






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      :       int    =						logging.get_logger(__name__)



@add_end_docstrings(a_      )
class 				_UpperCamelCase      (a_      ):


      def __init__(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						str:
           super().__init__(*__UpperCamelCase       ,    **__UpperCamelCase						)
           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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=None						)->						str:
           __lowerCAmelCase             =       {}
           if top_k is not None:
                __lowerCAmelCase             =       top_k
           return {}, {}, postprocess_params


      def __call__(						self       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           return super().__call__(__UpperCamelCase       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						List[Any]:
           __lowerCAmelCase             =       load_image(__UpperCamelCase						)
           __lowerCAmelCase             =       self.image_processor(images=__UpperCamelCase       ,    return_tensors=self.framework						)
           return model_inputs


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Any:
           __lowerCAmelCase             =       self.model(**__UpperCamelCase						)
           return model_outputs


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=5						)->						Tuple:
           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(__UpperCamelCase						)
           elif self.framework == "tf":
                __lowerCAmelCase             =       stable_softmax(model_outputs.logits       ,    axis=-1						)[0]
                __lowerCAmelCase             =       tf.math.top_k(__UpperCamelCase       ,    k=__UpperCamelCase						)
                __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(__UpperCamelCase       ,    __UpperCamelCase						)]














import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCamelCase      :       str    =						[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]





def    __lowerCAmelCase   (    __snake_case							):
     # Test all the extensions added in the setup
     for file in FILES_TO_FIND:
          if not (transformers_path / file).exists():
               return False
     return True


if __name__ == "__main__":
 lowerCamelCase      :       str    =						argparse.ArgumentParser()
 parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
 lowerCamelCase      :       Dict    =						parser.parse_args()
 if args.check_lib:
  lowerCamelCase      :       Optional[int]    =						importlib.import_module('''transformers''')
  lowerCamelCase      :       str    =						Path(transformers_module.__file__).parent
 else:
  lowerCamelCase      :       List[Any]    =						Path.cwd() / '''build/lib/transformers'''
 if not test_custom_files_are_present(transformers_path):
  raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')






