer by default. This matches the default for the transformers models.
					_UpperCAmelCase		:       int								=      config.out_indices if getattr(lowerCamelCase__	,  "out_indices"	,  lowerCamelCase__ ) is not None else (-1,)

					_UpperCAmelCase		:       List[Any]								=      timm.create_model(
					    config.backbone	,  pretrained=lowerCamelCase__	,  features_only=config.features_only	,  in_chans=config.num_channels	,  out_indices=lowerCamelCase__	,  **lowerCamelCase__	,  )
					# These are used to control the output of the model when called. If output_hidden_states is True, then
					# return_layers is modified to include all layers.
					_UpperCAmelCase		:       List[str]								=      self._backbone.return_layers
					_UpperCAmelCase		:       Optional[int]								=      {layer["module"]: str(lowerCamelCase__ ) for i, layer in enumerate(self._backbone.feature_info.info )}
					super()._init_backbone(lowerCamelCase__ )




			@classmethod
			def 							lowerCAmelCase__    (		cls  :     List[str]	,  lowerCamelCase__  :     str	,  *lowerCamelCase__  :     Tuple	,  **lowerCamelCase__  :     Optional[Any] )   ->Optional[Any]:



					'''simple docstring'''







					requires_backends(cls	,  ["vision", "timm"] )
					from ...models.timm_backbone import TimmBackboneConfig

					_UpperCAmelCase		:       Any								=      kwargs.pop("config"	,  TimmBackboneConfig() )

					_UpperCAmelCase		:       Dict								=      kwargs.pop("use_timm_backbone"	,  lowerCamelCase__ )
					if not use_timm:
							raise ValueError("use_timm_backbone must be True for timm backbones" )

					_UpperCAmelCase		:       str								=      kwargs.pop("num_channels"	,  config.num_channels )
					_UpperCAmelCase		:       Dict								=      kwargs.pop("features_only"	,  config.features_only )
					_UpperCAmelCase		:       str								=      kwargs.pop("use_pretrained_backbone"	,  config.use_pretrained_backbone )
					_UpperCAmelCase		:       Optional[Any]								=      kwargs.pop("out_indices"	,  config.out_indices )
					_UpperCAmelCase		:       Dict								=      TimmBackboneConfig(
					    backbone=lowerCamelCase__	,  num_channels=lowerCamelCase__	,  features_only=lowerCamelCase__	,  use_pretrained_backbone=lowerCamelCase__	,  out_indices=lowerCamelCase__	,  )
					return super()._from_config(lowerCamelCase__	,  **lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->Optional[int]:



					'''simple docstring'''







					pass






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     List[Any]=None	,  lowerCamelCase__  :     Union[str, Any]=None	,  **lowerCamelCase__  :     Dict )   ->Union[BackboneOutput, Tuple[Tensor, ...]]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      return_dict if return_dict is not None else self.config.use_return_dict
					_UpperCAmelCase		:       Optional[int]								=      (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					_UpperCAmelCase		:       Dict								=      output_attentions if output_attentions is not None else self.config.output_attentions

					if output_attentions:
							raise ValueError("Cannot output attentions for timm backbones at the moment" )

					if output_hidden_states:
							# We modify the return layers to include all the stages of the backbone
							_UpperCAmelCase		:       Optional[int]								=      self._all_layers
							_UpperCAmelCase		:       List[str]								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       List[Any]								=      self._return_layers
							_UpperCAmelCase		:       Tuple								=      tuple(hidden_states[i] for i in self.out_indices )
					else:
							_UpperCAmelCase		:       Any								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      None

					_UpperCAmelCase		:       Dict								=      tuple(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      tuple(lowerCamelCase__ ) if hidden_states is not None else None

					if not return_dict:
							_UpperCAmelCase		:       Dict								=      (feature_maps,)
							if output_hidden_states:
									_UpperCAmelCase		:       List[str]								=      output + (hidden_states,)
							return output

					return BackboneOutput(feature_maps=lowerCamelCase__	,  hidden_states=lowerCamelCase__	,  attentions=lowerCamelCase__ )



