"""simple docstring"""






import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
       import torch

       from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin


class        UpperCAmelCase		:



    """simple docstring"""

    def __init__(  self       ,     _UpperCAmelCase       ,     _UpperCAmelCase=None       ,     _UpperCAmelCase=None       ,     _UpperCAmelCase=None       ,     _UpperCAmelCase="resnet50"       ,     _UpperCAmelCase=3       ,     _UpperCAmelCase=32       ,     _UpperCAmelCase=3       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=True       ,     ):
       lowercase__:  Dict   = parent
       lowercase__:  str   = out_indices if out_indices is not None else [4]
       lowercase__:  Any   = stage_names
       lowercase__:  str   = out_features
       lowercase__:  Optional[int]   = backbone
       lowercase__:  Dict   = batch_size
       lowercase__:  int   = image_size
       lowercase__:  Optional[int]   = num_channels
       lowercase__:  Optional[int]   = use_pretrained_backbone
       lowercase__:  Any   = is_training






    def _snake_case     (  self				):
       lowercase__:  Optional[Any]   = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
       lowercase__:  Optional[int]   = self.get_config()

       return config, pixel_values






    def _snake_case     (  self				):
       return TimmBackboneConfig(
           image_size=self.image_size       ,     num_channels=self.num_channels       ,     out_features=self.out_features       ,     out_indices=self.out_indices       ,     stage_names=self.stage_names       ,     use_pretrained_backbone=self.use_pretrained_backbone       ,     backbone=self.backbone       ,     )






    def _snake_case     (  self       ,     _UpperCAmelCase       ,     _UpperCAmelCase				):
       lowercase__:  List[str]   = TimmBackbone(config=A_				)
       model.to(A_				)
       model.eval()
       with torch.no_grad():
          lowercase__:  Union[str, Any]   = model(A_				)
       self.parent.assertEqual(
           result.feature_map[-1].shape       ,     (self.batch_size, model.channels[-1], 14, 14)       ,     )






    def _snake_case     (  self				):
       lowercase__:  Dict   = self.prepare_config_and_inputs()
       lowercase__:  int   = config_and_inputs
       lowercase__:  Dict   = {"""pixel_values""": pixel_values}
       return config, inputs_dict



@require_torch
@require_timm
class        UpperCAmelCase		(_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase       ,unittest.TestCase    ):



    """simple docstring"""

    _UpperCAmelCase      :Union[str, Any]		       =					(TimmBackbone,) if is_torch_available() else ()
    _UpperCAmelCase      :str		       =					{"feature-extraction": TimmBackbone} if is_torch_available() else {}
    _UpperCAmelCase      :Any		       =					False
    _UpperCAmelCase      :Dict		       =					False
    _UpperCAmelCase      :Optional[Any]		       =					False
    _UpperCAmelCase      :str		       =					False






    def _snake_case     (  self				):
       lowercase__:  int   = TimmBackboneModelTester(self				)
       lowercase__:  Optional[Any]   = ConfigTester(self       ,     config_class=A_       ,     has_text_modality=A_				)






    def _snake_case     (  self				):
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






    def _snake_case     (  self				):
       lowercase__:  Optional[Any]   = """resnet18"""
       lowercase__:  List[Any]   = """microsoft/resnet-18"""

       lowercase__:  Dict   = AutoBackbone.from_pretrained(A_       ,     use_timm_backbone=A_				)
       lowercase__:  List[Any]   = AutoBackbone.from_pretrained(A_				)

       self.assertEqual(len(timm_model.out_features				)       ,     len(transformers_model.out_features				)				)
       self.assertEqual(len(timm_model.stage_names				)       ,     len(transformers_model.stage_names				)				)
       self.assertEqual(timm_model.channels       ,     transformers_model.channels				)
       # Out indices are set to the last layer by default. For timm models, we don't know
       # the number of layers in advance, so we set it to (-1,), whereas for transformers
       # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
       self.assertEqual(timm_model.out_indices       ,     (-1,)				)
       self.assertEqual(transformers_model.out_indices       ,     [len(timm_model.stage_names				) - 1]				)

       lowercase__:  List[Any]   = AutoBackbone.from_pretrained(A_       ,     use_timm_backbone=A_       ,     out_indices=[1, 2, 3]				)
       lowercase__:  List[Any]   = AutoBackbone.from_pretrained(A_       ,     out_indices=[1, 2, 3]				)

       self.assertEqual(timm_model.out_indices       ,     transformers_model.out_indices				)
       self.assertEqual(len(timm_model.out_features				)       ,     len(transformers_model.out_features				)				)
       self.assertEqual(timm_model.channels       ,     transformers_model.channels				)






    @unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''TimmBackbone initialization is managed on the timm side'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''Safetensors is not supported by timm.'''				)
    def _snake_case     (  self				):
       pass






    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
    def _snake_case     (  self				):
       pass






    def _snake_case     (  self				):
       lowercase__:  List[Any]   = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase__:  List[Any]   = model_class(A_				)
          lowercase__:  str   = inspect.signature(model.forward				)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowercase__:  Optional[int]   = [*signature.parameters.keys()]

          lowercase__:  str   = ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]       ,     A_				)






    def _snake_case     (  self				):
       lowercase__:  Optional[Any]   = self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__:  Union[str, Any]   = True
       lowercase__:  Tuple   = self.has_attentions

       # no need to test all models as different heads yield the same functionality
       lowercase__:  Tuple   = self.all_model_classes[0]
       lowercase__:  List[str]   = model_class(A_				)
       model.to(A_				)

       lowercase__:  Optional[Any]   = self._prepare_for_class(A_       ,     A_				)
       lowercase__:  Union[str, Any]   = model(**A_				)
       lowercase__:  Tuple   = outputs[0][-1]

       # Encoder-/Decoder-only models
       lowercase__:  int   = outputs.hidden_states[0]
       hidden_states.retain_grad()

       if self.has_attentions:
          lowercase__:  str   = outputs.attentions[0]
          attentions.retain_grad()

       output.flatten()[0].backward(retain_graph=A_				)

       self.assertIsNotNone(hidden_states.grad				)

       if self.has_attentions:
          self.assertIsNotNone(attentions.grad				)






    def _snake_case     (  self				):
       lowercase__:  Optional[int]   = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowercase__:  Dict   = model_class(A_				)
          model.to(A_				)
          model.eval()
          lowercase__:  Any   = model(**A_				)

          self.assertEqual(len(result.feature_maps				)       ,     len(config.out_indices				)				)
          self.assertEqual(len(model.channels				)       ,     len(config.out_indices				)				)

          # Check output of last stage is taken if out_features=None, out_indices=None
          lowercase__:  int   = copy.deepcopy(A_				)
          lowercase__:  Optional[Any]   = None
          lowercase__:  str   = model_class(A_				)
          model.to(A_				)
          model.eval()
          lowercase__:  Dict   = model(**A_				)

          self.assertEqual(len(result.feature_maps				)       ,     1				)
          self.assertEqual(len(model.channels				)       ,     1				)

          # Check backbone can be initialized with fresh weights
          lowercase__:  Optional[int]   = copy.deepcopy(A_				)
          lowercase__:  Tuple   = False
          lowercase__:  List[str]   = model_class(A_				)
          model.to(A_				)
          model.eval()
          lowercase__:  int   = model(**A_				)




from PIL import Image




def    a__    (		snake_case__					:							Image						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     (259 * (level + 255)) / (255 * (259 - level))

  def contrast(snake_case__					:							int     ) -> int:
    return int(128 + factor * (c - 128)     )

  return img.point(snake_case__     )


if __name__ == "__main__":
 # Load image
 with Image.open('image_data/lena.jpg') as img:
  # Change contrast to 170
  SCREAMING_SNAKE_CASE__					:     int       =     change_contrast(img, 170)
  cont_img.save('image_data/lena_high_contrast.png', format='png')





