

"""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

_lowerCAmelCase       						=		logging.get_logger(__name__)







@add_end_docstrings(a_    )
class 						__UpperCamelCase     (     a_    ):

  def __init__(		self     ,*_A     ,**_A   ):
      '''simple docstring'''

      super().__init__(*_a     ,**_a   )
      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 __lowerCamelCase		(		self     ,_A=None   ):
      '''simple docstring'''

      _lowerCAmelCase							: List[Any]	=     {}
      if top_k is not None:
          _lowerCAmelCase							: Dict	=     top_k
      return {}, {}, postprocess_params

  def __call__(		self     ,_A     ,**_A   ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self     ,_A   ):
      '''simple docstring'''

      _lowerCAmelCase							: int	=     load_image(_a   )
      _lowerCAmelCase							: List[Any]	=     self.image_processor(images=_a     ,return_tensors=self.framework   )
      return model_inputs

  def __lowerCamelCase		(		self     ,_A   ):
      '''simple docstring'''

      _lowerCAmelCase							: Dict	=     self.model(**_a   )
      return model_outputs







  def __lowerCamelCase		(		self     ,_A     ,_A=5   ):
      '''simple docstring'''

      if top_k > self.model.config.num_labels:
          _lowerCAmelCase							: List[Any]	=     self.model.config.num_labels

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

      _lowerCAmelCase							: int	=     scores.tolist()
      _lowerCAmelCase							: List[str]	=     ids.tolist()
      return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_a     ,_a   )]







"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


