"""simple docstring"""





import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
       from transformers.trainer import DEFAULT_CALLBACKS

       from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel


class      UpperCAmelCase_			(					_a):


      def __init__(     self							) ->      Any:
             lowercase__    :	Tuple     					=			[]


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Any:
             self.events.append('on_init_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Optional[int]:
             self.events.append('on_train_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      List[str]:
             self.events.append('on_train_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_epoch_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Optional[Any]:
             self.events.append('on_epoch_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_step_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      str:
             self.events.append('on_step_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_evaluate'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Tuple:
             self.events.append('on_predict'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Union[str, Any]:
             self.events.append('on_save'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      List[str]:
             self.events.append('on_log'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Any:
             self.events.append('on_prediction_step'							)





@require_torch
class      UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	str     					=			tempfile.mkdtemp()


      def 	_UpperCAmelCase (     self							) ->      Dict:
             shutil.rmtree(self.output_dir							)


      def 	_UpperCAmelCase (     self	,    a=0	,    a=0	,    a=6_4	,    a=6_4	,    a=None	,    a=False	,    **a							) ->      int:
             # disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
             # its set to False since the tests later on depend on its value.
             lowercase__    :	str     					=			RegressionDataset(length=a							)
             lowercase__    :	Any     					=			RegressionDataset(length=a							)
             lowercase__    :	Optional[Any]     					=			RegressionModelConfig(a=a	,    b=a							)
             lowercase__    :	Union[str, Any]     					=			RegressionPreTrainedModel(a							)

             lowercase__    :	Tuple     					=			TrainingArguments(self.output_dir	,    disable_tqdm=a	,    report_to=[]	,    **a							)
             return Trainer(
                 a	,    a	,    train_dataset=a	,    eval_dataset=a	,    callbacks=a	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Union[str, Any]:
             self.assertEqual(len(a							)	,    len(a							)							)

             # Order doesn't matter
             lowercase__    :	Optional[int]     					=			sorted(a	,    key=lambda a							: cb.__name__ if isinstance(a	,    a							) else cb.__class__.__name__							)
             lowercase__    :	Tuple     					=			sorted(a	,    key=lambda a							: cb.__name__ if isinstance(a	,    a							) else cb.__class__.__name__							)

             for cba, cba in zip(a	,    a							):
                    if isinstance(a	,    a							) and isinstance(a	,    a							):
                           self.assertEqual(a	,    a							)
                    elif isinstance(a	,    a							) and not isinstance(a	,    a							):
                           self.assertEqual(a	,    cba.__class__							)
                    elif not isinstance(a	,    a							) and isinstance(a	,    a							):
                           self.assertEqual(cba.__class__	,    a							)
                    else:
                           self.assertEqual(a	,    a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[Any]:
             lowercase__    :	Dict     					=			['on_init_end', 'on_train_begin']
             lowercase__    :	List[Any]     					=			0
             lowercase__    :	Optional[int]     					=			len(trainer.get_eval_dataloader()							)
             lowercase__    :	Tuple     					=			['on_prediction_step'] * len(trainer.get_eval_dataloader()							) + ['on_log', 'on_evaluate']
             for _ in range(trainer.state.num_train_epochs							):
                    expected_events.append('on_epoch_begin'							)
                    for _ in range(a							):
                           step += 1
                           expected_events += ["on_step_begin", "on_step_end"]
                           if step % trainer.args.logging_steps == 0:
                                  expected_events.append('on_log'							)
                           if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                                  expected_events += evaluation_events.copy()
                           if step % trainer.args.save_steps == 0:
                                  expected_events.append('on_save'							)
                    expected_events.append('on_epoch_end'							)
                    if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
                           expected_events += evaluation_events.copy()
             expected_events += ["on_log", "on_train_end"]
             return expected_events


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	int     					=			self.get_trainer()
             lowercase__    :	str     					=			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             # Callbacks passed at init are added to the default callbacks
             lowercase__    :	str     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]							)
             expected_callbacks.append(a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
             lowercase__    :	List[Any]     					=			self.get_trainer(disable_tqdm=a							)
             lowercase__    :	Optional[Any]     					=			DEFAULT_CALLBACKS.copy() + [PrinterCallback]
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	int     					=			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             lowercase__    :	List[str]     					=			self.get_trainer()

             # We can add, pop, or remove by class name
             trainer.remove_callback(a							)
             expected_callbacks.remove(a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             lowercase__    :	Optional[Any]     					=			self.get_trainer()
             lowercase__    :	List[Any]     					=			trainer.pop_callback(a							)
             self.assertEqual(cb.__class__	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             trainer.add_callback(a							)
             expected_callbacks.insert(0	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             # We can also add, pop, or remove by instance
             lowercase__    :	int     					=			self.get_trainer()
             lowercase__    :	List[str]     					=			trainer.callback_handler.callbacks[0]
             trainer.remove_callback(a							)
             expected_callbacks.remove(a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             lowercase__    :	Tuple     					=			self.get_trainer()
             lowercase__    :	Dict     					=			trainer.callback_handler.callbacks[0]
             lowercase__    :	Union[str, Any]     					=			trainer.pop_callback(a							)
             self.assertEqual(a	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             trainer.add_callback(a							)
             expected_callbacks.insert(0	,    a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             import warnings

             # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
             warnings.simplefilter(action='ignore'	,    category=a							)

             lowercase__    :	Optional[Any]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]							)
             trainer.train()
             lowercase__    :	Any     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             # Independent log/save/eval
             lowercase__    :	List[str]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    logging_steps=5							)
             trainer.train()
             lowercase__    :	int     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	Union[str, Any]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    save_steps=5							)
             trainer.train()
             lowercase__    :	Union[str, Any]     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	List[str]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    eval_steps=5	,    evaluation_strategy='steps'							)
             trainer.train()
             lowercase__    :	Optional[int]     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	int     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    evaluation_strategy='epoch'							)
             trainer.train()
             lowercase__    :	str     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             # A bit of everything
             lowercase__    :	Any     					=			self.get_trainer(
                 callbacks=[MyTestTrainerCallback]	,    logging_steps=3	,    save_steps=1_0	,    eval_steps=5	,    evaluation_strategy='steps'	,    )
             trainer.train()
             lowercase__    :	Any     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             # warning should be emitted for duplicated callbacks
             with patch('transformers.trainer_callback.logger.warning'							) as warn_mock:
                    lowercase__    :	str     					=			self.get_trainer(
                        callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]	,    )
                    assert str(a							) in warn_mock.call_args[0][0]


"""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   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=[3_0, 3_0]	,    a=2	,    a=3	,    a=True	,    a=True	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=1_0	,    a=0.02	,    a=3	,    a=None	,    a=8	,    a=1_0	,    ) ->      Any:
             lowercase__    :	List[str]     					=			parent
             lowercase__    :	Optional[Any]     					=			batch_size
             lowercase__    :	Optional[int]     					=			image_size
             lowercase__    :	List[Any]     					=			patch_size
             lowercase__    :	Optional[Any]     					=			num_channels
             lowercase__    :	str     					=			is_training
             lowercase__    :	Optional[Any]     					=			use_labels
             lowercase__    :	Optional[Any]     					=			hidden_size
             lowercase__    :	Dict     					=			num_hidden_layers
             lowercase__    :	Optional[Any]     					=			num_attention_heads
             lowercase__    :	Dict     					=			intermediate_size
             lowercase__    :	List[Any]     					=			hidden_act
             lowercase__    :	List[Any]     					=			hidden_dropout_prob
             lowercase__    :	Any     					=			attention_probs_dropout_prob
             lowercase__    :	Any     					=			type_sequence_label_size
             lowercase__    :	Dict     					=			initializer_range
             lowercase__    :	Union[str, Any]     					=			num_labels
             lowercase__    :	Tuple     					=			scope
             lowercase__    :	Tuple     					=			n_targets
             lowercase__    :	Optional[int]     					=			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__    :	Optional[Any]     					=			(image_size[1] // patch_size) * (image_size[0] // patch_size)
             lowercase__    :	Tuple     					=			num_patches + 1 + self.num_detection_tokens


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Union[str, Any]     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)

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

             lowercase__    :	Tuple     					=			self.get_config()

             return config, pixel_values, labels


      def 	_UpperCAmelCase (     self							) ->      List[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=a	,    initializer_range=self.initializer_range	,    num_detection_tokens=self.num_detection_tokens	,    num_labels=self.num_labels	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      int:
             lowercase__    :	List[str]     					=			YolosModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	List[Any]     					=			model(a							)
             self.parent.assertEqual(
                 result.last_hidden_state.shape	,    (self.batch_size, self.expected_seq_len, self.hidden_size)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Union[str, Any]:
             lowercase__    :	str     					=			YolosForObjectDetection(a							)
             model.to(a							)
             model.eval()

             lowercase__    :	Dict     					=			model(pixel_values=a							)
             lowercase__    :	Tuple     					=			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__    :	str     					=			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 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	int     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__    :	Any     					=			config_and_inputs
             lowercase__    :	Any     					=			{'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[int]										=						(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
      lowerCamelCase__	:					List[str]										=						(
          {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
      )

      lowerCamelCase__	:					List[Any]										=						False
      lowerCamelCase__	:					Dict										=						False
      lowerCamelCase__	:					Tuple										=						False
      lowerCamelCase__	:					Union[str, Any]										=						False


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a=False							) ->      Dict:
             lowercase__    :	List[str]     					=			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__    :	Dict     					=			{}
                                  lowercase__    :	Dict     					=			torch.ones(
                                      size=(self.model_tester.n_targets,)	,    device=a	,    dtype=torch.long							)
                                  lowercase__    :	Optional[Any]     					=			torch.ones(
                                      self.model_tester.n_targets	,    4	,    device=a	,    dtype=torch.float							)
                                  labels.append(a							)
                           lowercase__    :	Union[str, Any]     					=			labels

             return inputs_dict


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Dict     					=			YolosModelTester(self							)
             lowercase__    :	Optional[int]     					=			ConfigTester(self	,    config_class=a	,    has_text_modality=a	,    hidden_size=3_7							)


      def 	_UpperCAmelCase (     self							) ->      str:
             self.config_tester.run_common_tests()


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


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Union[str, 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 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__    :	List[Any]     					=			['pixel_values']
                    self.assertListEqual(arg_names[:1]	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__					,   lowercase__    :	int     					=			self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__    :	Dict     					=			True

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

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

                    lowercase__    :	Optional[Any]     					=			1
                    self.assertEqual(out_len + added_hidden_states	,    len(a							)							)

                    lowercase__    :	Tuple     					=			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 	_UpperCAmelCase (     self							) ->      List[str]:
             def check_hidden_states_output(a	,    a	,    a							):
                    lowercase__    :	str     					=			model_class(a							)
                    model.to(a							)
                    model.eval()

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

                    lowercase__    :	int     					=			outputs.hidden_states

                    lowercase__    :	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__    :	Optional[int]     					=			self.model_tester.expected_seq_len

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

             lowercase__					,   lowercase__    :	List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__    :	Any     					=			True
                    check_hidden_states_output(a	,    a	,    a							)

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

                    check_hidden_states_output(a	,    a	,    a							)


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	List[Any]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_object_detection(*a							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__    :	int     					=			YolosModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)





def        a_  (   ):





       '''simple docstring'''

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






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


      @cached_property
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return AutoImageProcessor.from_pretrained('hustvl/yolos-small'							) if is_vision_available() else None


      @slow
      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small'							).to(a							)

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

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

             # verify outputs
             lowercase__    :	Tuple     					=			torch.Size((1, 1_0_0, 9_2)							)
             self.assertEqual(outputs.logits.shape	,    a							)

             lowercase__    :	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=a	,    )
             lowercase__    :	List[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=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__    :	Optional[Any]     					=			image_processor.post_process_object_detection(
                 a	,    threshold=0.3	,    target_sizes=[image.size[::-1]]							)[0]
             lowercase__    :	str     					=			torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]							).to(a							)
             lowercase__    :	Any     					=			[7_5, 7_5, 1_7, 6_3, 1_7]
             lowercase__    :	Optional[int]     					=			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							)							)

