





import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
       from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor







class 			__A       :




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__=13      ,							a__=32      ,							a__=3      ,							a__=4      ,							a__=[10, 20, 30, 40]      ,							a__=[2, 2, 3, 2]      ,							a__=True      ,							a__=True      ,							a__=37      ,							a__="gelu"      ,							a__=10      ,							a__=0.02      ,							a__=["stage2", "stage3", "stage4"]      ,							a__=[2, 3, 4]      ,							a__=None      ,							):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		parent
             _lowerCamelCase							:     Any	=		batch_size
             _lowerCamelCase							:     List[str]	=		image_size
             _lowerCamelCase							:     Dict	=		num_channels
             _lowerCamelCase							:     Dict	=		num_stages
             _lowerCamelCase							:     Dict	=		hidden_sizes
             _lowerCamelCase							:     List[str]	=		depths
             _lowerCamelCase							:     Optional[int]	=		is_training
             _lowerCamelCase							:     Optional[Any]	=		use_labels
             _lowerCamelCase							:     int	=		intermediate_size
             _lowerCamelCase							:     str	=		hidden_act
             _lowerCamelCase							:     Optional[int]	=		num_labels
             _lowerCamelCase							:     Union[str, Any]	=		initializer_range
             _lowerCamelCase							:     Any	=		out_features
             _lowerCamelCase							:     Union[str, Any]	=		out_indices
             _lowerCamelCase							:     Any	=		scope


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

             _lowerCamelCase							:     Optional[int]	=		None
             if self.use_labels:
                    _lowerCamelCase							:     Any	=		ids_tensor([self.batch_size]      ,							self.num_labels)

             _lowerCamelCase							:     Optional[int]	=		self.get_config()
             return config, pixel_values, labels


      def 				__snake_case			(		self):



             """simple docstring"""





             return ConvNextConfig(
                 num_channels=self.num_channels      ,							hidden_sizes=self.hidden_sizes      ,							depths=self.depths      ,							num_stages=self.num_stages      ,							hidden_act=self.hidden_act      ,							is_decoder=a__      ,							initializer_range=self.initializer_range      ,							out_features=self.out_features      ,							out_indices=self.out_indices      ,							num_labels=self.num_labels      ,							)


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		ConvNextModel(config=a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     Dict	=		model(a__)
             # expected last hidden states: B, C, H // 32, W // 32
             self.parent.assertEqual(
                 result.last_hidden_state.shape      ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,							)


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		ConvNextForImageClassification(a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     List[str]	=		model(a__      ,							labels=a__)
             self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels))


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		ConvNextBackbone(config=a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     List[str]	=		model(a__)

             # verify hidden states
             self.parent.assertEqual(len(result.feature_maps)      ,							len(config.out_features))
             self.parent.assertListEqual(list(result.feature_maps[0].shape)      ,							[self.batch_size, self.hidden_sizes[1], 4, 4])

             # verify channels
             self.parent.assertEqual(len(model.channels)      ,							len(config.out_features))
             self.parent.assertListEqual(model.channels      ,							config.hidden_sizes[1:])

             # verify backbone works with out_features=None
             _lowerCamelCase							:     str	=		None
             _lowerCamelCase							:     Optional[Any]	=		ConvNextBackbone(config=a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     List[Any]	=		model(a__)

             # verify feature maps
             self.parent.assertEqual(len(result.feature_maps)      ,							1)
             self.parent.assertListEqual(list(result.feature_maps[0].shape)      ,							[self.batch_size, self.hidden_sizes[-1], 1, 1])

             # verify channels
             self.parent.assertEqual(len(model.channels)      ,							1)
             self.parent.assertListEqual(model.channels      ,							[config.hidden_sizes[-1]])



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.prepare_config_and_inputs()
             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Optional[int]	=		config_and_inputs
             _lowerCamelCase							:     Any	=		{'''pixel_values''': pixel_values}
             return config, inputs_dict







@require_torch
class 			__A       (      lowerCamelCase__ ,lowerCamelCase__ ,unittest.TestCase  ):




      """simple docstring"""







      UpperCAmelCase__						=      (
          (
              ConvNextModel,
              ConvNextForImageClassification,
              ConvNextBackbone,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase__						=      (
          {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCAmelCase__						=      True
      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		ConvNextModelTester(self)
             _lowerCamelCase							:     Tuple	=		ConfigTester(self      ,							config_class=a__      ,							has_text_modality=a__      ,							hidden_size=37)


      def 				__snake_case			(		self):



             """simple docstring"""





             self.create_and_test_config_common_properties()
             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):



             """simple docstring"""





             return


      @unittest.skip(reason='''ConvNext does not use inputs_embeds''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      @unittest.skip(reason='''ConvNext does not support input and output embeddings''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      @unittest.skip(reason='''ConvNext does not use feedforward chunking''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.model_tester.prepare_config_and_inputs_for_common()

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

                    _lowerCamelCase							:     Optional[int]	=		['''pixel_values''']
                    self.assertListEqual(arg_names[:1]      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_backbone(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             def check_hidden_states_output(a__      ,							a__      ,							a__):
                    _lowerCamelCase							:     Any	=		model_class(a__)
                    model.to(a__)
                    model.eval()

                    with torch.no_grad():
                           _lowerCamelCase							:     Tuple	=		model(**self._prepare_for_class(a__      ,							a__))

                    _lowerCamelCase							:     Optional[Any]	=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                    _lowerCamelCase							:     Optional[int]	=		self.model_tester.num_stages
                    self.assertEqual(len(a__)      ,							expected_num_stages + 1)

                    # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                    self.assertListEqual(
                        list(hidden_states[0].shape[-2:])      ,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]      ,							)

             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase							:     List[str]	=		True
                    check_hidden_states_output(a__      ,							a__      ,							a__)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    _lowerCamelCase							:     Optional[Any]	=		True

                    check_hidden_states_output(a__      ,							a__      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a__)



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase							:     Optional[int]	=		ConvNextModel.from_pretrained(a__)
                    self.assertIsNotNone(a__)




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Optional[int]	=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       return image







@require_torch
@require_vision
class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      @cached_property
      def 				__snake_case			(		self):



             """simple docstring"""





             return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224''') if is_vision_available() else None



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224''').to(a__)

             _lowerCamelCase							:     List[str]	=		self.default_image_processor
             _lowerCamelCase							:     Optional[Any]	=		prepare_img()
             _lowerCamelCase							:     Any	=		image_processor(images=a__      ,							return_tensors='''pt''').to(a__)

             # forward pass
             with torch.no_grad():
                    _lowerCamelCase							:     Tuple	=		model(**a__)

             # verify the logits
             _lowerCamelCase							:     str	=		torch.Size((1, 1000))
             self.assertEqual(outputs.logits.shape      ,							a__)

             _lowerCamelCase							:     int	=		torch.tensor([-0.0260, -0.4739, 0.1911]).to(a__)

             self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							a__      ,							atol=1e-4))







@require_torch
class 			__A       (      unittest.TestCase ,lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      (ConvNextBackbone,) if is_torch_available() else ()
      UpperCAmelCase__						=      ConvNextConfig

      UpperCAmelCase__						=      False


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		ConvNextModelTester(self)








import math




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[str]	=		len(lowercase_      )
       _lowerCamelCase							:     Optional[int]	=		int(math.floor(math.sqrt(lowercase_      )      )      )
       _lowerCamelCase							:     Tuple	=		0
       while arr[min(lowercase_ ,    lowercase_      ) - 1] < x:
              _lowerCamelCase							:     Optional[Any]	=		step
              step += int(math.floor(math.sqrt(lowercase_      )      )      )
              if prev >= n:
                     return -1

       while arr[prev] < x:
              _lowerCamelCase							:     int	=		prev + 1
              if prev == min(lowercase_ ,    lowercase_      ):
                     return -1
       if arr[prev] == x:
              return prev
       return -1


if __name__ == "__main__":
       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       _lowerCamelCase			       =      int(input('Enter the number to be searched:\n'))
       _lowerCamelCase			       =      jump_search(arr, x)
       if res == -1:
              print('Number not found!')
       else:
              print(F'''Number {x} is at index {res}''')

