

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 				SCREAMING_SNAKE_CASE__ :





    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						,       ):
         lowercase__   :       List[str]				=	parent
         lowercase__   :       int				=	batch_size
         lowercase__   :       Optional[int]				=	image_size
         lowercase__   :       Optional[int]				=	patch_size
         lowercase__   :       int				=	num_channels
         lowercase__   :       Dict				=	is_training
         lowercase__   :       Tuple				=	use_labels
         lowercase__   :       str				=	hidden_size
         lowercase__   :       Dict				=	num_hidden_layers
         lowercase__   :       List[Any]				=	num_attention_heads
         lowercase__   :       List[str]				=	intermediate_size
         lowercase__   :       int				=	hidden_act
         lowercase__   :       Any				=	hidden_dropout_prob
         lowercase__   :       Optional[Any]				=	attention_probs_dropout_prob
         lowercase__   :       Optional[Any]				=	type_sequence_label_size
         lowercase__   :       Tuple				=	initializer_range
         lowercase__   :       Tuple				=	num_labels
         lowercase__   :       Tuple				=	scope
         lowercase__   :       List[str]				=	n_targets
         lowercase__   :       str				=	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__   :       List[Any]				=	(image_size[1] // patch_size) * (image_size[0] // patch_size)
         lowercase__   :       str				=	num_patches + 1 + self.num_detection_tokens





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

         lowercase__   :       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)
              lowercase__   :       Dict				=	[]
              for i in range(self.batch_size):
                   lowercase__   :       Optional[Any]				=	{}
                   lowercase__   :       Union[str, Any]				=	torch.randint(
                       high=self.num_labels						,       size=(self.n_targets,)						,       device=_a)
                   lowercase__   :       str				=	torch.rand(self.n_targets						,       4						,       device=_a)
                   labels.append(_a)

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

         return config, pixel_values, labels





    def 				snake_case_    (				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 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       List[str]				=	YolosModel(config=_a)
         model.to(_a)
         model.eval()
         lowercase__   :       Tuple				=	model(_a)
         self.parent.assertEqual(
             result.last_hidden_state.shape						,       (self.batch_size, self.expected_seq_len, self.hidden_size))





    def 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       Tuple				=	YolosForObjectDetection(_a)
         model.to(_a)
         model.eval()

         lowercase__   :       str				=	model(pixel_values=_a)
         lowercase__   :       Any				=	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))

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



@require_torch
class 				SCREAMING_SNAKE_CASE__ (lowercase__		,	lowercase__		,	unittest.TestCase					):
    __lowerCamelCase						:	Any														=					(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
    __lowerCamelCase						:	List[Any]														=					(
        {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
    )

    __lowerCamelCase						:	Optional[int]														=					False
    __lowerCamelCase						:	Union[str, Any]														=					False
    __lowerCamelCase						:	Optional[int]														=					False
    __lowerCamelCase						:	Dict														=					False





    def 				snake_case_    (				self						,       a						,       a						,       a=False):
         lowercase__   :       List[Any]				=	super()._prepare_for_class(_a						,       _a						,       return_labels=_a)

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

         return inputs_dict





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	YolosModelTester(self)
         lowercase__   :       str				=	ConfigTester(self						,       config_class=_a						,       has_text_modality=_a						,       hidden_size=37)





    def 				snake_case_    (				self):
         self.config_tester.run_common_tests()





    def 				snake_case_    (				self):
         pass





    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	self.model_tester.prepare_config_and_inputs_for_common()

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





    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.model_tester.prepare_config_and_inputs_for_common()

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

              lowercase__   :       Optional[int]				=	['pixel_values']
              self.assertListEqual(arg_names[:1]						,       _a)





    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_a)





    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__   :       Tuple				=	True

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

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

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

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






         def check_hidden_states_output(a						,       a						,       a):
              lowercase__   :       List[str]				=	model_class(_a)
              model.to(_a)
              model.eval()

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

              lowercase__   :       Any				=	outputs.hidden_states

              lowercase__   :       Union[str, Any]				=	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
              lowercase__   :       Union[str, Any]				=	self.model_tester.expected_seq_len

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

         lowercase__   :       Any				=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowercase__   :       Dict				=	True
              check_hidden_states_output(_a						,       _a						,       _a)

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

              check_hidden_states_output(_a						,       _a						,       _a)





    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_object_detection(*_a)





    @slow
    def 				snake_case_    (				self):






         for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__   :       Union[str, Any]				=	YolosModel.from_pretrained(_a)
              self.assertIsNotNone(_a)



def      snake_case__      (   ):



     '''simple docstring'''





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


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





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





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       List[Any]				=	YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(_a)

         lowercase__   :       Tuple				=	self.default_image_processor
         lowercase__   :       Optional[int]				=	prepare_img()
         lowercase__   :       Any				=	image_processor(images=_a						,       return_tensors='pt').to(_a)

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

         # verify outputs
         lowercase__   :       List[Any]				=	torch.Size((1, 100, 92))
         self.assertEqual(outputs.logits.shape						,       _a)

         lowercase__   :       str				=	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						,       )
         lowercase__   :       Tuple				=	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
         lowercase__   :       str				=	image_processor.post_process_object_detection(
             _a						,       threshold=0.3						,       target_sizes=[image.size[::-1]])[0]
         lowercase__   :       Union[str, Any]				=	torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]).to(_a)
         lowercase__   :       Dict				=	[75, 75, 17, 63, 17]
         lowercase__   :       List[str]				=	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))









import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)

