



'''simple docstring'''




import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image
else:






      class 		_UpperCamelCase						:


         '''simple docstring'''

         @staticmethod
         def UpperCamelCase__      (							*lowerCAmelCase__  :				Tuple							,  **lowerCAmelCase__  :				Tuple		):





           """simple docstring"""






           pass






@is_pipeline_test
@require_vision
@require_torch
class 		_UpperCamelCase						(							unittest.TestCase					):


   '''simple docstring'''

   _A   :	Any    					=	MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

   def UpperCamelCase__      (							self  :				Optional[Any]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				List[Any]		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	str					 =	pipeline(
         """zero-shot-object-detection"""							,  model="""hf-internal-testing/tiny-random-owlvit-object-detection"""		)

     __SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[
         {
             """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
             """candidate_labels""": ["""cat""", """remote""", """couch"""],
         }
     ]
     return object_detector, examples

   def UpperCamelCase__      (							self  :				Optional[Any]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Optional[Any]		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	Any					 =	object_detector(examples[0]							,  threshold=0.0		)

     __SCREAMING_SNAKE_CASE :	Tuple					 =	len(lowerCAmelCase_		)
     self.assertGreater(lowerCAmelCase_							,  0		)
     self.assertEqual(
         lowerCAmelCase_							,  [
             {
                 """score""": ANY(lowerCAmelCase_		),
                 """label""": ANY(lowerCAmelCase_		),
                 """box""": {"""xmin""": ANY(lowerCAmelCase_		), """ymin""": ANY(lowerCAmelCase_		), """xmax""": ANY(lowerCAmelCase_		), """ymax""": ANY(lowerCAmelCase_		)},
             }
             for i in range(lowerCAmelCase_		)
         ]							,  )

   @require_tf
   @unittest.skip("""Zero Shot Object Detection not implemented in TF"""		)
   def UpperCamelCase__      (							self  :				Any		):





     """simple docstring"""






     pass

   @require_torch
   def UpperCamelCase__      (							self  :				List[Any]		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	int					 =	pipeline(
         """zero-shot-object-detection"""							,  model="""hf-internal-testing/tiny-random-owlvit-object-detection"""		)

     __SCREAMING_SNAKE_CASE :	Optional[int]					 =	object_detector(
         """./tests/fixtures/tests_samples/COCO/000000039769.png"""							,  candidate_labels=["""cat""", """remote""", """couch"""]							,  threshold=0.64							,  )

     self.assertEqual(
         nested_simplify(lowerCAmelCase_							,  decimals=4		)							,  [
             {"""score""": 0.72_35, """label""": """cat""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
             {"""score""": 0.72_18, """label""": """remote""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
             {"""score""": 0.71_84, """label""": """couch""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
             {"""score""": 0.67_48, """label""": """remote""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
             {"""score""": 0.66_56, """label""": """cat""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
             {"""score""": 0.66_14, """label""": """couch""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
             {"""score""": 0.64_56, """label""": """remote""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
             {"""score""": 0.6_42, """label""": """remote""", """box""": {"""xmin""": 6_7, """ymin""": 2_7_4, """xmax""": 9_3, """ymax""": 2_9_7}},
             {"""score""": 0.64_19, """label""": """cat""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
         ]							,  )

     __SCREAMING_SNAKE_CASE :	List[str]					 =	object_detector(
         [
             {
                 """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
                 """candidate_labels""": ["""cat""", """remote""", """couch"""],
             }
         ]							,  threshold=0.64							,  )

     self.assertEqual(
         nested_simplify(lowerCAmelCase_							,  decimals=4		)							,  [
             [
                 {"""score""": 0.72_35, """label""": """cat""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
                 {"""score""": 0.72_18, """label""": """remote""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
                 {"""score""": 0.71_84, """label""": """couch""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
                 {"""score""": 0.67_48, """label""": """remote""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
                 {"""score""": 0.66_56, """label""": """cat""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
                 {"""score""": 0.66_14, """label""": """couch""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
                 {"""score""": 0.64_56, """label""": """remote""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
                 {"""score""": 0.6_42, """label""": """remote""", """box""": {"""xmin""": 6_7, """ymin""": 2_7_4, """xmax""": 9_3, """ymax""": 2_9_7}},
                 {"""score""": 0.64_19, """label""": """cat""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
             ]
         ]							,  )

   @require_torch
   @slow
   def UpperCamelCase__      (							self  :				Dict		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	Any					 =	pipeline("""zero-shot-object-detection"""		)

     __SCREAMING_SNAKE_CASE :	Any					 =	object_detector(
         """http://images.cocodataset.org/val2017/000000039769.jpg"""							,  candidate_labels=["""cat""", """remote""", """couch"""]							,  )
     self.assertEqual(
         nested_simplify(lowerCAmelCase_							,  decimals=4		)							,  [
             {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
             {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
             {"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
             {"""score""": 0.14_74, """label""": """remote""", """box""": {"""xmin""": 3_3_5, """ymin""": 7_4, """xmax""": 3_7_1, """ymax""": 1_8_7}},
             {"""score""": 0.12_08, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_4_2, """ymax""": 4_7_6}},
         ]							,  )

     __SCREAMING_SNAKE_CASE :	Optional[int]					 =	object_detector(
         [
             {
                 """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """candidate_labels""": ["""cat""", """remote""", """couch"""],
             },
             {
                 """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """candidate_labels""": ["""cat""", """remote""", """couch"""],
             },
         ]							,  )
     self.assertEqual(
         nested_simplify(lowerCAmelCase_							,  decimals=4		)							,  [
             [
                 {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
                 {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
                 {"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
                 {"""score""": 0.14_74, """label""": """remote""", """box""": {"""xmin""": 3_3_5, """ymin""": 7_4, """xmax""": 3_7_1, """ymax""": 1_8_7}},
                 {"""score""": 0.12_08, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_4_2, """ymax""": 4_7_6}},
             ],
             [
                 {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
                 {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
                 {"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
                 {"""score""": 0.14_74, """label""": """remote""", """box""": {"""xmin""": 3_3_5, """ymin""": 7_4, """xmax""": 3_7_1, """ymax""": 1_8_7}},
                 {"""score""": 0.12_08, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_4_2, """ymax""": 4_7_6}},
             ],
         ]							,  )

   @require_tf
   @unittest.skip("""Zero Shot Object Detection not implemented in TF"""		)
   def UpperCamelCase__      (							self  :				List[Any]		):





     """simple docstring"""






     pass

   @require_torch
   @slow
   def UpperCamelCase__      (							self  :				List[str]		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	0.2
     __SCREAMING_SNAKE_CASE :	Dict					 =	pipeline("""zero-shot-object-detection"""		)

     __SCREAMING_SNAKE_CASE :	Dict					 =	object_detector(
         """http://images.cocodataset.org/val2017/000000039769.jpg"""							,  candidate_labels=["""cat""", """remote""", """couch"""]							,  threshold=lowerCAmelCase_							,  )
     self.assertEqual(
         nested_simplify(lowerCAmelCase_							,  decimals=4		)							,  [
             {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
             {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
             {"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
         ]							,  )



   @require_torch
   @slow
   def UpperCamelCase__      (							self  :				Any		):





     """simple docstring"""






     __SCREAMING_SNAKE_CASE :	Optional[Any]					 =	2
     __SCREAMING_SNAKE_CASE :	Optional[int]					 =	pipeline("""zero-shot-object-detection"""		)

     __SCREAMING_SNAKE_CASE :	List[str]					 =	object_detector(
         """http://images.cocodataset.org/val2017/000000039769.jpg"""							,  candidate_labels=["""cat""", """remote""", """couch"""]							,  top_k=lowerCAmelCase_							,  )
     self.assertEqual(
         nested_simplify(lowerCAmelCase_							,  decimals=4		)							,  [
             {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
             {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
         ]							,  )




'''simple docstring'''




import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard






@slow
@require_flax
class 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					super().tearDown()
					gc.collect()

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	int					 =	FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-canny"""							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,  controlnet=lowerCAmelCase__							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE :	str					 =	controlnet_params

					__SCREAMING_SNAKE_CASE :	str					 =	"""bird"""
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	jax.device_count()
					__SCREAMING_SNAKE_CASE :	str					 =	pipe.prepare_text_inputs([prompts] * num_samples		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	pipe.prepare_image_inputs([canny_image] * num_samples		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	jax.random.PRNGKey(0		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	jax.random.split(lowerCAmelCase__							,  jax.device_count()		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	replicate(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	shard(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	shard(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	pipe(
					    prompt_ids=lowerCAmelCase__							,  image=lowerCAmelCase__							,  params=lowerCAmelCase__							,  prng_seed=lowerCAmelCase__							,  num_inference_steps=5_0							,  jit=lowerCAmelCase__							,  ).images
					assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jnp.asarray(jax.device_get(image_slice.flatten()		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	jnp.array(
					    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
					print(F"output_slice: {output_slice}"		)
					assert jnp.abs(output_slice - expected_slice		).max() < 1E-2



			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-openpose"""							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Any					 =	FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""							,  controlnet=lowerCAmelCase__							,  from_pt=lowerCAmelCase__							,  dtype=jnp.bfloataa		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	controlnet_params

					__SCREAMING_SNAKE_CASE :	List[str]					 =	"""Chef in the kitchen"""
					__SCREAMING_SNAKE_CASE :	int					 =	jax.device_count()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	pipe.prepare_text_inputs([prompts] * num_samples		)

					__SCREAMING_SNAKE_CASE :	Any					 =	load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	pipe.prepare_image_inputs([pose_image] * num_samples		)

					__SCREAMING_SNAKE_CASE :	Dict					 =	jax.random.PRNGKey(0		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	jax.random.split(lowerCAmelCase__							,  jax.device_count()		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	replicate(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	shard(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	shard(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	pipe(
					    prompt_ids=lowerCAmelCase__							,  image=lowerCAmelCase__							,  params=lowerCAmelCase__							,  prng_seed=lowerCAmelCase__							,  num_inference_steps=5_0							,  jit=lowerCAmelCase__							,  ).images
					assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

					__SCREAMING_SNAKE_CASE :	Tuple					 =	jnp.asarray(jax.device_get(image_slice.flatten()		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	jnp.array(
					    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
					print(F"output_slice: {output_slice}"		)
					assert jnp.abs(output_slice - expected_slice		).max() < 1E-2