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


_SCREAMING_SNAKE_CASE    :      int          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      str          =			{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"megatron-bert"





	def __init__(			self				,       UpperCamelCase__=2_9056				,       UpperCamelCase__=1024				,       UpperCamelCase__=24				,       UpperCamelCase__=16				,       UpperCamelCase__=4096				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=512				,       UpperCamelCase__=2				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=1e-12				,       UpperCamelCase__=0				,       UpperCamelCase__="absolute"				,       UpperCamelCase__=True				,       **UpperCamelCase__				,       ):
					super().__init__(pad_token_id=UpperCamelCase__				,       **UpperCamelCase__		)

					A__  :	List[Any]         =							vocab_size
					A__  :	Tuple         =							hidden_size
					A__  :	Union[str, Any]         =							num_hidden_layers
					A__  :	str         =							num_attention_heads
					A__  :	Dict         =							hidden_act
					A__  :	Dict         =							intermediate_size
					A__  :	str         =							hidden_dropout_prob
					A__  :	Tuple         =							attention_probs_dropout_prob
					A__  :	List[Any]         =							max_position_embeddings
					A__  :	Tuple         =							type_vocab_size
					A__  :	List[str]         =							initializer_range
					A__  :	List[Any]         =							layer_norm_eps
					A__  :	Optional[int]         =							position_embedding_type
					A__  :	str         =							use_cache

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  UpperCamelCase__		:







	'''simple docstring'''





	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__=0.0_2				,       UpperCamelCase__=3				,       UpperCamelCase__=None				,       UpperCamelCase__=8				,       UpperCamelCase__=10				,       ):
					A__  :	Optional[int]         =							parent
					A__  :	List[Any]         =							batch_size
					A__  :	Dict         =							image_size
					A__  :	Any         =							patch_size
					A__  :	Dict         =							num_channels
					A__  :	List[Any]         =							is_training
					A__  :	int         =							use_labels
					A__  :	Any         =							hidden_size
					A__  :	List[str]         =							num_hidden_layers
					A__  :	Optional[int]         =							num_attention_heads
					A__  :	Optional[Any]         =							intermediate_size
					A__  :	str         =							hidden_act
					A__  :	str         =							hidden_dropout_prob
					A__  :	Optional[int]         =							attention_probs_dropout_prob
					A__  :	Optional[int]         =							type_sequence_label_size
					A__  :	Any         =							initializer_range
					A__  :	Optional[int]         =							num_labels
					A__  :	Union[str, Any]         =							scope
					A__  :	Union[str, Any]         =							n_targets
					A__  :	Dict         =							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__  :	int         =							(image_size[1] // patch_size) * (image_size[0] // patch_size)
					A__  :	List[str]         =							num_patches + 1 + self.num_detection_tokens





	def   __snake_case				(			self		):
					A__  :	Dict         =							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

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

					A__  :	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=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__		):
					A__  :	Tuple         =							YolosModel(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()
					A__  :	Optional[Any]         =							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__		):
					A__  :	Any         =							YolosForObjectDetection(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()

					A__  :	Union[str, Any]         =							model(pixel_values=UpperCamelCase__		)
					A__  :	Optional[int]         =							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)		)

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







@require_torch
class  UpperCamelCase__		(    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   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=False		):
					A__  :	Optional[int]         =							super()._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__				,       return_labels=UpperCamelCase__		)

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

					return inputs_dict





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							YolosModelTester(self		)
					A__  :	List[str]         =							ConfigTester(self				,       config_class=UpperCamelCase__				,       has_text_modality=UpperCamelCase__				,       hidden_size=37		)





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





	def   __snake_case				(			self		):
					# YOLOS does not use inputs_embeds
					pass





	def   __snake_case				(			self		):
					A__       ,							A__  :	Dict         =							self.model_tester.prepare_config_and_inputs_for_common()

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





	def   __snake_case				(			self		):
					A__       ,							A__  :	int         =							self.model_tester.prepare_config_and_inputs_for_common()

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

									A__  :	Optional[Any]         =							['''pixel_values''']
									self.assertListEqual(arg_names[:1]				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__       ,							A__  :	List[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()
					A__  :	Tuple         =							True

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

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

									A__  :	Tuple         =							1
									self.assertEqual(out_len + added_hidden_states				,       len(UpperCamelCase__		)		)

									A__  :	List[str]         =							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		):
					def check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	str         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()

									with torch.no_grad():
													A__  :	int         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)

									A__  :	Optional[Any]         =							outputs.hidden_states

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

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

					A__       ,							A__  :	Optional[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	int         =							True
									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

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

									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_object_detection(*UpperCamelCase__		)





	@slow
	def   __snake_case				(			self		):
					for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									A__  :	Union[str, Any]         =							YolosModel.from_pretrained(UpperCamelCase__		)
									self.assertIsNotNone(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      ) ->       List[str]:

				"""simple docstring"""







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







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







	'''simple docstring'''





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





	@slow
	def   __snake_case				(			self		):
					A__  :	Tuple         =							YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(UpperCamelCase__		)

					A__  :	str         =							self.default_image_processor
					A__  :	Tuple         =							prepare_img()
					A__  :	Tuple         =							image_processor(images=UpperCamelCase__				,       return_tensors='''pt'''		).to(UpperCamelCase__		)

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

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

					A__  :	Optional[int]         =							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=UpperCamelCase__				,       )
					A__  :	Optional[int]         =							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=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
					A__  :	Dict         =							image_processor.post_process_object_detection(
					    UpperCamelCase__				,       threshold=0.3				,       target_sizes=[image.size[::-1]]		)[0]
					A__  :	int         =							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(UpperCamelCase__		)
					A__  :	str         =							[75, 75, 17, 63, 17]
					A__  :	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(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__		)		)