


"""simple docstring"""







from collections import deque
def _A						(						lowercase		):







    """simple docstring"""
    a					=len(lowercase		)
    a					=deque()
    a					=[False for _ in range(lowercase		)]
    a					=[-1 for _ in range(lowercase		)]
    a					=index_of[:]

    def strong_connect(lowercase	,   lowercase	,   lowercase		):
        a					=index  # the number when this node is seen
        a					=index  # lowest rank node reachable from here
        index += 1
        stack.append(lowercase		)
        a					=True

        for w in g[v]:
            if index_of[w] == -1:
                a					=strong_connect(lowercase	,   lowercase	,   lowercase		)
                a					=(
                    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                )
            elif on_stack[w]:
                a					=(
                    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                )

        if lowlink_of[v] == index_of[v]:
            a					=[]
            a					=stack.pop()
            a					=False
            component.append(lowercase		)
            while w != v:
                a					=stack.pop()
                a					=False
                component.append(lowercase		)
            components.append(lowercase		)
        return index

    a					=[]
    for v in range(lowercase		):
        if index_of[v] == -1:
            strong_connect(lowercase	,   0	,   lowercase		)

    return components
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a					=[[] for _ in range(lowercase		)]
    for u, v in edges:
        g[u].append(lowercase		)
    return g


if __name__ == "__main__":
       # Test
       lowerCamelCase_       :       str      						=  7
       lowerCamelCase_       :       str      						=  [0, 0, 1, 2, 3, 3, 4, 4, 6]
       lowerCamelCase_       :       Optional[int]      						=  [1, 3, 2, 0, 1, 4, 5, 6, 5]
       lowerCamelCase_       :       str      						=  [(u, v) for u, v in zip(source, target)]
       lowerCamelCase_       :       Any      						=  create_graph(n_vertices, edges)

       assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)



"""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 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A			,   __A=13			,   __A=[30, 30]			,   __A=2			,   __A=3			,   __A=True			,   __A=True			,   __A=32			,   __A=5			,   __A=4			,   __A=37			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=10			,   __A=0.02			,   __A=3			,   __A=None			,   __A=8			,   __A=10			,   ) ->					List[Any]:
     a					=parent
     a					=batch_size
     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=is_training
     a					=use_labels
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=type_sequence_label_size
     a					=initializer_range
     a					=num_labels
     a					=scope
     a					=n_targets
     a					=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
     a					=(image_size[1] // patch_size) * (image_size[0] // patch_size)
     a					=num_patches + 1 + self.num_detection_tokens


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)

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

     a					=self.get_config()

     return config, pixel_values, labels


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					List[Any]:
     a					=YolosModel(config=__A						)
     model.to(__A						)
     model.eval()
     a					=model(__A						)
     self.parent.assertEqual(
         result.last_hidden_state.shape			,   (self.batch_size, self.expected_seq_len, self.hidden_size)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Dict:
     a					=YolosForObjectDetection(__A						)
     model.to(__A						)
     model.eval()

     a					=model(pixel_values=__A						)
     a					=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)						)

     a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.prepare_config_and_inputs()
     a		,				a		,				a					=config_and_inputs
     a					={'''pixel_values''': pixel_values}
     return config, inputs_dict






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






 """simple docstring"""




 __lowerCAmelCase			 =  (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
 __lowerCAmelCase			 =  (
     {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
 )

 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A=False						) ->					Any:
     a					=super()._prepare_for_class(__A			,   __A			,   return_labels=__A						)

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

     return inputs_dict


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=YolosModelTester(self						)
     a					=ConfigTester(self			,   config_class=__A			,   has_text_modality=__A			,   hidden_size=37						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self.config_tester.run_common_tests()


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     # YOLOS does not use inputs_embeds
     pass


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

     for model_class in self.all_model_classes:
         a					=model_class(__A						)
         self.assertIsInstance(model.get_input_embeddings()			,   (nn.Module)						)
         a					=model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__A			,   nn.Linear						)						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

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

         a					=['''pixel_values''']
         self.assertListEqual(arg_names[:1]			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()
     a					=True

     # in YOLOS, the seq_len is different
     a					=self.model_tester.expected_seq_len
     for model_class in self.all_model_classes:
         a					=True
         a					=False
         a					=True
         a					=model_class(__A						)
         model.to(__A						)
         model.eval()
         with torch.no_grad():
             a					=model(**self._prepare_for_class(__A			,   __A						)						)
         a					=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"]
         a					=True
         a					=model_class(__A						)
         model.to(__A						)
         model.eval()
         with torch.no_grad():
             a					=model(**self._prepare_for_class(__A			,   __A						)						)
         a					=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]			,   )
         a					=len(__A						)

         # Check attention is always last and order is fine
         a					=True
         a					=True
         a					=model_class(__A						)
         model.to(__A						)
         model.eval()
         with torch.no_grad():
             a					=model(**self._prepare_for_class(__A			,   __A						)						)

         a					=1
         self.assertEqual(out_len + added_hidden_states			,   len(__A						)						)

         a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     def check_hidden_states_output(__A			,   __A			,   __A						):
         a					=model_class(__A						)
         model.to(__A						)
         model.eval()

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

         a					=outputs.hidden_states

         a					=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
         a					=self.model_tester.expected_seq_len

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

     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         a					=True
         check_hidden_states_output(__A			,   __A			,   __A						)

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

         check_hidden_states_output(__A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_object_detection(*__A						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a					=YolosModel.from_pretrained(__A						)
         self.assertIsNotNone(__A						)

def _A						(						):







    """simple docstring"""
    a					=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image






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






 """simple docstring"""




 @cached_property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''						) if is_vision_available() else None





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''						).to(__A						)

     a					=self.default_image_processor
     a					=prepare_img()
     a					=image_processor(images=__A			,   return_tensors='''pt'''						).to(__A						)

     # forward pass
     with torch.no_grad():
         a					=model(inputs.pixel_values						)

     # verify outputs
     a					=torch.Size((1, 100, 92)						)
     self.assertEqual(outputs.logits.shape			,   __A						)

     a					=torch.tensor(
         [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]			,   device=__A			,   )
     a					=torch.tensor(
         [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]			,   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
     a					=image_processor.post_process_object_detection(
         __A			,   threshold=0.3			,   target_sizes=[image.size[::-1]]						)[0]
     a					=torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]						).to(__A						)
     a					=[75, 75, 17, 63, 17]
     a					=torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]						).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						)						)