


'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 					lowercase_     (    a__  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	DiTPipeline
    __UpperCAmelCase   			=	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
    __UpperCAmelCase   			=	PipelineTesterMixin.required_optional_params - {
        'latents',
        'num_images_per_prompt',
        'callback',
        'callback_steps',
    }
    __UpperCAmelCase   			=	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
    __UpperCAmelCase   			=	False


    def    __a							(     self    ):
          torch.manual_seed(0    )
          UpperCamelCase__										=				TransformeraDModel(
              sample_size=16  ,					num_layers=2  ,					patch_size=4  ,					attention_head_dim=8  ,					num_attention_heads=2  ,					in_channels=4  ,					out_channels=8  ,					attention_bias=a  ,					activation_fn="gelu-approximate"  ,					num_embeds_ada_norm=10_00  ,					norm_type="ada_norm_zero"  ,					norm_elementwise_affine=a  ,					)
          UpperCamelCase__										=				AutoencoderKL()
          UpperCamelCase__										=				DDIMScheduler()
          UpperCamelCase__										=				{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
          return components


    def    __a							(     self  ,					a  ,					a=0    ):
          if str(a    ).startswith("mps"    ):
                UpperCamelCase__										=				torch.manual_seed(a    )
          else:
                UpperCamelCase__										=				torch.Generator(device=a    ).manual_seed(a    )
          UpperCamelCase__										=				{
              "class_labels": [1],
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }
          return inputs


    def    __a							(     self    ):
          UpperCamelCase__										=				"cpu"

          UpperCamelCase__										=				self.get_dummy_components()
          UpperCamelCase__										=				self.pipeline_class(**a    )
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )

          UpperCamelCase__										=				self.get_dummy_inputs(a    )
          UpperCamelCase__										=				pipe(**a    ).images
          UpperCamelCase__										=				image[0, -3:, -3:, -1]

          self.assertEqual(image.shape  ,					(1, 16, 16, 3)    )
          UpperCamelCase__										=				np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]    )
          UpperCamelCase__										=				np.abs(image_slice.flatten() - expected_slice    ).max()
          self.assertLessEqual(a  ,					1e-3    )


    def    __a							(     self    ):
          self._test_inference_batch_single_identical(relax_max_difference=a  ,					expected_max_diff=1e-3    )


    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()  ,					reason="XFormers attention is only available with CUDA and `xformers` installed"  ,					)
    def    __a							(     self    ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )

@require_torch_gpu
@slow
class 					lowercase_     (    unittest.TestCase   ):


    def    __a							(     self    ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


    def    __a							(     self    ):
          UpperCamelCase__										=				torch.manual_seed(0    )

          UpperCamelCase__										=				DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"    )
          pipe.to("cuda"    )

          UpperCamelCase__										=				["vase", "umbrella", "white shark", "white wolf"]
          UpperCamelCase__										=				pipe.get_label_ids(a    )

          UpperCamelCase__										=				pipe(a  ,					generator=a  ,					num_inference_steps=40  ,					output_type="np"    ).images

          for word, image in zip(a  ,					a    ):
                UpperCamelCase__										=				load_numpy(
                    f'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''    )
                assert np.abs((expected_image - image).max()    ) < 1e-2


    def    __a							(     self    ):
          UpperCamelCase__										=				DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"    )
          UpperCamelCase__										=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
          pipe.to("cuda"    )

          UpperCamelCase__										=				["vase", "umbrella"]
          UpperCamelCase__										=				pipe.get_label_ids(a    )

          UpperCamelCase__										=				torch.manual_seed(0    )
          UpperCamelCase__										=				pipe(a  ,					generator=a  ,					num_inference_steps=25  ,					output_type="np"    ).images

          for word, image in zip(a  ,					a    ):
                UpperCamelCase__										=				load_numpy(
                    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                    f'''/dit/{word}_512.npy'''    )

                assert np.abs((expected_image - image).max()    ) < 1e-1











'''simple docstring'''

import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:






							class 						a				:


											@staticmethod
											def   A_ (		*lowercase_    :					int       ,				**lowercase_    :					str					):
																pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						a				(   unittest.TestCase     ):
				snake_case_      =		MODEL_FOR_OBJECT_DETECTION_MAPPING


				def   A_ (		self    :					Any       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]					):
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				image_processor=lowercase_					)
									return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


				def   A_ (		self    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					int					):
									snake_case_									=				object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ,				threshold=0.0					)

									self.assertGreater(len(lowercase_					)       ,				0					)
									for detected_object in outputs:
														self.assertEqual(
														    lowercase_       ,				{
														        '''score''': ANY(lowercase_					),
														        '''label''': ANY(lowercase_					),
														        '''box''': {'''xmin''': ANY(lowercase_					), '''ymin''': ANY(lowercase_					), '''xmax''': ANY(lowercase_					), '''ymax''': ANY(lowercase_					)},
														    }       ,				)

									import datasets

									snake_case_									=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''       ,				'''image'''       ,				split='''test'''					)

									snake_case_									=				[
									    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					),
									    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    # RGBA
									    dataset[0]['''file'''],
									    # LA
									    dataset[1]['''file'''],
									    # L
									    dataset[2]['''file'''],
									]
									snake_case_									=				object_detector(lowercase_       ,				threshold=0.0					)

									self.assertEqual(len(lowercase_					)       ,				len(lowercase_					)					)
									for outputs in batch_outputs:
														self.assertGreater(len(lowercase_					)       ,				0					)
														for detected_object in outputs:
																			self.assertEqual(
																			    lowercase_       ,				{
																			        '''score''': ANY(lowercase_					),
																			        '''label''': ANY(lowercase_					),
																			        '''box''': {'''xmin''': ANY(lowercase_					), '''ymin''': ANY(lowercase_					), '''xmax''': ANY(lowercase_					), '''ymax''': ANY(lowercase_					)},
																			    }       ,				)


				@require_tf
				@unittest.skip('''Object detection not implemented in TF'''					)
				def   A_ (		self    :					int					):
									pass


				@require_torch
				def   A_ (		self    :					Tuple					):
									snake_case_									=				'''hf-internal-testing/tiny-detr-mobilenetsv3'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,				threshold=0.0					)

									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									    ]       ,				)

									snake_case_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]       ,				threshold=0.0       ,				)

									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        [
									            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									        ],
									        [
									            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									        ],
									    ]       ,				)


				@require_torch
				@slow
				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									    ]       ,				)

									snake_case_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        [
									            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									        ],
									        [
									            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									        ],
									    ]       ,				)


				@require_torch
				@slow
				def   A_ (		self    :					Tuple					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									    ]       ,				)

									snake_case_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        [
									            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									        ],
									        [
									            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									        ],
									    ]       ,				)


				@require_torch
				@slow
				def   A_ (		self    :					str					):
									snake_case_									=				0.9985
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,				threshold=lowercase_					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									    ]       ,				)


				@require_torch
				@require_pytesseract
				@slow
				def   A_ (		self    :					Dict					):
									snake_case_									=				'''Narsil/layoutlmv3-finetuned-funsd'''
									snake_case_									=				0.9993

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_       ,				threshold=lowercase_					)

									snake_case_									=				object_detector(
									    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
									        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
									    ]       ,				)

