





'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


a        =						logging.get_logger(__name__)

a        =						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a        =						{
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

a        =						{
    "distilbert-base-uncased": 512,
    "distilbert-base-uncased-distilled-squad": 512,
    "distilbert-base-cased": 512,
    "distilbert-base-cased-distilled-squad": 512,
    "distilbert-base-german-cased": 512,
    "distilbert-base-multilingual-cased": 512,
}


a        =						{
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}







class __a (   _snake_case			):
   __UpperCamelCase   : int	      =	VOCAB_FILES_NAMES
   __UpperCamelCase   : Union[str, Any]	      =	PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase   : Optional[int]	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase   : List[Any]	      =	PRETRAINED_INIT_CONFIGURATION
   __UpperCamelCase   : str	      =	['input_ids', 'attention_mask']
   __UpperCamelCase   : str	      =	DistilBertTokenizer





   def __init__(					self				:			Union[str, Any]				,lowerCamelCase				:			Optional[int]=None				,lowerCamelCase				:			List[str]=None				,lowerCamelCase				:			int=True				,lowerCamelCase				:			Any="[UNK]"				,lowerCamelCase				:			Tuple="[SEP]"				,lowerCamelCase				:			Dict="[PAD]"				,lowerCamelCase				:			Optional[Any]="[CLS]"				,lowerCamelCase				:			int="[MASK]"				,lowerCamelCase				:			Union[str, Any]=True				,lowerCamelCase				:			Tuple=None				,**lowerCamelCase				:			Any				,):

      '''simple docstring'''

      super().__init__(
          lowerCamelCase				,tokenizer_file=lowerCamelCase				,do_lower_case=lowerCamelCase				,unk_token=lowerCamelCase				,sep_token=lowerCamelCase				,pad_token=lowerCamelCase				,cls_token=lowerCamelCase				,mask_token=lowerCamelCase				,tokenize_chinese_chars=lowerCamelCase				,strip_accents=lowerCamelCase				,**lowerCamelCase				,)

      __SCREAMING_SNAKE_CASE      							=       json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
      if (
          normalizer_state.get("""lowercase"""				,lowerCamelCase    ) != do_lower_case
          or normalizer_state.get("""strip_accents"""				,lowerCamelCase    ) != strip_accents
          or normalizer_state.get("""handle_chinese_chars"""				,lowerCamelCase    ) != tokenize_chinese_chars
      ):
         __SCREAMING_SNAKE_CASE      							=       getattr(lowerCamelCase				,normalizer_state.pop("""type"""    )    )
         __SCREAMING_SNAKE_CASE      							=       do_lower_case
         __SCREAMING_SNAKE_CASE      							=       strip_accents
         __SCREAMING_SNAKE_CASE      							=       tokenize_chinese_chars
         __SCREAMING_SNAKE_CASE      							=       normalizer_class(**lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       do_lower_case





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			List[str]				,lowerCamelCase				:			Dict=None    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

      return output





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			List[int]				,lowerCamelCase				:			Optional[List[int]] = None    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       [self.sep_token_id]
      __SCREAMING_SNAKE_CASE      							=       [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    ) * [0] + len(token_ids_a + sep    ) * [1]





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			str				,lowerCamelCase				:			Optional[str] = None    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self._tokenizer.model.save(lowerCamelCase				,name=lowerCamelCase    )
      return tuple(lowerCamelCase    )












'''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

a        =						logging.get_logger(__name__)







@add_end_docstrings(_snake_case			)
class __a (   _snake_case			):





   def __init__(					self				:			Union[str, Any]				,**lowerCamelCase				:			str    ):

      '''simple docstring'''

      super().__init__(**lowerCamelCase    )

      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				:			Dict				,lowerCamelCase				:			Union[str, List[str], "Image", List["Image"]]				,**lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

      return super().__call__(lowerCamelCase				,**lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,**lowerCamelCase				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       {}
      if "candidate_labels" in kwargs:
         __SCREAMING_SNAKE_CASE      							=       kwargs["""candidate_labels"""]
      if "hypothesis_template" in kwargs:
         __SCREAMING_SNAKE_CASE      							=       kwargs["""hypothesis_template"""]

      return preprocess_params, {}, {}





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			Union[str, Any]=None				,lowerCamelCase				:			Union[str, Any]="This is a photo of {}."    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       load_image(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.image_processor(images=[image]				,return_tensors=self.framework    )
      __SCREAMING_SNAKE_CASE      							=       candidate_labels
      __SCREAMING_SNAKE_CASE      							=       [hypothesis_template.format(lowerCamelCase    ) for x in candidate_labels]
      __SCREAMING_SNAKE_CASE      							=       self.tokenizer(lowerCamelCase				,return_tensors=self.framework				,padding=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       [text_inputs]
      return inputs





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       model_inputs.pop("""candidate_labels"""    )
      __SCREAMING_SNAKE_CASE      							=       model_inputs.pop("""text_inputs"""    )
      if isinstance(text_inputs[0]				,lowerCamelCase    ):
         __SCREAMING_SNAKE_CASE      							=       text_inputs[0]
      else:
         # Batching case.
         __SCREAMING_SNAKE_CASE      							=       text_inputs[0][0]

      __SCREAMING_SNAKE_CASE      							=       self.model(**lowerCamelCase				,**lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       {
          """candidate_labels""": candidate_labels,
          """logits""": outputs.logits_per_image,
      }
      return model_outputs





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       model_outputs.pop("""candidate_labels"""    )
      __SCREAMING_SNAKE_CASE      							=       model_outputs["""logits"""][0]
      if self.framework == "pt":
         __SCREAMING_SNAKE_CASE      							=       logits.softmax(dim=-1    ).squeeze(-1    )
         __SCREAMING_SNAKE_CASE      							=       probs.tolist()
         if not isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       [scores]
      elif self.framework == "tf":
         __SCREAMING_SNAKE_CASE      							=       stable_softmax(lowerCamelCase				,axis=-1    )
         __SCREAMING_SNAKE_CASE      							=       probs.numpy().tolist()
      else:
         raise ValueError(f"""Unsupported framework: {self.framework}"""    )

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





