"""simple docstring"""



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[Any]           =		["""image_processor""", """tokenizer"""]
 _snake_case   :			Optional[int]           =		"""BridgeTowerImageProcessor"""
 _snake_case   :			Tuple           =		("""RobertaTokenizer""", """RobertaTokenizerFast""")


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase  ,						lowerCamelCase		)


 def __call__(				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = True  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = 0  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  self.tokenizer(
            text=lowerCamelCase  ,						add_special_tokens=lowerCamelCase  ,						padding=lowerCamelCase  ,						truncation=lowerCamelCase  ,						max_length=lowerCamelCase  ,						stride=lowerCamelCase  ,						pad_to_multiple_of=lowerCamelCase  ,						return_token_type_ids=lowerCamelCase  ,						return_attention_mask=lowerCamelCase  ,						return_overflowing_tokens=lowerCamelCase  ,						return_special_tokens_mask=lowerCamelCase  ,						return_offsets_mapping=lowerCamelCase  ,						return_length=lowerCamelCase  ,						verbose=lowerCamelCase  ,						return_tensors=lowerCamelCase  ,						**lowerCamelCase  ,						)
        # add pixel_values + pixel_mask
        __a     				=  self.image_processor(
            lowerCamelCase  ,						return_tensors=lowerCamelCase  ,						do_normalize=lowerCamelCase  ,						do_center_crop=lowerCamelCase  ,						**lowerCamelCase		)
        encoding.update(lowerCamelCase		)

        return encoding


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.batch_decode(*lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.decode(*lowerCamelCase  ,						**lowerCamelCase		)






 @property
 def 		a__ (				self		):
        __a     				=  self.tokenizer.model_input_names
        __a     				=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

"""simple docstring"""



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_tf_available():
   import tensorflow as tf

   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   from ..tf_utils import stable_softmax

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

SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)






@add_end_docstrings(snake_case_   )
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)
        requires_backends(self  ,						"vision"		)
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == "tf"
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING		)


 def 		a__ (				self  ,						lowerCamelCase=None		):
        __a     				=  {}
        if top_k is not None:
               __a     				=  top_k
        return {}, {}, postprocess_params


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  load_image(lowerCamelCase		)
        __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
        return model_inputs


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.model(**lowerCamelCase		)
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=5		):
        if top_k > self.model.config.num_labels:
               __a     				=  self.model.config.num_labels

        if self.framework == "pt":
               __a     				=  model_outputs.logits.softmax(-1		)[0]
               __a				, __a     				=  probs.topk(lowerCamelCase		)
        elif self.framework == "tf":
               __a     				=  stable_softmax(model_outputs.logits  ,						axis=-1		)[0]
               __a     				=  tf.math.top_k(lowerCamelCase  ,						k=lowerCamelCase		)
               __a				, __a     				=  topk.values.numpy(), topk.indices.numpy()
        else:
               raise ValueError(F"Unsupported framework: {self.framework}"		)

        __a     				=  scores.tolist()
        __a     				=  ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase  ,						lowerCamelCase		)]
