"""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__    :


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase=13,				_UpperCAmelCase=[30, 30],				_UpperCAmelCase=2,				_UpperCAmelCase=3,				_UpperCAmelCase=True,				_UpperCAmelCase=True,				_UpperCAmelCase=32,				_UpperCAmelCase=5,				_UpperCAmelCase=4,				_UpperCAmelCase=37,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=10,				_UpperCAmelCase=0.02,				_UpperCAmelCase=3,				_UpperCAmelCase=None,				_UpperCAmelCase=8,				_UpperCAmelCase=10,				):
        '''simple docstring'''



        lowercase__				 =							parent
        lowercase__				 =							batch_size
        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							is_training
        lowercase__				 =							use_labels
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							type_sequence_label_size
        lowercase__				 =							initializer_range
        lowercase__				 =							num_labels
        lowercase__				 =							scope
        lowercase__				 =							n_targets
        lowercase__				 =							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
        lowercase__				 =							(image_size[1] // patch_size) * (image_size[0] // patch_size)
        lowercase__				 =							num_patches + 1 + self.num_detection_tokens


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]  )

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

        lowercase__				 =							self.get_config()

        return config, pixel_values, labels


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        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=_UpperCAmelCase,				initializer_range=self.initializer_range,				num_detection_tokens=self.num_detection_tokens,				num_labels=self.num_labels,				)


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							YolosModel(config=_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowercase__				 =							model(_UpperCAmelCase  )
        self.parent.assertEqual(
            result.last_hidden_state.shape,				(self.batch_size, self.expected_seq_len, self.hidden_size)  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							YolosForObjectDetection(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()

        lowercase__				 =							model(pixel_values=_UpperCAmelCase  )
        lowercase__				 =							model(_UpperCAmelCase  )

        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)  )

        lowercase__				 =							model(pixel_values=_UpperCAmelCase,				labels=_UpperCAmelCase  )

        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 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.prepare_config_and_inputs()
        lowercase__ ,						lowercase__ ,						lowercase__				 =							config_and_inputs
        lowercase__				 =							{"pixel_values": pixel_values}
        return config, inputs_dict






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

   snake_case_							=      False
   snake_case_							=      False
   snake_case_							=      False
   snake_case_							=      False


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=False  ):
        '''simple docstring'''



        lowercase__				 =							super()._prepare_for_class(_UpperCAmelCase,				_UpperCAmelCase,				return_labels=_UpperCAmelCase  )

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

        return inputs_dict


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							YolosModelTester(self  )
        lowercase__				 =							ConfigTester(self,				config_class=_UpperCAmelCase,				has_text_modality=_UpperCAmelCase,				hidden_size=37  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        self.config_tester.run_common_tests()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )
             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],				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__				 =							True

        # in YOLOS, the seq_len is different
        lowercase__				 =							self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
             lowercase__				 =							True
             lowercase__				 =							False
             lowercase__				 =							True
             lowercase__				 =							model_class(_UpperCAmelCase  )
             model.to(_UpperCAmelCase  )
             model.eval()
             with torch.no_grad():
                  lowercase__				 =							model(**self._prepare_for_class(_UpperCAmelCase,				_UpperCAmelCase  )  )
             lowercase__				 =							outputs.attentions
             self.assertEqual(len(_UpperCAmelCase  ),				self.model_tester.num_hidden_layers  )

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

             self.assertListEqual(
                 list(attentions[0].shape[-3:]  ),				[self.model_tester.num_attention_heads, seq_len, seq_len],				)
             lowercase__				 =							len(_UpperCAmelCase  )

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

             lowercase__				 =							1
             self.assertEqual(out_len + added_hidden_states,				len(_UpperCAmelCase  )  )

             lowercase__				 =							outputs.attentions

             self.assertEqual(len(_UpperCAmelCase  ),				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 	snake_case__	(     self  ):
        '''simple docstring'''



        def check_hidden_states_output(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
             lowercase__				 =							model_class(_UpperCAmelCase  )
             model.to(_UpperCAmelCase  )
             model.eval()

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

             lowercase__				 =							outputs.hidden_states

             lowercase__				 =							getattr(
                 self.model_tester,				"expected_num_hidden_layers",				self.model_tester.num_hidden_layers + 1  )
             self.assertEqual(len(_UpperCAmelCase  ),				_UpperCAmelCase  )

             # YOLOS has a different seq_length
             lowercase__				 =							self.model_tester.expected_seq_len

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

        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(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )

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

             check_hidden_states_output(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase__				 =							YolosModel.from_pretrained(_UpperCAmelCase  )
             self.assertIsNotNone(_UpperCAmelCase  )







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
     return image






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


   @cached_property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return AutoImageProcessor.from_pretrained("hustvl/yolos-small"  ) if is_vision_available() else None


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							YolosForObjectDetection.from_pretrained("hustvl/yolos-small"  ).to(_UpperCAmelCase  )

        lowercase__				 =							self.default_image_processor
        lowercase__				 =							prepare_img()
        lowercase__				 =							image_processor(images=_UpperCAmelCase,				return_tensors="pt"  ).to(_UpperCAmelCase  )

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

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

        lowercase__				 =							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=_UpperCAmelCase,				)
        lowercase__				 =							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=_UpperCAmelCase  )
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3],				_UpperCAmelCase,				atol=1E-4  )  )
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3],				_UpperCAmelCase,				atol=1E-4  )  )

        # verify postprocessing
        lowercase__				 =							image_processor.post_process_object_detection(
            _UpperCAmelCase,				threshold=0.3,				target_sizes=[image.size[::-1]]  )[0]
        lowercase__				 =							torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]  ).to(_UpperCAmelCase  )
        lowercase__				 =							[75, 75, 17, 63, 17]
        lowercase__				 =							torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]  ).to(_UpperCAmelCase  )

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





"""simple docstring"""


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      PriorTransformer
   snake_case_							=      "hidden_states"


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   def 	snake_case__	(     self,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							{
            "num_attention_heads": 2,
            "attention_head_dim": 4,
            "num_layers": 2,
            "embedding_dim": 8,
            "num_embeddings": 7,
            "additional_embeddings": 4,
        }
        lowercase__				 =							self.dummy_input
        return init_dict, inputs_dict


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							PriorTransformer.from_pretrained(
            "hf-internal-testing/prior-dummy",				output_loading_info=_UpperCAmelCase  )
        self.assertIsNotNone(_UpperCAmelCase  )
        self.assertEqual(len(loading_info["missing_keys"]  ),				0  )

        model.to(_UpperCAmelCase  )
        lowercase__				 =							model(**self.dummy_input  )[0]

        assert hidden_states is not None, "Make sure output is not None"


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.prepare_init_args_and_inputs_for_common()

        lowercase__				 =							self.model_class(**_UpperCAmelCase  )
        lowercase__				 =							inspect.signature(model.forward  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase__				 =							[*signature.parameters.keys()]

        lowercase__				 =							["hidden_states", "timestep"]
        self.assertListEqual(arg_names[:2],				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"  )
        lowercase__				 =							model.to(_UpperCAmelCase  )

        if hasattr(_UpperCAmelCase,				"set_default_attn_processor"  ):
             model.set_default_attn_processor()

        lowercase__				 =							self.get_dummy_seed_input()

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        lowercase__				 =							output[0, :5].flatten().cpu()
        print(_UpperCAmelCase  )

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        lowercase__				 =							torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]  )
        self.assertTrue(torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				rtol=1E-2  )  )






@slow
class    a__    (   unittest.TestCase	):


   def 	snake_case__	(     self,				_UpperCAmelCase=1,				_UpperCAmelCase=768,				_UpperCAmelCase=77,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							batch_size
        lowercase__				 =							embedding_dim
        lowercase__				 =							num_embeddings

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]  )
   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior",				subfolder="prior"  )
        model.to(_UpperCAmelCase  )
        lowercase__				 =							self.get_dummy_seed_input(seed=_UpperCAmelCase  )

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        assert list(sample.shape  ) == [1, 768]

        lowercase__				 =							sample[0, :8].flatten().cpu()
        print(_UpperCAmelCase  )
        lowercase__				 =							torch.tensor(_UpperCAmelCase  )

        assert torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				atol=1E-3  )




