
"""simple docstring"""

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 __lowerCAmelCase		(				__UpperCamelCase      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.config_class(**self.inputs_dict				)
      self.parent.assertTrue(hasattr(__UpperCAmelCase     ,					'tf_padding'				)				)
      self.parent.assertTrue(hasattr(__UpperCAmelCase     ,					'depth_multiplier'				)				)


class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=13     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=0.2_5     ,					__UpperCAmelCase=8     ,					__UpperCAmelCase=8     ,					__UpperCAmelCase=6     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase="relu6"     ,					__UpperCAmelCase=1280     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=None     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	parent
      __UpperCamelCase      =	batch_size
      __UpperCamelCase      =	num_channels
      __UpperCamelCase      =	image_size
      __UpperCamelCase      =	depth_multiplier
      __UpperCamelCase      =	depth_divisible_by
      __UpperCamelCase      =	min_depth
      __UpperCamelCase      =	expand_ratio
      __UpperCamelCase      =	tf_padding
      __UpperCamelCase      =	output_stride
      __UpperCamelCase      =	first_layer_is_expansion
      __UpperCamelCase      =	finegrained_output
      __UpperCamelCase      =	hidden_act
      __UpperCamelCase      =	last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier				)
      __UpperCamelCase      =	classifier_dropout_prob
      __UpperCamelCase      =	use_labels
      __UpperCamelCase      =	is_training
      __UpperCamelCase      =	num_labels
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	scope





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

      __UpperCamelCase      =	None
      __UpperCamelCase      =	None
      if self.use_labels:
       __UpperCamelCase      =	ids_tensor([self.batch_size]     ,					self.num_labels				)
       __UpperCamelCase      =	ids_tensor([self.batch_size, self.image_size, self.image_size]     ,					self.num_labels				)

      __UpperCamelCase      =	self.get_config()

      return config, pixel_values, labels, pixel_labels





     def 						UpperCAmelCase (     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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	MobileNetVaModel(config=__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      __UpperCamelCase      =	model(__UpperCAmelCase				)
      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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.num_labels
      __UpperCamelCase      =	MobileNetVaForImageClassification(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      __UpperCamelCase      =	model(__UpperCAmelCase     ,					labels=__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.num_labels
      __UpperCamelCase      =	MobileNetVaForSemanticSegmentation(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      __UpperCamelCase      =	model(__UpperCAmelCase				)
      self.parent.assertEqual(
          result.logits.shape     ,					(
              self.batch_size,
              self.num_labels,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )     ,					)
      __UpperCamelCase      =	model(__UpperCAmelCase     ,					labels=__UpperCAmelCase				)
      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 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.prepare_config_and_inputs()
      __UpperCamelCase      =	config_and_inputs
      __UpperCamelCase      =	{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class __lowerCAmelCase		(				__UpperCamelCase      ,  __UpperCamelCase      ,  unittest.TestCase      ):
     lowercase						       =						(
         (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     lowercase						       =						(
         {
             "feature-extraction": MobileNetVaModel,
             "image-classification": MobileNetVaForImageClassification,
             "image-segmentation": MobileNetVaForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	MobileNetVaModelTester(self				)
      __UpperCamelCase      =	MobileNetVaConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.config_tester.run_common_tests()





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

      '''simple docstring'''




      pass





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

      '''simple docstring'''




      pass





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

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

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

       __UpperCamelCase      =	['pixel_values']
       self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      def check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()

       with torch.no_grad():
        __UpperCamelCase      =	model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)				)

       __UpperCamelCase      =	outputs.hidden_states

       __UpperCamelCase      =	16
       self.assertEqual(len(__UpperCAmelCase				)     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	True
       check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __UpperCamelCase      =	True

       check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase				)







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase      =	MobileNetVaModel.from_pretrained(__UpperCAmelCase				)
       self.assertIsNotNone(__UpperCAmelCase				)


def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
 return image





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





     @cached_property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return (
          MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'				) if is_vision_available() else None
      )





     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'				).to(__UpperCAmelCase				)

      __UpperCamelCase      =	self.default_image_processor
      __UpperCamelCase      =	prepare_img()
      __UpperCamelCase      =	image_processor(images=__UpperCAmelCase     ,					return_tensors='pt'				).to(__UpperCAmelCase				)

      # forward pass
      with torch.no_grad():
       __UpperCamelCase      =	model(**__UpperCAmelCase				)

      # verify the logits
      __UpperCamelCase      =	torch.Size((1, 1001)				)
      self.assertEqual(outputs.logits.shape     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]				).to(__UpperCAmelCase				)

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







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'				)
      __UpperCamelCase      =	model.to(__UpperCAmelCase				)

      __UpperCamelCase      =	MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'				)

      __UpperCamelCase      =	prepare_img()
      __UpperCamelCase      =	image_processor(images=__UpperCAmelCase     ,					return_tensors='pt'				).to(__UpperCAmelCase				)

      # forward pass
      with torch.no_grad():
       __UpperCamelCase      =	model(**__UpperCAmelCase				)
      __UpperCamelCase      =	outputs.logits

      # verify the logits
      __UpperCamelCase      =	torch.Size((1, 21, 65, 65)				)
      self.assertEqual(logits.shape     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	torch.tensor(
          [
              [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
              [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
              [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
          ]     ,					device=__UpperCAmelCase     ,					)

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





"""simple docstring"""

from math import isqrt






def 							A						(    snake_case				:int		)				->     list[int]:
 __UpperCamelCase      =	[True] * max_number
 for i in range(2    , isqrt(max_number - 1		) + 1		):
  if is_prime[i]:
   for j in range(i**2    , snake_case    , snake_case		):
    __UpperCamelCase      =	False

 return [i for i in range(2    , snake_case		) if is_prime[i]]






def 							A						(    snake_case				:int = 1_0**8		)				->     int:
 __UpperCamelCase      =	calculate_prime_numbers(max_number // 2		)

 __UpperCamelCase      =	0
 __UpperCamelCase      =	0
 __UpperCamelCase      =	len(snake_case		) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


if __name__ == "__main__":
      print(f'''{solution() = }''')



