






'''simple docstring'''





import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import YolosForObjectDetection, YolosModel
       from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor







class  _lowercase   :
      def __init__(     self				,			a				,			a=1_3				,			a=[3_0, 3_0]				,			a=2				,			a=3				,			a=True				,			a=True				,			a=3_2				,			a=5				,			a=4				,			a=3_7				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=1_0				,			a=0.02				,			a=3				,			a=None				,			a=8				,			a=1_0				,			):
       snake_case__  :    Dict							       =parent
       snake_case__  :    Dict							       =batch_size
       snake_case__  :    Optional[Any]							       =image_size
       snake_case__  :    int							       =patch_size
       snake_case__  :    Optional[Any]							       =num_channels
       snake_case__  :    List[Any]							       =is_training
       snake_case__  :    Union[str, Any]							       =use_labels
       snake_case__  :    Any							       =hidden_size
       snake_case__  :    Tuple							       =num_hidden_layers
       snake_case__  :    str							       =num_attention_heads
       snake_case__  :    Optional[Any]							       =intermediate_size
       snake_case__  :    Tuple							       =hidden_act
       snake_case__  :    Optional[Any]							       =hidden_dropout_prob
       snake_case__  :    Optional[Any]							       =attention_probs_dropout_prob
       snake_case__  :    List[str]							       =type_sequence_label_size
       snake_case__  :    Dict							       =initializer_range
       snake_case__  :    List[Any]							       =num_labels
       snake_case__  :    str							       =scope
       snake_case__  :    Optional[int]							       =n_targets
       snake_case__  :    Optional[int]							       =num_detection_tokens
       # we set the expected sequence length (which is used in several tests)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
       snake_case__  :    Any							       =(image_size[1] // patch_size) * (image_size[0] // patch_size)
       snake_case__  :    Optional[Any]							       =num_patches + 1 + self.num_detection_tokens
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]   )

       snake_case__  :    Dict							       =None
       if self.use_labels:
        # labels is a list of Dict (each Dict being the labels for a given example in the batch)
        snake_case__  :    Any							       =[]
        for i in range(self.batch_size   ):
         snake_case__  :    Optional[Any]							       ={}
         snake_case__  :    Union[str, Any]							       =torch.randint(
             high=self.num_labels				,			size=(self.n_targets,)				,			device=a   )
         snake_case__  :    str							       =torch.rand(self.n_targets				,			4				,			device=a   )
         labels.append(a   )

       snake_case__  :    Dict							       =self.get_config()

       return config, pixel_values, labels
      def 						lowercase__					(     self   ):
       return YolosConfig(
           image_size=self.image_size				,			patch_size=self.patch_size				,			num_channels=self.num_channels				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			is_decoder=a				,			initializer_range=self.initializer_range				,			num_detection_tokens=self.num_detection_tokens				,			num_labels=self.num_labels				,			)
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    Tuple							       =YolosModel(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    str							       =model(a   )
       self.parent.assertEqual(
           result.last_hidden_state.shape				,			(self.batch_size, self.expected_seq_len, self.hidden_size)   )
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    List[str]							       =YolosForObjectDetection(a   )
       model.to(a   )
       model.eval()

       snake_case__  :    Optional[int]							       =model(pixel_values=a   )
       snake_case__  :    str							       =model(a   )

       self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1)   )
       self.parent.assertEqual(result.pred_boxes.shape				,			(self.batch_size, self.num_detection_tokens, 4)   )

       snake_case__  :    List[Any]							       =model(pixel_values=a				,			labels=a   )

       self.parent.assertEqual(result.loss.shape				,			()   )
       self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1)   )
       self.parent.assertEqual(result.pred_boxes.shape				,			(self.batch_size, self.num_detection_tokens, 4)   )



      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =self.prepare_config_and_inputs()
       snake_case__  :    Optional[int]							       =config_and_inputs
       snake_case__  :    Union[str, Any]							       ={'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class  _lowercase   (   UpperCamelCase_						,				UpperCamelCase_						,				unittest.TestCase						):
      _a							:						Union[str, Any]										=							(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
      _a							:						Tuple										=							(
          {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
      )

      _a							:						Optional[int]										=							False
      _a							:						Dict										=							False
      _a							:						List[str]										=							False
      _a							:						int										=							False
      def 						lowercase__					(     self				,			a				,			a				,			a=False   ):
       snake_case__  :    Optional[int]							       =super()._prepare_for_class(a				,			a				,			return_labels=a   )

       if return_labels:
        if model_class.__name__ == "YolosForObjectDetection":
         snake_case__  :    Any							       =[]
         for i in range(self.model_tester.batch_size   ):
          snake_case__  :    List[Any]							       ={}
          snake_case__  :    int							       =torch.ones(
              size=(self.model_tester.n_targets,)				,			device=a				,			dtype=torch.long   )
          snake_case__  :    Union[str, Any]							       =torch.ones(
              self.model_tester.n_targets				,			4				,			device=a				,			dtype=torch.float   )
          labels.append(a   )
         snake_case__  :    Union[str, Any]							       =labels

       return inputs_dict
      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =YolosModelTester(self   )
       snake_case__  :    str							       =ConfigTester(self				,			config_class=a				,			has_text_modality=a				,			hidden_size=3_7   )
      def 						lowercase__					(     self   ):
       self.config_tester.run_common_tests()
      def 						lowercase__					(     self   ):
       # YOLOS does not use inputs_embeds
       pass
      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        snake_case__  :    Dict							       =model_class(a   )
        self.assertIsInstance(model.get_input_embeddings()				,			(nn.Module)   )
        snake_case__  :    List[str]							       =model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(a				,			nn.Linear   )   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =self.model_tester.prepare_config_and_inputs_for_common()

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

        snake_case__  :    List[str]							       =['''pixel_values''']
        self.assertListEqual(arg_names[:1]				,			a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =self.model_tester.prepare_config_and_inputs_for_common()
       snake_case__  :    Union[str, Any]							       =True

       # in YOLOS, the seq_len is different
       snake_case__  :    List[str]							       =self.model_tester.expected_seq_len
       for model_class in self.all_model_classes:
        snake_case__  :    List[Any]							       =True
        snake_case__  :    Optional[int]							       =False
        snake_case__  :    Any							       =True
        snake_case__  :    int							       =model_class(a   )
        model.to(a   )
        model.eval()
        with torch.no_grad():
         snake_case__  :    int							       =model(**self._prepare_for_class(a				,			a   )   )
        snake_case__  :    Optional[Any]							       =outputs.attentions
        self.assertEqual(len(a   )				,			self.model_tester.num_hidden_layers   )

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        snake_case__  :    Optional[int]							       =True
        snake_case__  :    str							       =model_class(a   )
        model.to(a   )
        model.eval()
        with torch.no_grad():
         snake_case__  :    List[Any]							       =model(**self._prepare_for_class(a				,			a   )   )
        snake_case__  :    Any							       =outputs.attentions
        self.assertEqual(len(a   )				,			self.model_tester.num_hidden_layers   )

        self.assertListEqual(
            list(attentions[0].shape[-3:]   )				,			[self.model_tester.num_attention_heads, seq_len, seq_len]				,			)
        snake_case__  :    Optional[int]							       =len(a   )

        # Check attention is always last and order is fine
        snake_case__  :    Tuple							       =True
        snake_case__  :    Union[str, Any]							       =True
        snake_case__  :    Union[str, Any]							       =model_class(a   )
        model.to(a   )
        model.eval()
        with torch.no_grad():
         snake_case__  :    Any							       =model(**self._prepare_for_class(a				,			a   )   )

        snake_case__  :    Dict							       =1
        self.assertEqual(out_len + added_hidden_states				,			len(a   )   )

        snake_case__  :    int							       =outputs.attentions

        self.assertEqual(len(a   )				,			self.model_tester.num_hidden_layers   )
        self.assertListEqual(
            list(self_attentions[0].shape[-3:]   )				,			[self.model_tester.num_attention_heads, seq_len, seq_len]				,			)
      def 						lowercase__					(     self   ):
       def check_hidden_states_output(a				,			a				,			a   ):
        snake_case__  :    List[Any]							       =model_class(a   )
        model.to(a   )
        model.eval()

        with torch.no_grad():
         snake_case__  :    str							       =model(**self._prepare_for_class(a				,			a   )   )

        snake_case__  :    Tuple							       =outputs.hidden_states

        snake_case__  :    Optional[int]							       =getattr(
            self.model_tester				,			"""expected_num_hidden_layers"""				,			self.model_tester.num_hidden_layers + 1   )
        self.assertEqual(len(a   )				,			a   )

        # YOLOS has a different seq_length
        snake_case__  :    List[str]							       =self.model_tester.expected_seq_len

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]   )				,			[seq_length, self.model_tester.hidden_size]				,			)

       snake_case__  :    Tuple							       =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        snake_case__  :    Union[str, Any]							       =True
        check_hidden_states_output(a				,			a				,			a   )

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

        check_hidden_states_output(a				,			a				,			a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_object_detection(*a   )



      @slow
      def 						lowercase__					(     self   ):
       for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case__  :    Tuple							       =YolosModel.from_pretrained(a   )
        self.assertIsNotNone(a   )







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    List[Any]							       =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
 return image







@require_torch
@require_vision
class  _lowercase   (   unittest.TestCase						):
      @cached_property
      def 						lowercase__					(     self   ):
       return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""   ) if is_vision_available() else None



      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""   ).to(a   )

       snake_case__  :    Dict							       =self.default_image_processor
       snake_case__  :    List[Any]							       =prepare_img()
       snake_case__  :    Union[str, Any]							       =image_processor(images=a				,			return_tensors="""pt"""   ).to(a   )

       # forward pass
       with torch.no_grad():
        snake_case__  :    List[str]							       =model(inputs.pixel_values   )

       # verify outputs
       snake_case__  :    Union[str, Any]							       =torch.Size((1, 1_0_0, 9_2)   )
       self.assertEqual(outputs.logits.shape				,			a   )

       snake_case__  :    Dict							       =torch.tensor(
           [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]				,			device=a				,			)
       snake_case__  :    Optional[Any]							       =torch.tensor(
           [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]				,			device=a   )
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,			a				,			atol=1e-4   )   )
       self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]				,			a				,			atol=1e-4   )   )

       # verify postprocessing
       snake_case__  :    List[str]							       =image_processor.post_process_object_detection(
           a				,			threshold=0.3				,			target_sizes=[image.size[::-1]]   )[0]
       snake_case__  :    List[str]							       =torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]   ).to(a   )
       snake_case__  :    str							       =[7_5, 7_5, 1_7, 6_3, 1_7]
       snake_case__  :    str							       =torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]   ).to(a   )

       self.assertEqual(len(results["""scores"""]   )				,			5   )
       self.assertTrue(torch.allclose(results["""scores"""]				,			a				,			atol=1e-4   )   )
       self.assertSequenceEqual(results["""labels"""].tolist()				,			a   )
       self.assertTrue(torch.allclose(results["""boxes"""][0, :]				,			a   )   )













from collections import namedtuple

import requests
from lxml import html  # type: ignore

__lowerCamelCase	:		Any										=   namedtuple("""covid_data""", """cases deaths recovered""")







def 			A__							(      _a  :					str = "https://www.worldometers.info/coronavirus/"	):





 '''simple docstring'''







 snake_case__  :    Any							       ="""//div[@class = \"maincounter-number\"]/span/text()"""
 return covid_data(*html.fromstring(requests.get(_a	).content	).xpath(_a	)	)


__lowerCamelCase	:		str										=   """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""

print(fmt.format(*covid_stats()))





