




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

lowercase__				:      Union[str, Any]	  = logging.get_logger(__name__)




@add_end_docstrings(lowerCamelCase__				)
class       a__	( lowerCamelCase__				):







     def __init__(     self  ,		*A  ,		**A			)  ->   List[str]:



          '''simple docstring'''



          super().__init__(*__snake_case  ,		**__snake_case			)
          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			)  ->   List[str]:



          '''simple docstring'''



          a     =				{}
          if top_k is not None:
               a     =				top_k
          return {}, {}, postprocess_params







     def __call__(     self  ,		A  ,		**A			)  ->   List[str]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Tuple:



          '''simple docstring'''



          a     =				load_image(__snake_case			)
          a     =				self.image_processor(images=__snake_case  ,		return_tensors=self.framework			)
          return model_inputs







     def       lowerCAmelCase_    (     self  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				self.model(**__snake_case			)
          return model_outputs







     def       lowerCAmelCase_    (     self  ,		A  ,		A=5			)  ->   List[Any]:



          '''simple docstring'''



          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     =				probs.topk(__snake_case			)
          elif self.framework == "tf":
               a     =				stable_softmax(model_outputs.logits  ,		axis=-1			)[0]
               a     =				tf.math.top_k(__snake_case  ,		k=__snake_case			)
               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(__snake_case  ,		__snake_case			)]







import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(UpperCamelCase__				)       , """Tatoeba directory does not exist."""				)
class       a__	( unittest.TestCase				):







     @cached_property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				tempfile.mkdtemp()
          return TatoebaConverter(save_dir=A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          self.resolver.convert_models(["heb-eng"]			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a  ,				a     =				self.resolver.write_model_card("opus-mt-he-en"  ,		dry_run=A			)
          assert mmeta["long_pair"] == "heb-eng"

