





import inspect
import unittest

from transformers import MobileNetVaConfig
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_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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
       from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import MobileNetVaImageProcessor







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		self.config_class(**self.inputs_dict)
             self.parent.assertTrue(hasattr(a__      ,							'''tf_padding'''))
             self.parent.assertTrue(hasattr(a__      ,							'''depth_multiplier'''))







class 			__A       :




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__=13      ,							a__=3      ,							a__=32      ,							a__=0.25      ,							a__=8      ,							a__=8      ,							a__=6      ,							a__=32      ,							a__=True      ,							a__=True      ,							a__=True      ,							a__="relu6"      ,							a__=1280      ,							a__=0.1      ,							a__=0.02      ,							a__=True      ,							a__=True      ,							a__=10      ,							a__=None      ,							):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		parent
             _lowerCamelCase							:     List[str]	=		batch_size
             _lowerCamelCase							:     Dict	=		num_channels
             _lowerCamelCase							:     Dict	=		image_size
             _lowerCamelCase							:     Optional[int]	=		depth_multiplier
             _lowerCamelCase							:     Dict	=		depth_divisible_by
             _lowerCamelCase							:     Optional[int]	=		min_depth
             _lowerCamelCase							:     int	=		expand_ratio
             _lowerCamelCase							:     List[Any]	=		tf_padding
             _lowerCamelCase							:     int	=		output_stride
             _lowerCamelCase							:     Optional[int]	=		first_layer_is_expansion
             _lowerCamelCase							:     Optional[Any]	=		finegrained_output
             _lowerCamelCase							:     str	=		hidden_act
             _lowerCamelCase							:     int	=		last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier)
             _lowerCamelCase							:     Any	=		classifier_dropout_prob
             _lowerCamelCase							:     Optional[Any]	=		use_labels
             _lowerCamelCase							:     str	=		is_training
             _lowerCamelCase							:     List[str]	=		num_labels
             _lowerCamelCase							:     Optional[Any]	=		initializer_range
             _lowerCamelCase							:     Any	=		scope


      def 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Union[str, Any]	=		None
             _lowerCamelCase							:     Optional[Any]	=		None
             if self.use_labels:
                    _lowerCamelCase							:     List[Any]	=		ids_tensor([self.batch_size]      ,							self.num_labels)
                    _lowerCamelCase							:     Union[str, Any]	=		ids_tensor([self.batch_size, self.image_size, self.image_size]      ,							self.num_labels)

             _lowerCamelCase							:     List[str]	=		self.get_config()

             return config, pixel_values, labels, pixel_labels


      def 				__snake_case			(		self):



             """simple docstring"""





             return MobileNetVaConfig(
                 num_channels=self.num_channels      ,							image_size=self.image_size      ,							depth_multiplier=self.depth_multiplier      ,							depth_divisible_by=self.depth_divisible_by      ,							min_depth=self.min_depth      ,							expand_ratio=self.expand_ratio      ,							output_stride=self.output_stride      ,							first_layer_is_expansion=self.first_layer_is_expansion      ,							finegrained_output=self.finegrained_output      ,							hidden_act=self.hidden_act      ,							tf_padding=self.tf_padding      ,							classifier_dropout_prob=self.classifier_dropout_prob      ,							initializer_range=self.initializer_range      ,							)


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



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		MobileNetVaModel(config=a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     Dict	=		model(a__)
             self.parent.assertEqual(
                 result.last_hidden_state.shape      ,							(
                     self.batch_size,
                     self.last_hidden_size,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 )      ,							)
             self.parent.assertEqual(
                 result.pooler_output.shape      ,							(self.batch_size, self.last_hidden_size)      ,							)


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



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.num_labels
             _lowerCamelCase							:     Optional[int]	=		MobileNetVaForImageClassification(a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     Dict	=		model(a__      ,							labels=a__)
             self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels))


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



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		self.num_labels
             _lowerCamelCase							:     int	=		MobileNetVaForSemanticSegmentation(a__)
             model.to(a__)
             model.eval()
             _lowerCamelCase							:     Optional[Any]	=		model(a__)
             self.parent.assertEqual(
                 result.logits.shape      ,							(
                     self.batch_size,
                     self.num_labels,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 )      ,							)
             _lowerCamelCase							:     Optional[int]	=		model(a__      ,							labels=a__)
             self.parent.assertEqual(
                 result.logits.shape      ,							(
                     self.batch_size,
                     self.num_labels,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 )      ,							)



      def 				__snake_case			(		self):



             """simple docstring"""





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







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




      """simple docstring"""







      UpperCAmelCase__						=      (
          (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      UpperCAmelCase__						=      (
          {
              """feature-extraction""": MobileNetVaModel,
              """image-classification""": MobileNetVaForImageClassification,
              """image-segmentation""": MobileNetVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False
      UpperCAmelCase__						=      False


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		MobileNetVaModelTester(self)
             _lowerCamelCase							:     Optional[int]	=		MobileNetVaConfigTester(self      ,							config_class=a__      ,							has_text_modality=a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             self.config_tester.run_common_tests()


      @unittest.skip(reason='''MobileNetV2 does not use inputs_embeds''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      @unittest.skip(reason='''MobileNetV2 does not support input and output embeddings''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      @unittest.skip(reason='''MobileNetV2 does not output attentions''')
      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		self.model_tester.prepare_config_and_inputs_for_common()

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





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

                    with torch.no_grad():
                           _lowerCamelCase							:     Optional[int]	=		model(**self._prepare_for_class(a__      ,							a__))

                    _lowerCamelCase							:     Union[str, Any]	=		outputs.hidden_states

                    _lowerCamelCase							:     Optional[int]	=		16
                    self.assertEqual(len(a__)      ,							a__)

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

             for model_class in self.all_model_classes:
                    _lowerCamelCase							:     Tuple	=		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[int]	=		True

                    check_hidden_states_output(a__      ,							a__      ,							a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*a__)



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase							:     Union[str, Any]	=		MobileNetVaModel.from_pretrained(a__)
                    self.assertIsNotNone(a__)




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     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 (
                 MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224''') if is_vision_available() else None
             )


      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224''').to(a__)

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

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

             # verify the logits
             _lowerCamelCase							:     Tuple	=		torch.Size((1, 1001))
             self.assertEqual(outputs.logits.shape      ,							a__)

             _lowerCamelCase							:     Optional[Any]	=		torch.tensor([0.2445, -1.1993, 0.1905]).to(a__)

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



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''')
             _lowerCamelCase							:     Dict	=		model.to(a__)

             _lowerCamelCase							:     Tuple	=		MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513''')

             _lowerCamelCase							:     Union[str, Any]	=		prepare_img()
             _lowerCamelCase							:     int	=		image_processor(images=a__      ,							return_tensors='''pt''').to(a__)

             # forward pass
             with torch.no_grad():
                    _lowerCamelCase							:     str	=		model(**a__)
             _lowerCamelCase							:     int	=		outputs.logits

             # verify the logits
             _lowerCamelCase							:     str	=		torch.Size((1, 21, 65, 65))
             self.assertEqual(logits.shape      ,							a__)

             _lowerCamelCase							:     Dict	=		torch.tensor(
                 [
                     [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                     [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                     [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                 ]      ,							device=a__      ,							)

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








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}''')

