




'''simple docstring'''

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class __a (   _snake_case			):
   __UpperCamelCase   : Optional[Any]	      =	'EncodecFeatureExtractor'
   __UpperCamelCase   : Dict	      =	('T5Tokenizer', 'T5TokenizerFast')





   def __init__(					self				:			Any				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			List[str]    ):

      '''simple docstring'''

      super().__init__(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.feature_extractor
      __SCREAMING_SNAKE_CASE      							=       False





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

      '''simple docstring'''

      return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase				,language=lowerCamelCase				,no_timestamps=lowerCamelCase    )





   def __call__(					self				:			Any				,*lowerCamelCase				:			Tuple				,**lowerCamelCase				:			List[str]    ):

      '''simple docstring'''

      if self._in_target_context_manager:
         return self.current_processor(*lowerCamelCase				,**lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""audio"""				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""sampling_rate"""				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""text"""				,lowerCamelCase    )
      if len(lowerCamelCase    ) > 0:
         __SCREAMING_SNAKE_CASE      							=       args[0]
         __SCREAMING_SNAKE_CASE      							=       args[1:]

      if audio is None and text is None:
         raise ValueError("""You need to specify either an `audio` or `text` input to process."""    )

      if text is not None:
         __SCREAMING_SNAKE_CASE      							=       self.tokenizer(lowerCamelCase				,**lowerCamelCase    )

      if audio is not None:
         __SCREAMING_SNAKE_CASE      							=       self.feature_extractor(lowerCamelCase				,*lowerCamelCase				,sampling_rate=lowerCamelCase				,**lowerCamelCase    )

      if audio is None:
         return inputs

      elif text is None:
         return audio_inputs

      else:
         __SCREAMING_SNAKE_CASE      							=       audio_inputs["""input_values"""]
         if "padding_mask" in audio_inputs:
            __SCREAMING_SNAKE_CASE      							=       audio_inputs["""padding_mask"""]
         return inputs





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""audio"""				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       kwargs.pop("""padding_mask"""				,lowerCamelCase    )

      if len(lowerCamelCase    ) > 0:
         __SCREAMING_SNAKE_CASE      							=       args[0]
         __SCREAMING_SNAKE_CASE      							=       args[1:]

      if audio_values is not None:
         return self._decode_audio(lowerCamelCase				,padding_mask=lowerCamelCase    )
      else:
         return self.tokenizer.batch_decode(*lowerCamelCase				,**lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Any				,*lowerCamelCase				:			Any				,**lowerCamelCase				:			Dict    ):

      '''simple docstring'''

      return self.tokenizer.decode(*lowerCamelCase				,**lowerCamelCase    )





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       to_numpy(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       audio_values.shape

      if padding_mask is None:
         return list(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       to_numpy(lowerCamelCase    )

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      __SCREAMING_SNAKE_CASE      							=       seq_len - padding_mask.shape[-1]
      __SCREAMING_SNAKE_CASE      							=       1 - self.feature_extractor.padding_value
      __SCREAMING_SNAKE_CASE      							=       np.pad(lowerCamelCase				,((0, 0), (0, difference))				,"""constant"""				,constant_values=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       audio_values.tolist()
      for i in range(lowerCamelCase    ):
         __SCREAMING_SNAKE_CASE      							=       np.asarray(audio_values[i]    )[
             padding_mask[i][None, :] != self.feature_extractor.padding_value
         ]
         __SCREAMING_SNAKE_CASE      							=       sliced_audio.reshape(lowerCamelCase				,-1    )

      return audio_values












'''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





