


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__	:

       def __init__(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Dict=1_3      ,							lowerCamelCase_      :     Dict=[3_0, 3_0]      ,							lowerCamelCase_      :     List[Any]=2      ,							lowerCamelCase_      :     Dict=3      ,							lowerCamelCase_      :     Optional[Any]=True      ,							lowerCamelCase_      :     Tuple=True      ,							lowerCamelCase_      :     List[Any]=3_2      ,							lowerCamelCase_      :     List[Any]=5      ,							lowerCamelCase_      :     Union[str, Any]=4      ,							lowerCamelCase_      :     Optional[Any]=3_7      ,							lowerCamelCase_      :     Optional[int]="gelu"      ,							lowerCamelCase_      :     int=0.1      ,							lowerCamelCase_      :     Any=0.1      ,							lowerCamelCase_      :     str=1_0      ,							lowerCamelCase_      :     str=0.0_2      ,							lowerCamelCase_      :     Optional[int]=3      ,							lowerCamelCase_      :     Tuple=None      ,							lowerCamelCase_      :     List[str]=8      ,							lowerCamelCase_      :     int=1_0      ,							):
            a_    :     int										=    parent
            a_    :     Any										=    batch_size
            a_    :     str										=    image_size
            a_    :     int										=    patch_size
            a_    :     List[Any]										=    num_channels
            a_    :     Optional[int]										=    is_training
            a_    :     int										=    use_labels
            a_    :     List[str]										=    hidden_size
            a_    :     str										=    num_hidden_layers
            a_    :     Tuple										=    num_attention_heads
            a_    :     Optional[int]										=    intermediate_size
            a_    :     Any										=    hidden_act
            a_    :     int										=    hidden_dropout_prob
            a_    :     Union[str, Any]										=    attention_probs_dropout_prob
            a_    :     int										=    type_sequence_label_size
            a_    :     Dict										=    initializer_range
            a_    :     Tuple										=    num_labels
            a_    :     List[str]										=    scope
            a_    :     str										=    n_targets
            a_    :     Tuple										=    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_    :     Optional[int]										=    (image_size[1] // patch_size) * (image_size[0] // patch_size)
            a_    :     List[Any]										=    num_patches + 1 + self.num_detection_tokens

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     Tuple										=    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]    )

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

            a_    :     Tuple										=    self.get_config()

            return config, pixel_values, labels

       def 						UpperCAmelCase(   self      :     str    ):
            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=lowerCamelCase_      ,							initializer_range=self.initializer_range      ,							num_detection_tokens=self.num_detection_tokens      ,							num_labels=self.num_labels      ,							)

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Union[str, Any]    ):
            a_    :     Any										=    YolosModel(config=lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     Any										=    model(lowerCamelCase_    )
            self.parent.assertEqual(
                result.last_hidden_state.shape      ,							(self.batch_size, self.expected_seq_len, self.hidden_size)    )

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Tuple    ):
            a_    :     int										=    YolosForObjectDetection(lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()

            a_    :     Tuple										=    model(pixel_values=lowerCamelCase_    )
            a_    :     str										=    model(lowerCamelCase_    )

            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_    :     Dict										=    model(pixel_values=lowerCamelCase_      ,							labels=lowerCamelCase_    )

            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 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Optional[int]										=    self.prepare_config_and_inputs()
            a_    :     Union[str, Any]										=    config_and_inputs
            a_    :     List[Any]										=    {"""pixel_values""": pixel_values}
            return config, inputs_dict


@require_torch
class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Tuple							    =     (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
       lowerCamelCase__:     int							    =     (
           {"""feature-extraction""": YolosModel, """object-detection""": YolosForObjectDetection} if is_torch_available() else {}
       )

       lowerCamelCase__:     Optional[Any]							    =     False
       lowerCamelCase__:     int							    =     False
       lowerCamelCase__:     Dict							    =     False
       lowerCamelCase__:     Optional[int]							    =     False

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[int]=False    ):
            a_    :     List[str]										=    super()._prepare_for_class(lowerCamelCase_      ,							lowerCamelCase_      ,							return_labels=lowerCamelCase_    )

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

            return inputs_dict

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     Dict										=    YolosModelTester(self    )
            a_    :     Any										=    ConfigTester(self      ,							config_class=lowerCamelCase_      ,							has_text_modality=lowerCamelCase_      ,							hidden_size=3_7    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            self.config_tester.run_common_tests()

       def 						UpperCAmelCase(   self      :     List[str]    ):
            # YOLOS does not use inputs_embeds
            pass

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     Union[str, Any]										=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 a_    :     str										=    model_class(lowerCamelCase_    )
                 self.assertIsInstance(model.get_input_embeddings()      ,							(nn.Module)    )
                 a_    :     Union[str, Any]										=    model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowerCamelCase_      ,							nn.Linear    )    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     Any										=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 a_    :     Union[str, Any]										=    model_class(lowerCamelCase_    )
                 a_    :     Any										=    inspect.signature(model.forward    )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a_    :     int										=    [*signature.parameters.keys()]

                 a_    :     List[Any]										=    ["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     Optional[Any]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_    :     Optional[int]										=    self.model_tester.prepare_config_and_inputs_for_common()
            a_    :     Optional[Any]										=    True

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

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

                 self.assertListEqual(
                     list(attentions[0].shape[-3:]    )      ,							[self.model_tester.num_attention_heads, seq_len, seq_len]      ,							)
                 a_    :     Union[str, Any]										=    len(lowerCamelCase_    )

                 # Check attention is always last and order is fine
                 a_    :     Optional[Any]										=    True
                 a_    :     int										=    True
                 a_    :     Any										=    model_class(lowerCamelCase_    )
                 model.to(lowerCamelCase_    )
                 model.eval()
                 with torch.no_grad():
                      a_    :     Dict										=    model(**self._prepare_for_class(lowerCamelCase_      ,							lowerCamelCase_    )    )

                 a_    :     List[str]										=    1
                 self.assertEqual(out_len + added_hidden_states      ,							len(lowerCamelCase_    )    )

                 a_    :     Tuple										=    outputs.attentions

                 self.assertEqual(len(lowerCamelCase_    )      ,							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 						UpperCAmelCase(   self      :     List[str]    ):
            def check_hidden_states_output(lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     int    ):
                 a_    :     Tuple										=    model_class(lowerCamelCase_    )
                 model.to(lowerCamelCase_    )
                 model.eval()

                 with torch.no_grad():
                      a_    :     Optional[int]										=    model(**self._prepare_for_class(lowerCamelCase_      ,							lowerCamelCase_    )    )

                 a_    :     Optional[int]										=    outputs.hidden_states

                 a_    :     Dict										=    getattr(
                     self.model_tester      ,							"""expected_num_hidden_layers"""      ,							self.model_tester.num_hidden_layers + 1    )
                 self.assertEqual(len(lowerCamelCase_    )      ,							lowerCamelCase_    )

                 # YOLOS has a different seq_length
                 a_    :     Optional[int]										=    self.model_tester.expected_seq_len

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

            a_    :     int										=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 a_    :     List[Any]										=    True
                 check_hidden_states_output(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )

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

                 check_hidden_states_output(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     int										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_object_detection(*lowerCamelCase_    )

       @slow
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a_    :     Optional[int]										=    YolosModel.from_pretrained(lowerCamelCase_    )
                 self.assertIsNotNone(lowerCamelCase_    )



def  _a			(						):
     a_    :     Optional[int]										=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
     return image


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

       @cached_property
       def 						UpperCAmelCase(   self      :     Any    ):
            return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""    ) if is_vision_available() else None

       @slow
       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     List[Any]										=    YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""    ).to(lowerCamelCase_    )

            a_    :     Tuple										=    self.default_image_processor
            a_    :     int										=    prepare_img()
            a_    :     int										=    image_processor(images=lowerCamelCase_      ,							return_tensors="""pt"""    ).to(lowerCamelCase_    )

            # forward pass
            with torch.no_grad():
                 a_    :     Optional[int]										=    model(inputs.pixel_values    )

            # verify outputs
            a_    :     Tuple										=    torch.Size((1, 1_0_0, 9_2)    )
            self.assertEqual(outputs.logits.shape      ,							lowerCamelCase_    )

            a_    :     Union[str, Any]										=    torch.tensor(
                [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]      ,							device=lowerCamelCase_      ,							)
            a_    :     str										=    torch.tensor(
                [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]      ,							device=lowerCamelCase_    )
            self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      ,							lowerCamelCase_      ,							atol=1E-4    )    )
            self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]      ,							lowerCamelCase_      ,							atol=1E-4    )    )

            # verify postprocessing
            a_    :     Any										=    image_processor.post_process_object_detection(
                lowerCamelCase_      ,							threshold=0.3      ,							target_sizes=[image.size[::-1]]    )[0]
            a_    :     Union[str, Any]										=    torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]    ).to(lowerCamelCase_    )
            a_    :     Union[str, Any]										=    [7_5, 7_5, 1_7, 6_3, 1_7]
            a_    :     List[str]										=    torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]    ).to(lowerCamelCase_    )

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







from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def  _a			(						__UpperCamelCase ):
     a_    :     int										=    int(number**0.5 )
     return number == sq * sq



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     int										=    x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
     a_    :     int										=    x_den * y_den * z_den
     a_    :     int										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
     top //= hcf
     bottom //= hcf
     return top, bottom



def  _a			(						__UpperCamelCase = 3_5 ):
     a_    :     set										=    set()
     a_    :     int
     a_    :     Fraction										=    Fraction(0 )
     a_    :     tuple[int, int]

     for x_num in range(1  ,  order + 1 ):
          for x_den in range(x_num + 1  ,  order + 1 ):
               for y_num in range(1  ,  order + 1 ):
                    for y_den in range(y_num + 1  ,  order + 1 ):
                         # n=1
                         a_    :     int										=    x_num * y_den + x_den * y_num
                         a_    :     Optional[int]										=    x_den * y_den
                         a_    :     Optional[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              a_    :     Optional[int]										=    add_three(
                                  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                              unique_s.add(__UpperCamelCase )

                         # n=2
                         a_    :     List[Any]										=    (
                             x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                         )
                         a_    :     str										=    x_den * x_den * y_den * y_den
                         if is_sq(__UpperCamelCase ) and is_sq(__UpperCamelCase ):
                              a_    :     Optional[Any]										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Optional[int]										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Optional[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   a_    :     str										=    add_three(
                                       __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                                   unique_s.add(__UpperCamelCase )

                    # n=-1
                         a_    :     Union[str, Any]										=    x_num * y_num
                         a_    :     Tuple										=    x_den * y_num + x_num * y_den
                         a_    :     List[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              a_    :     Union[str, Any]										=    add_three(
                                  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                              unique_s.add(__UpperCamelCase )

                         # n=2
                         a_    :     Union[str, Any]										=    x_num * x_num * y_num * y_num
                         a_    :     Dict										=    (
                             x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                         )
                         if is_sq(__UpperCamelCase ) and is_sq(__UpperCamelCase ):
                              a_    :     Dict										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Union[str, Any]										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Dict										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   a_    :     List[Any]										=    add_three(
                                       __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                                   unique_s.add(__UpperCamelCase )

     for num, den in unique_s:
          total += Fraction(__UpperCamelCase  ,  __UpperCamelCase )

     return total.denominator + total.numerator


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



