from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__           =							logging.get_logger(__name__)

A__           =							{
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'vit_msn'







							def __init__(				self     :    List[str]	,					__snake_case     :    Union[str, Any]=768	,					__snake_case     :    Any=12	,					__snake_case     :    List[str]=12	,					__snake_case     :    Any=3072	,					__snake_case     :    Any="gelu"	,					__snake_case     :    int=0.0	,					__snake_case     :    str=0.0	,					__snake_case     :    List[Any]=0.0_2	,					__snake_case     :    int=1e-0_6	,					__snake_case     :    Any=224	,					__snake_case     :    Dict=16	,					__snake_case     :    Dict=3	,					__snake_case     :    Union[str, Any]=True	,					**__snake_case     :    Union[str, Any]	,					):
										super().__init__(**__snake_case		)

										lowerCamelCase						:Optional[int]						  =     hidden_size
										lowerCamelCase						:int						  =     num_hidden_layers
										lowerCamelCase						:int						  =     num_attention_heads
										lowerCamelCase						:List[Any]						  =     intermediate_size
										lowerCamelCase						:Tuple						  =     hidden_act
										lowerCamelCase						:Union[str, Any]						  =     hidden_dropout_prob
										lowerCamelCase						:Any						  =     attention_probs_dropout_prob
										lowerCamelCase						:List[str]						  =     initializer_range
										lowerCamelCase						:Tuple						  =     layer_norm_eps
										lowerCamelCase						:Tuple						  =     image_size
										lowerCamelCase						:List[str]						  =     patch_size
										lowerCamelCase						:Dict						  =     num_channels
										lowerCamelCase						:Union[str, Any]						  =     qkv_bias





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   _lowerCAmelCase		:







							def __init__(				self     :    Any	,					__snake_case     :    Optional[int]	,					__snake_case     :    int=13	,					__snake_case     :    str=[30, 30]	,					__snake_case     :    Tuple=2	,					__snake_case     :    Optional[Any]=3	,					__snake_case     :    int=True	,					__snake_case     :    Tuple=True	,					__snake_case     :    List[Any]=32	,					__snake_case     :    int=5	,					__snake_case     :    Optional[Any]=4	,					__snake_case     :    Union[str, Any]=37	,					__snake_case     :    str="gelu"	,					__snake_case     :    Tuple=0.1	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    Union[str, Any]=10	,					__snake_case     :    str=0.0_2	,					__snake_case     :    Union[str, Any]=3	,					__snake_case     :    Union[str, Any]=None	,					__snake_case     :    List[str]=8	,					__snake_case     :    Any=10	,					):
										lowerCamelCase						:Optional[Any]						  =     parent
										lowerCamelCase						:List[Any]						  =     batch_size
										lowerCamelCase						:Any						  =     image_size
										lowerCamelCase						:Union[str, Any]						  =     patch_size
										lowerCamelCase						:Any						  =     num_channels
										lowerCamelCase						:List[Any]						  =     is_training
										lowerCamelCase						:Optional[Any]						  =     use_labels
										lowerCamelCase						:Any						  =     hidden_size
										lowerCamelCase						:List[Any]						  =     num_hidden_layers
										lowerCamelCase						:List[str]						  =     num_attention_heads
										lowerCamelCase						:Tuple						  =     intermediate_size
										lowerCamelCase						:List[str]						  =     hidden_act
										lowerCamelCase						:List[str]						  =     hidden_dropout_prob
										lowerCamelCase						:Any						  =     attention_probs_dropout_prob
										lowerCamelCase						:List[Any]						  =     type_sequence_label_size
										lowerCamelCase						:Optional[int]						  =     initializer_range
										lowerCamelCase						:List[Any]						  =     num_labels
										lowerCamelCase						:Any						  =     scope
										lowerCamelCase						:Union[str, Any]						  =     n_targets
										lowerCamelCase						:Optional[Any]						  =     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
										lowerCamelCase						:Tuple						  =     (image_size[1] // patch_size) * (image_size[0] // patch_size)
										lowerCamelCase						:str						  =     num_patches + 1 + self.num_detection_tokens







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

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

										lowerCamelCase						:str						  =     self.get_config()

										return config, pixel_values, labels







							def 				snake_case  (				self     :    Union[str, Any]		):
										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=__snake_case	,					initializer_range=self.initializer_range	,					num_detection_tokens=self.num_detection_tokens	,					num_labels=self.num_labels	,					)







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    Tuple	,					__snake_case     :    Tuple	,					__snake_case     :    Any		):
										lowerCamelCase						:Optional[Any]						  =     YolosModel(config=__snake_case		)
										model.to(__snake_case		)
										model.eval()
										lowerCamelCase						:Union[str, Any]						  =     model(__snake_case		)
										self.parent.assertEqual(
										    result.last_hidden_state.shape	,					(self.batch_size, self.expected_seq_len, self.hidden_size)		)







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    str	,					__snake_case     :    Optional[int]	,					__snake_case     :    Optional[Any]		):
										lowerCamelCase						:int						  =     YolosForObjectDetection(__snake_case		)
										model.to(__snake_case		)
										model.eval()

										lowerCamelCase						:str						  =     model(pixel_values=__snake_case		)
										lowerCamelCase						:Any						  =     model(__snake_case		)

										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)		)

										lowerCamelCase						:int						  =     model(pixel_values=__snake_case	,					labels=__snake_case		)

										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     :    int		):
										lowerCamelCase						:List[Any]						  =     self.prepare_config_and_inputs()
										lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						:str						  =     config_and_inputs
										lowerCamelCase						:Union[str, Any]						  =     {'''pixel_values''': pixel_values}
										return config, inputs_dict


@require_torch
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							_UpperCAmelCase						    =     (
							    {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
							)

							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False







							def 				snake_case  (				self     :    Any	,					__snake_case     :    List[Any]	,					__snake_case     :    Tuple	,					__snake_case     :    Dict=False		):
										lowerCamelCase						:Optional[int]						  =     super()._prepare_for_class(__snake_case	,					__snake_case	,					return_labels=__snake_case		)

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

										return inputs_dict







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Union[str, Any]						  =     YolosModelTester(self		)
										lowerCamelCase						:Dict						  =     ConfigTester(self	,					config_class=__snake_case	,					has_text_modality=__snake_case	,					hidden_size=37		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										self.config_tester.run_common_tests()







							def 				snake_case  (				self     :    Optional[Any]		):
										# YOLOS does not use inputs_embeds
										pass







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Optional[int]						  =     model_class(__snake_case		)
													self.assertIsInstance(model.get_input_embeddings()	,					(nn.Module)		)
													lowerCamelCase						:str						  =     model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__snake_case	,					nn.Linear		)		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCamelCase						:Tuple						  =     ['''pixel_values''']
													self.assertListEqual(arg_names[:1]	,					__snake_case		)







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:List[str]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase						:int						  =     True

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

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

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

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

													lowerCamelCase						:Dict						  =     1
													self.assertEqual(out_len + added_hidden_states	,					len(__snake_case		)		)

													lowerCamelCase						:Dict						  =     outputs.attentions

													self.assertEqual(len(__snake_case		)	,					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     :    List[str]		):
										def check_hidden_states_output(__snake_case     :    Union[str, Any]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Tuple		):
													lowerCamelCase						:Union[str, Any]						  =     model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()

													with torch.no_grad():
																lowerCamelCase						:Any						  =     model(**self._prepare_for_class(__snake_case	,					__snake_case		)		)

													lowerCamelCase						:Optional[Any]						  =     outputs.hidden_states

													lowerCamelCase						:Any						  =     getattr(
													    self.model_tester	,					'''expected_num_hidden_layers'''	,					self.model_tester.num_hidden_layers + 1		)
													self.assertEqual(len(__snake_case		)	,					__snake_case		)

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

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

										lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

													check_hidden_states_output(__snake_case	,					__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Optional[Any]						  =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_object_detection(*__snake_case		)







							@slow
							def 				snake_case  (				self     :    Dict		):
										for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase						:Tuple						  =     YolosModel.from_pretrained(__snake_case		)
													self.assertIsNotNone(__snake_case		)





def  _lowerCamelCase					(   ):
			lowerCamelCase						:int						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
			return image


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







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







							@slow
							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Union[str, Any]						  =     YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(__snake_case		)

										lowerCamelCase						:Optional[Any]						  =     self.default_image_processor
										lowerCamelCase						:str						  =     prepare_img()
										lowerCamelCase						:Dict						  =     image_processor(images=__snake_case	,					return_tensors='''pt'''		).to(__snake_case		)

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

										# verify outputs
										lowerCamelCase						:int						  =     torch.Size((1, 100, 92)		)
										self.assertEqual(outputs.logits.shape	,					__snake_case		)

										lowerCamelCase						: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=__snake_case	,					)
										lowerCamelCase						:Any						  =     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=__snake_case		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)
										self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]	,					__snake_case	,					atol=1e-4		)		)

										# verify postprocessing
										lowerCamelCase						:List[str]						  =     image_processor.post_process_object_detection(
										    __snake_case	,					threshold=0.3	,					target_sizes=[image.size[::-1]]		)[0]
										lowerCamelCase						:List[str]						  =     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(__snake_case		)
										lowerCamelCase						:str						  =     [75, 75, 17, 63, 17]
										lowerCamelCase						:Tuple						  =     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(__snake_case		)

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



