


"""simple docstring"""


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 torch import nn

   from transformers import SwiftFormerForImageClassification, SwiftFormerModel
   from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor

class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=13 ,     lowerCamelCase__=3 ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=224 ,     lowerCamelCase__=1000 ,     lowerCamelCase__=[3, 3, 6, 4] ,     lowerCamelCase__=[48, 56, 112, 220] ,     )      ->							Optional[int]:
    lowercase__       :       List[str]				=						parent
    lowercase__       :       Any				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Tuple				=						is_training
    lowercase__       :       Optional[int]				=						use_labels
    lowercase__       :       str				=						hidden_dropout_prob
    lowercase__       :       Union[str, Any]				=						attention_probs_dropout_prob
    lowercase__       :       Optional[Any]				=						num_labels
    lowercase__       :       Optional[Any]				=						image_size
    lowercase__       :       Any				=						layer_depths
    lowercase__       :       Dict				=						embed_dims


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Optional[Any]				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

    lowercase__       :       Optional[int]				=						None
    if self.use_labels:
     lowercase__       :       List[str]				=						ids_tensor([self.batch_size] ,     self.num_labels						)

    lowercase__       :       Union[str, Any]				=						self.get_config()

    return config, pixel_values, labels


   def 					UpperCAmelCase__(						self						)      ->							Any:
    return SwiftFormerConfig(
        depths=self.layer_depths ,     embed_dims=self.embed_dims ,     mlp_ratio=4 ,     downsamples=[True, True, True, True] ,     hidden_act="""gelu""" ,     num_labels=self.num_labels ,     down_patch_size=3 ,     down_stride=2 ,     down_pad=1 ,     drop_rate=0.0 ,     drop_path_rate=0.0 ,     use_layer_scale=lowerCamelCase__ ,     layer_scale_init_value=1E-5 ,     )


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       Optional[Any]				=						SwiftFormerModel(config=lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       Any				=						model(lowerCamelCase__						)
    self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.embed_dims[-1], 7, 7)						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       Optional[Any]				=						self.num_labels
    lowercase__       :       Union[str, Any]				=						SwiftFormerForImageClassification(lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       str				=						model(lowerCamelCase__ ,     labels=lowerCamelCase__						)
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)						)

    lowercase__       :       Optional[Any]				=						SwiftFormerForImageClassification(lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()

    lowercase__       :       Any				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
    lowercase__       :       Union[str, Any]				=						model(lowerCamelCase__						)
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)						)







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    ((lowercase__)	,       (lowercase__)	,       (lowercase__))       :       List[Any]				=						self.prepare_config_and_inputs()
    lowercase__       :       Any				=						{"""pixel_values""": pixel_values}
    return config, inputs_dict

@require_torch
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       int									=    (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   _a   :       str									=    (
       {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   _a   :       Dict									=    False
   _a   :       Dict									=    False
   _a   :       Optional[Any]									=    False
   _a   :       List[str]									=    False
   _a   :       Union[str, Any]									=    False


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Tuple				=						SwiftFormerModelTester(self						)
    lowercase__       :       Any				=						ConfigTester(
        self ,     config_class=lowerCamelCase__ ,     has_text_modality=lowerCamelCase__ ,     hidden_size=37 ,     num_attention_heads=12 ,     num_hidden_layers=12 ,     )


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    self.config_tester.run_common_tests()


   @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""						)
   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    pass


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__	,       lowercase__       :       List[Any]				=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     lowercase__       :       str				=						model_class(lowerCamelCase__						)
     lowercase__       :       Optional[Any]				=						model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowerCamelCase__ ,     nn.Linear						)						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__	,       lowercase__       :       Optional[Any]				=						self.model_tester.prepare_config_and_inputs_for_common()

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

     lowercase__       :       Dict				=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       str				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[Any]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__						)


   @slow
   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__       :       Optional[int]				=						SwiftFormerModel.from_pretrained(lowerCamelCase__						)
     self.assertIsNotNone(lowerCamelCase__						)


   @unittest.skip(reason="""SwiftFormer does not output attentions"""						)
   def 					UpperCAmelCase__(						self						)      ->							str:
    pass


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    def check_hidden_states_output(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						):
     lowercase__       :       str				=						model_class(lowerCamelCase__						)
     model.to(lowerCamelCase__						)
     model.eval()

     with torch.no_grad():
      lowercase__       :       Any				=						model(**self._prepare_for_class(lowerCamelCase__ ,     lowerCamelCase__						)						)

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

     lowercase__       :       Optional[Any]				=						8
     self.assertEqual(len(lowerCamelCase__						) ,     lowerCamelCase__						)  # TODO

     # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
     # with the width and height being successively divided by 2, after every 2 blocks
     for i in range(len(lowerCamelCase__						)						):
      self.assertEqual(
          hidden_states[i].shape ,     torch.Size(
              [
                  self.model_tester.batch_size,
                  self.model_tester.embed_dims[i // 2],
                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
                  (self.model_tester.image_size // 4) // 2 ** (i // 2),
              ]						) ,     )

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

    for model_class in self.all_model_classes:
     lowercase__       :       Tuple				=						True
     check_hidden_states_output(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)

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

     check_hidden_states_output(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    def _config_zero_init(lowerCamelCase__						):
     lowercase__       :       Union[str, Any]				=						copy.deepcopy(lowerCamelCase__						)
     for key in configs_no_init.__dict__.keys():
      if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
       setattr(lowerCamelCase__ ,     lowerCamelCase__ ,     1E-10						)
      if isinstance(getattr(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						) ,     lowerCamelCase__						):
       lowercase__       :       str				=						_config_zero_init(getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)
       setattr(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)
     return configs_no_init

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

    lowercase__       :       Tuple				=						_config_zero_init(lowerCamelCase__						)
    for model_class in self.all_model_classes:
     lowercase__       :       List[Any]				=						model_class(config=lowerCamelCase__						)
     for name, param in model.named_parameters():
      if param.requires_grad:
       self.assertIn(
           ((param.data.mean() * 1E9) / 1E9).round().item() ,     [0.0, 1.0] ,     msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,     )







   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    pass


def    _lowerCamelCase   (     ):
 lowercase__       :       List[str]				=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
 return image






@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @cached_property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""						) if is_vision_available() else None







   @slow
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Any				=						SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""						).to(lowerCamelCase__						)

    lowercase__       :       Optional[Any]				=						self.default_image_processor
    lowercase__       :       Any				=						prepare_img()
    lowercase__       :       Any				=						image_processor(images=lowerCamelCase__ ,     return_tensors="""pt"""						).to(lowerCamelCase__						)

    # forward pass
    with torch.no_grad():
     lowercase__       :       Any				=						model(**lowerCamelCase__						)

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

    lowercase__       :       List[str]				=						torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]						).to(lowerCamelCase__						)
    self.assertTrue(torch.allclose(outputs.logits[0, :3] ,     lowerCamelCase__ ,     atol=1E-4						)						)



"""simple docstring"""


import requests
from bsa import BeautifulSoup


def    _lowerCamelCase   (     lowerCamelCase__	:							str = "https://www.worldometers.info/coronavirus"  ):
 lowercase__       :       Optional[Any]				=						BeautifulSoup(requests.get(lowerCamelCase__  ).text       ,		"""html.parser"""  )
 lowercase__       :       Union[str, Any]				=						soup.findAll("""h1"""  )
 lowercase__       :       List[str]				=						soup.findAll("""div"""       ,		{"""class""": """maincounter-number"""}  )
 keys += soup.findAll("""span"""       ,		{"""class""": """panel-title"""}  )
 values += soup.findAll("""div"""       ,		{"""class""": """number-table-main"""}  )
 return {key.text.strip(): value.text.strip() for key, value in zip(lowerCamelCase__       ,		lowerCamelCase__  )}


if __name__ == "__main__":
   print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
   for key, value in world_covidaa_stats().items():
      print(F"{key}\n{value}\n")