






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_  			=			{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_  			=			['PLBartTokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_  			=			[
							    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'PLBartForCausalLM',
							    'PLBartForConditionalGeneration',
							    'PLBartForSequenceClassification',
							    'PLBartModel',
							    'PLBartPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							a_  			=			_LazyModule(__name__, globals()['__file__'], _import_structure)





'''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		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=[30, 30]		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=37		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=10		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=10		,     )					->							Dict:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Tuple   =		floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )

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

         A			:  Any   =		self.get_config()

         return config, pixel_values, labels





     def 							__lowerCAmelCase				(				self      )					->							str:






         """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=SCREAMING_SNAKE_CASE		,     initializer_range=self.initializer_range		,     num_detection_tokens=self.num_detection_tokens		,     num_labels=self.num_labels		,     )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  int   =		YolosModel(config=SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Optional[int]   =		model(SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(
             result.last_hidden_state.shape		,     (self.batch_size, self.expected_seq_len, self.hidden_size)      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  int   =		YolosForObjectDetection(SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()

         A			:  Any   =		model(pixel_values=SCREAMING_SNAKE_CASE      )
         A			:  Tuple   =		model(SCREAMING_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)      )

         A			:  List[str]   =		model(pixel_values=SCREAMING_SNAKE_CASE		,     labels=SCREAMING_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 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






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





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

     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=False      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		super()._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )

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

         return inputs_dict





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		YolosModelTester(self      )
         A			:  Any   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE		,     has_text_modality=SCREAMING_SNAKE_CASE		,     hidden_size=37      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         self.config_tester.run_common_tests()





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A,    A			:  Tuple   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A			:  Dict   =		model_class(SCREAMING_SNAKE_CASE      )
             self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module)      )
             A			:  List[Any]   =		model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE		,     nn.Linear      )      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A,    A			:  int   =		self.model_tester.prepare_config_and_inputs_for_common()

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

             A			:  Dict   =		['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  Optional[int]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






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

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

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             A			:  int   =		True
             A			:  Optional[int]   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()
             with torch.no_grad():
                 A			:  List[str]   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )
             A			:  List[Any]   =		outputs.attentions
             self.assertEqual(len(SCREAMING_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]		,     )
             A			:  Any   =		len(SCREAMING_SNAKE_CASE      )

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

             A			:  Optional[int]   =		1
             self.assertEqual(out_len + added_hidden_states		,     len(SCREAMING_SNAKE_CASE      )      )

             A			:  List[str]   =		outputs.attentions

             self.assertEqual(len(SCREAMING_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 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         def check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
             A			:  List[Any]   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()

             with torch.no_grad():
                 A			:  List[Any]   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )

             A			:  Dict   =		outputs.hidden_states

             A			:  List[Any]   =		getattr(
                 self.model_tester		,     '''expected_num_hidden_layers'''		,     self.model_tester.num_hidden_layers + 1      )
             self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     SCREAMING_SNAKE_CASE      )

             # YOLOS has a different seq_length
             A			:  int   =		self.model_tester.expected_seq_len

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

         A,    A			:  Optional[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(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

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

             check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  Dict   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_object_detection(*SCREAMING_SNAKE_CASE      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A			:  int   =		YolosModel.from_pretrained(SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE      )







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



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







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





     @cached_property
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






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





     @slow
     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Union[str, Any]   =		YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''      ).to(SCREAMING_SNAKE_CASE      )

         A			:  str   =		self.default_image_processor
         A			:  Any   =		prepare_img()
         A			:  Any   =		image_processor(images=SCREAMING_SNAKE_CASE		,     return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE      )

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

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

         A			:  Optional[Any]   =		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=SCREAMING_SNAKE_CASE		,     )
         A			:  str   =		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=SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )
         self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]		,     SCREAMING_SNAKE_CASE		,     atol=1e-4      )      )

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

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




