




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

UpperCamelCase								=	logging.get_logger(__name__)





@add_end_docstrings(UpperCamelCase__       )
class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    def __init__(			self	:	Optional[int]  ,      *SCREAMING_SNAKE_CASE__	:	Tuple  ,      **SCREAMING_SNAKE_CASE__	:	Tuple  ) ->			Dict:
         super().__init__(*SCREAMING_SNAKE_CASE__  ,      **SCREAMING_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   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=None  ) ->			Optional[int]:
         lowerCAmelCase__            =					{}
         if top_k is not None:
              lowerCAmelCase__            =					top_k
         return {}, {}, postprocess_params


    def __call__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	Union[str, List[str], "Image.Image", List["Image.Image"]]  ,      **SCREAMING_SNAKE_CASE__	:	str  ) ->			Dict:
         return super().__call__(SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ) ->			Optional[Any]:
         lowerCAmelCase__            =					load_image(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					self.image_processor(images=SCREAMING_SNAKE_CASE__  ,      return_tensors=self.framework  )
         return model_inputs


    def   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	int  ) ->			List[Any]:
         lowerCAmelCase__            =					self.model(**SCREAMING_SNAKE_CASE__  )
         return model_outputs


    def   a (			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	int=5  ) ->			str:
         if top_k > self.model.config.num_labels:
              lowerCAmelCase__            =					self.model.config.num_labels

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

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

'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  tf.convert_to_tensor(
            [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4     )     )



