

import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
  from transformers.models.mobilevitva.modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      make_divisible,
  )


if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =self.config_class(**self.inputs_dict					)
        self.parent.assertTrue(hasattr(lowerCAmelCase							,   'width_multiplier'					)					)





class  __lowerCAmelCase     :




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase=13							,   lowerCAmelCase=64							,   lowerCAmelCase=2							,   lowerCAmelCase=3							,   lowerCAmelCase="swish"							,   lowerCAmelCase=3							,   lowerCAmelCase=32							,   lowerCAmelCase=0.1							,   lowerCAmelCase=0.02							,   lowerCAmelCase=True							,   lowerCAmelCase=True							,   lowerCAmelCase=10							,   lowerCAmelCase=None							,   lowerCAmelCase=0.25							,   lowerCAmelCase=0.0							,   lowerCAmelCase=0.0							,   )  -> List[str]:
        '''simple docstring'''






        _lowercase	       =parent
        _lowercase	       =batch_size
        _lowercase	       =image_size
        _lowercase	       =patch_size
        _lowercase	       =num_channels
        _lowercase	       =make_divisible(512 * width_multiplier							,   divisor=8					)
        _lowercase	       =hidden_act
        _lowercase	       =conv_kernel_size
        _lowercase	       =output_stride
        _lowercase	       =classifier_dropout_prob
        _lowercase	       =use_labels
        _lowercase	       =is_training
        _lowercase	       =num_labels
        _lowercase	       =initializer_range
        _lowercase	       =scope
        _lowercase	       =width_multiplier
        _lowercase	       =ffn_dropout
        _lowercase	       =attn_dropout




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






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

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

        _lowercase	       =self.get_config()

        return config, pixel_values, labels, pixel_labels




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        return MobileViTVaConfig(
            image_size=self.image_size							,   patch_size=self.patch_size							,   num_channels=self.num_channels							,   hidden_act=self.hidden_act							,   conv_kernel_size=self.conv_kernel_size							,   output_stride=self.output_stride							,   classifier_dropout_prob=self.classifier_dropout_prob							,   initializer_range=self.initializer_range							,   width_multiplier=self.width_multiplier							,   ffn_dropout=self.ffn_dropout_prob							,   attn_dropout=self.attn_dropout_prob							,   )




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        _lowercase	       =MobileViTVaModel(config=lowerCAmelCase					)
        model.to(lowerCAmelCase					)
        model.eval()
        _lowercase	       =model(lowerCAmelCase					)
        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,
            )							,   )




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Optional[int]:
        '''simple docstring'''






        _lowercase	       =self.num_labels
        _lowercase	       =MobileViTVaForImageClassification(lowerCAmelCase					)
        model.to(lowerCAmelCase					)
        model.eval()
        _lowercase	       =model(lowerCAmelCase							,   labels=lowerCAmelCase					)
        self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.num_labels
        _lowercase	       =MobileViTVaForSemanticSegmentation(lowerCAmelCase					)
        model.to(lowerCAmelCase					)
        model.eval()
        _lowercase	       =model(lowerCAmelCase					)
        self.parent.assertEqual(
            result.logits.shape							,   (
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )							,   )
        _lowercase	       =model(lowerCAmelCase							,   labels=lowerCAmelCase					)
        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   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






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





@require_torch
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			unittest.TestCase							):
  _a									=  (
      (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
      if is_torch_available()
      else ()
  )

  _a									=  (
      {
          """feature-extraction""": MobileViTVaModel,
          """image-classification""": MobileViTVaForImageClassification,
          """image-segmentation""": MobileViTVaForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  _a									=  False
  _a									=  False
  _a									=  False
  _a									=  False




  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =MobileViTVaModelTester(self					)
        _lowercase	       =MobileViTVaConfigTester(self							,   config_class=lowerCAmelCase							,   has_text_modality=lowerCAmelCase					)




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        self.config_tester.run_common_tests()




  @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'					)
  def   A__     (				self					)  -> int:
        '''simple docstring'''






        pass




  @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'					)
  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        pass




  @unittest.skip(reason='MobileViTV2 does not output attentions'					)
  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        pass




  @require_torch_multi_gpu
  @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'					)
  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        pass




  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'					)
  def   A__     (				self					)  -> int:
        '''simple docstring'''






        pass




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        _lowercase       ,       _lowercase	       =self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowercase	       =['pixel_values']
              self.assertListEqual(arg_names[:1]							,   lowerCAmelCase					)




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase					)




  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        def check_hidden_states_output(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					):
              _lowercase	       =model_class(lowerCAmelCase					)
              model.to(lowerCAmelCase					)
              model.eval()

              with torch.no_grad():
                    _lowercase	       =model(**self._prepare_for_class(lowerCAmelCase							,   lowerCAmelCase					)					)

              _lowercase	       =outputs.hidden_states

              _lowercase	       =5
              self.assertEqual(len(lowerCAmelCase					)							,   lowerCAmelCase					)

              # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
              # with the width and height being successively divided by 2.
              _lowercase	       =2
              for i in range(len(lowerCAmelCase					)					):
                    self.assertListEqual(
                        list(hidden_states[i].shape[-2:]					)							,   [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]							,   )
                    divisor *= 2

              self.assertEqual(self.model_tester.output_stride							,   divisor // 2					)

        _lowercase       ,       _lowercase	       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _lowercase	       =True
              check_hidden_states_output(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)

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

              check_hidden_states_output(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase					)




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        _lowercase	       =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase					)




  @slow
  def   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowercase	       =MobileViTVaModel.from_pretrained(lowerCAmelCase					)
              self.assertIsNotNone(lowerCAmelCase					)
def      a      (			)    ->      Any:





      """simple docstring"""



      _lowercase	       =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
      return image







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




  @cached_property
  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        return (
            MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'					)
            if is_vision_available()
            else None
        )




  @slow
  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'					).to(
            lowerCAmelCase					)

        _lowercase	       =self.default_image_processor
        _lowercase	       =prepare_img()
        _lowercase	       =image_processor(images=lowerCAmelCase							,   return_tensors='pt'					).to(lowerCAmelCase					)

        # forward pass
        with torch.no_grad():
              _lowercase	       =model(**lowerCAmelCase					)

        # verify the logits
        _lowercase	       =torch.Size((1, 1_000)					)
        self.assertEqual(outputs.logits.shape							,   lowerCAmelCase					)

        _lowercase	       =torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]					).to(lowerCAmelCase					)

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




  @slow
  def   A__     (				self					)  -> str:
        '''simple docstring'''






        _lowercase	       =MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'					)
        _lowercase	       =model.to(lowerCAmelCase					)

        _lowercase	       =MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'					)

        _lowercase	       =prepare_img()
        _lowercase	       =image_processor(images=lowerCAmelCase							,   return_tensors='pt'					).to(lowerCAmelCase					)

        # forward pass
        with torch.no_grad():
              _lowercase	       =model(**lowerCAmelCase					)
        _lowercase	       =outputs.logits

        # verify the logits
        _lowercase	       =torch.Size((1, 21, 32, 32)					)
        self.assertEqual(logits.shape							,   lowerCAmelCase					)

        _lowercase	       =torch.tensor(
            [
                [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
            ]							,   device=lowerCAmelCase							,   )

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




  @slow
  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'					)
        _lowercase	       =model.to(lowerCAmelCase					)

        _lowercase	       =MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'					)

        _lowercase	       =prepare_img()
        _lowercase	       =image_processor(images=lowerCAmelCase							,   return_tensors='pt'					).to(lowerCAmelCase					)

        # forward pass
        with torch.no_grad():
              _lowercase	       =model(**lowerCAmelCase					)

        _lowercase	       =outputs.logits.detach().cpu()

        _lowercase	       =image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase							,   target_sizes=[(50, 60)]					)
        _lowercase	       =torch.Size((50, 60)					)
        self.assertEqual(segmentation[0].shape							,   lowerCAmelCase					)

        _lowercase	       =image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase					)
        _lowercase	       =torch.Size((32, 32)					)
        self.assertEqual(segmentation[0].shape							,   lowerCAmelCase					)





import os
def      a      (			)    ->      Any:





      """simple docstring"""



      with open(os.path.dirname(A__     ) + '/p022_names.txt'     ) as file:
            _lowercase	       =str(file.readlines()[0]     )
            _lowercase	       =names.replace('"'      ,   ''     ).split(','     )

      names.sort()

      _lowercase	       =0
      _lowercase	       =0

      for i, name in enumerate(A__     ):
            for letter in name:
                  name_score += ord(A__     ) - 64

            total_score += (i + 1) * name_score
            _lowercase	       =0
      return total_score


if __name__ == "__main__":
  print(solution())


