



'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 _lowerCAmelCase      						=	None

_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase      						=	{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

_lowerCAmelCase      						=	{
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

_lowerCAmelCase      						=	'''▁'''






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Tuple				=      VOCAB_FILES_NAMES
  __lowercase							:      Dict				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      Dict				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:      Tuple				=      ['''input_ids''', '''attention_mask''']
  __lowercase							:      List[str]				=      BarthezTokenizer




  def __init__(    self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase="<s>"  ,__UpperCAmelCase="</s>"  ,__UpperCAmelCase="</s>"  ,__UpperCAmelCase="<s>"  ,__UpperCAmelCase="<unk>"  ,__UpperCAmelCase="<pad>"  ,__UpperCAmelCase="<mask>"  ,**__UpperCAmelCase  ,)    ->		Dict:
      # Mask token behave like a normal word, i.e. include the space before it
      lowerCAmelCase__ :  Union[str, Any]							      =					AddedToken(__UpperCAmelCase  ,lstrip=__UpperCAmelCase  ,rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) else mask_token

      super().__init__(
          __UpperCAmelCase  ,tokenizer_file=__UpperCAmelCase  ,bos_token=__UpperCAmelCase  ,eos_token=__UpperCAmelCase  ,unk_token=__UpperCAmelCase  ,sep_token=__UpperCAmelCase  ,cls_token=__UpperCAmelCase  ,pad_token=__UpperCAmelCase  ,mask_token=__UpperCAmelCase  ,**__UpperCAmelCase  ,)

      lowerCAmelCase__ :  List[Any]							      =					vocab_file
      lowerCAmelCase__ :  Union[str, Any]							      =					False if not self.vocab_file else True




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		List[int]:

      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowerCAmelCase__ :  Tuple							      =					[self.cls_token_id]
      lowerCAmelCase__ :  Union[str, Any]							      =					[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		List[int]:
      lowerCAmelCase__ :  Tuple							      =					[self.sep_token_id]
      lowerCAmelCase__ :  str							      =					[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]






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      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(__UpperCAmelCase      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      lowerCAmelCase__ :  Tuple							      =					os.path.join(
          __UpperCAmelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

      if os.path.abspath(self.vocab_file      ) != os.path.abspath(__UpperCAmelCase      ):
          copyfile(self.vocab_file  ,__UpperCAmelCase      )

      return (out_vocab_file,)







'''simple docstring'''





from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

_lowerCAmelCase      						=	logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE_     )
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,**__UpperCAmelCase      )    ->		Tuple:
      super().__init__(**__UpperCAmelCase      )

      requires_backends(self  ,"""vision"""      )
      self.check_model_type(
          TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == """tf"""
          else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING      )




  def __call__(    self  ,__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		str:
      return super().__call__(__UpperCAmelCase  ,**__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,**__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  List[Any]							      =					{}
      if "candidate_labels" in kwargs:
          lowerCAmelCase__ :  int							      =					kwargs["""candidate_labels"""]
      if "hypothesis_template" in kwargs:
          lowerCAmelCase__ :  Optional[int]							      =					kwargs["""hypothesis_template"""]

      return preprocess_params, {}, {}




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,__UpperCAmelCase="This is a photo of {}."      )    ->		int:
      lowerCAmelCase__ :  str							      =					load_image(__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					self.image_processor(images=[image]  ,return_tensors=self.framework      )
      lowerCAmelCase__ :  List[Any]							      =					candidate_labels
      lowerCAmelCase__ :  List[str]							      =					[hypothesis_template.format(__UpperCAmelCase      ) for x in candidate_labels]
      lowerCAmelCase__ :  Optional[Any]							      =					self.tokenizer(__UpperCAmelCase  ,return_tensors=self.framework  ,padding=__UpperCAmelCase      )
      lowerCAmelCase__ :  Tuple							      =					[text_inputs]
      return inputs




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Tuple							      =					model_inputs.pop("""candidate_labels"""      )
      lowerCAmelCase__ :  Union[str, Any]							      =					model_inputs.pop("""text_inputs"""      )
      if isinstance(text_inputs[0]  ,__UpperCAmelCase      ):
          lowerCAmelCase__ :  int							      =					text_inputs[0]
      else:
          # Batching case.
          lowerCAmelCase__ :  Dict							      =					text_inputs[0][0]

      lowerCAmelCase__ :  Any							      =					self.model(**__UpperCAmelCase  ,**__UpperCAmelCase      )

      lowerCAmelCase__ :  Union[str, Any]							      =					{
          """candidate_labels""": candidate_labels,
          """logits""": outputs.logits_per_image,
      }
      return model_outputs






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      lowerCAmelCase__ :  Union[str, Any]							      =					model_outputs.pop("""candidate_labels"""      )
      lowerCAmelCase__ :  List[str]							      =					model_outputs["""logits"""][0]
      if self.framework == "pt":
          lowerCAmelCase__ :  List[str]							      =					logits.softmax(dim=-1      ).squeeze(-1      )
          lowerCAmelCase__ :  Optional[Any]							      =					probs.tolist()
          if not isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
              lowerCAmelCase__ :  Dict							      =					[scores]
      elif self.framework == "tf":
          lowerCAmelCase__ :  Any							      =					stable_softmax(__UpperCAmelCase  ,axis=-1      )
          lowerCAmelCase__ :  List[Any]							      =					probs.numpy().tolist()
      else:
          raise ValueError(F"""Unsupported framework: {self.framework}"""      )

      lowerCAmelCase__ :  Tuple							      =					[
          {"""score""": score, """label""": candidate_label}
          for score, candidate_label in sorted(zip(__UpperCAmelCase  ,__UpperCAmelCase      )  ,key=lambda __UpperCAmelCase      : -x[0]      )
      ]
      return result


