






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       UpperCAmelCase__(							UpperCAmelCase__     ,							UpperCAmelCase__	):

   '''simple docstring'''




   A    :  int       				=    "pixel_values"
   A    :  Dict       				=    False
   A    :  Union[str, Any]       				=    TimmBackboneConfig


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




         """simple docstring"""

         requires_backends(self			,				'timm')
         super().__init__(lowerCamelCase__)
         lowercase__     =  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.')

         lowercase__     =  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.
         lowercase__     =  config.out_indices if getattr(lowerCamelCase__			,				'out_indices'			,				lowerCamelCase__) is not None else (-1,)

         lowercase__     =  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.
         lowercase__     =  self._backbone.return_layers
         lowercase__     =  {layer["module"]: str(lowerCamelCase__) for i, layer in enumerate(self._backbone.feature_info.info)}
         super()._init_backbone(lowerCamelCase__)


   @classmethod
   def 			UpperCAmelCase		(						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

         lowercase__     =  kwargs.pop('config'			,				TimmBackboneConfig())

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

         lowercase__     =  kwargs.pop('num_channels'			,				config.num_channels)
         lowercase__     =  kwargs.pop('features_only'			,				config.features_only)
         lowercase__     =  kwargs.pop('use_pretrained_backbone'			,				config.use_pretrained_backbone)
         lowercase__     =  kwargs.pop('out_indices'			,				config.out_indices)
         lowercase__     =  TimmBackboneConfig(
             backbone=lowerCamelCase__			,				num_channels=lowerCamelCase__			,				features_only=lowerCamelCase__			,				use_pretrained_backbone=lowerCamelCase__			,				out_indices=lowerCamelCase__			,				)
         return super()._from_config(lowerCamelCase__			,				**lowerCamelCase__)


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			str)	->				Optional[int]:




         """simple docstring"""

         pass



   def 			UpperCAmelCase		(						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"""

         lowercase__     =  return_dict if return_dict is not None else self.config.use_return_dict
         lowercase__     =  (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         lowercase__     =  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
               lowercase__     =  self._all_layers
               lowercase__     =  self._backbone(lowerCamelCase__			,				**lowerCamelCase__)
               lowercase__     =  self._return_layers
               lowercase__     =  tuple(hidden_states[i] for i in self.out_indices)
         else:
               lowercase__     =  self._backbone(lowerCamelCase__			,				**lowerCamelCase__)
               lowercase__     =  None

         lowercase__     =  tuple(lowerCamelCase__)
         lowercase__     =  tuple(lowerCamelCase__) if hidden_states is not None else None

         if not return_dict:
               lowercase__     =  (feature_maps,)
               if output_hidden_states:
                     lowercase__     =  output + (hidden_states,)
               return output

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





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


a__      :				List[Any]   	=			logging.get_logger(__name__)


a__      :				Optional[int]   	=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
a__      :				List[Any]   	=			{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

a__      :				int   	=			{
    "facebook/bart-base": 10_24,
    "facebook/bart-large": 10_24,
    "facebook/bart-large-mnli": 10_24,
    "facebook/bart-large-cnn": 10_24,
    "facebook/bart-large-xsum": 10_24,
    "yjernite/bart_eli5": 10_24,
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Optional[Any]       				=    VOCAB_FILES_NAMES
   A    :  Dict       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  str       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  int       				=    ["input_ids", "attention_mask"]
   A    :  Any       				=    BartTokenizer


   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			Any=None			,				lowerCAmelCase  :			Optional[int]=None			,				lowerCAmelCase  :			List[Any]=None			,				lowerCAmelCase  :			str="replace"			,				lowerCAmelCase  :			str="<s>"			,				lowerCAmelCase  :			int="</s>"			,				lowerCAmelCase  :			Optional[int]="</s>"			,				lowerCAmelCase  :			Union[str, Any]="<s>"			,				lowerCAmelCase  :			str="<unk>"			,				lowerCAmelCase  :			int="<pad>"			,				lowerCAmelCase  :			int="<mask>"			,				lowerCAmelCase  :			Dict=False			,				lowerCAmelCase  :			List[Any]=True			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				Optional[Any]:




         """simple docstring"""

         super().__init__(
             lowerCAmelCase			,				lowerCAmelCase			,				tokenizer_file=lowerCAmelCase			,				errors=lowerCAmelCase			,				bos_token=lowerCAmelCase			,				eos_token=lowerCAmelCase			,				sep_token=lowerCAmelCase			,				cls_token=lowerCAmelCase			,				unk_token=lowerCAmelCase			,				pad_token=lowerCAmelCase			,				mask_token=lowerCAmelCase			,				add_prefix_space=lowerCAmelCase			,				trim_offsets=lowerCAmelCase			,				**lowerCAmelCase			,				)

         lowercase__     =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'			,				lowerCAmelCase) != add_prefix_space:
               lowercase__     =  getattr(lowerCAmelCase			,				pre_tok_state.pop('type'))
               lowercase__     =  add_prefix_space
               lowercase__     =  pre_tok_class(**lowerCAmelCase)

         lowercase__     =  add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         lowercase__     =  'post_processor'
         lowercase__     =  getattr(self.backend_tokenizer			,				lowerCAmelCase			,				lowerCAmelCase)
         if tokenizer_component_instance:
               lowercase__     =  json.loads(tokenizer_component_instance.__getstate__())

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                     lowercase__     =  tuple(state['sep'])
               if "cls" in state:
                     lowercase__     =  tuple(state['cls'])

               lowercase__     =  False

               if state.get('add_prefix_space'			,				lowerCAmelCase) != add_prefix_space:
                     lowercase__     =  add_prefix_space
                     lowercase__     =  True

               if state.get('trim_offsets'			,				lowerCAmelCase) != trim_offsets:
                     lowercase__     =  trim_offsets
                     lowercase__     =  True

               if changes_to_apply:
                     lowercase__     =  getattr(lowerCAmelCase			,				state.pop('type'))
                     lowercase__     =  component_class(**lowerCAmelCase)
                     setattr(self.backend_tokenizer			,				lowerCAmelCase			,				lowerCAmelCase)


   @property
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				str:




         """simple docstring"""

         if self._mask_token is None:
               if self.verbose:
                     logger.error('Using mask_token, but it is not set yet.')
               return None
         return str(self._mask_token)


   @mask_token.setter
   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			int)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  AddedToken(lowerCAmelCase			,				lstrip=lowerCAmelCase			,				rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase			,				lowerCAmelCase) else value
         lowercase__     =  value


   def 			UpperCAmelCase		(						self  :			List[str]			,				*lowerCAmelCase  :			Tuple			,				**lowerCAmelCase  :			Optional[int])	->				BatchEncoding:




         """simple docstring"""

         lowercase__     =  kwargs.get('is_split_into_words'			,				lowerCAmelCase)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                   'to use it with pretokenized inputs.')

         return super()._batch_encode_plus(*lowerCAmelCase			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str			,				*lowerCAmelCase  :			Tuple			,				**lowerCAmelCase  :			str)	->				BatchEncoding:




         """simple docstring"""

         lowercase__     =  kwargs.get('is_split_into_words'			,				lowerCAmelCase)

         if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                   'to use it with pretokenized inputs.')

         return super()._encode_plus(*lowerCAmelCase			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[str] = None)	->				Tuple[str]:




         """simple docstring"""

         lowercase__     =  self._tokenizer.model.save(lowerCAmelCase			,				name=lowerCAmelCase)
         return tuple(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[int]=None)	->				Tuple:




         """simple docstring"""

         lowercase__     =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
               return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			List[int]			,				lowerCAmelCase  :			Optional[List[int]] = None)	->				List[int]:




         """simple docstring"""

         lowercase__     =  [self.sep_token_id]
         lowercase__     =  [self.cls_token_id]

         if token_ids_a is None:
               return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
