"""simple docstring"""






import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:

		class 	__SCREAMING_SNAKE_CASE     :


							'''simple docstring'''





							@staticmethod
							def 		snake_case	(	*lowerCamelCase							:  int,  **lowerCamelCase							:  str					)-> Any:
										pass






def 					snake_case__       (		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple		     =hashlib.mda(image.tobytes()     )
			return m.hexdigest()

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					_a             =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  int,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[Any]					)-> Optional[int]:
								lowerCamelCase__		:		int		     =DepthEstimationPipeline(model=_UpperCAmelCase,  image_processor=_UpperCAmelCase					)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]



					def 		snake_case	(	self							:  Any,  lowerCamelCase							:  Dict,  lowerCamelCase							:  str					)-> Dict:
								lowerCamelCase__		:		List[str]		     =depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
								self.assertEqual({'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},  _UpperCAmelCase					)
								import datasets

								lowerCamelCase__		:		Optional[int]		     =datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''',  '''image''',  split='''test'''					)
								lowerCamelCase__		:		Dict		     =depth_estimator(
								    [
								        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'''],
								    ]					)
								self.assertEqual(
								    [
								        {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
								        {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
								        {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
								        {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
								        {'''predicted_depth''': ANY(torch.Tensor					), '''depth''': ANY(Image.Image					)},
								    ],  _UpperCAmelCase,  )



					@require_tf
					@unittest.skip('''Depth estimation is not implemented in TF'''					)
					def 		snake_case	(	self							:  List[str]					)-> Dict:
								pass



					@slow
					@require_torch
					def 		snake_case	(	self							:  Optional[Any]					)-> List[Any]:
								lowerCamelCase__		:		List[str]		     ='Intel/dpt-large'
								lowerCamelCase__		:		Optional[int]		     =pipeline('''depth-estimation''',  model=_UpperCAmelCase					)
								lowerCamelCase__		:		Optional[int]		     =depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
								lowerCamelCase__		:		Any		     =hashimage(outputs['''depth''']					)

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()					),  29.304					)
								self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()					),  2.662					)




					@require_torch
					def 		snake_case	(	self							:  Tuple					)-> int:
								# This is highly irregular to have no small tests.
								self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''					)









"""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 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[int]		     =controlnet_params

								lowerCamelCase__		:		Dict		     ='''bird'''
								lowerCamelCase__		:		List[str]		     =jax.device_count()
								lowerCamelCase__		:		Optional[Any]		     =pipe.prepare_text_inputs([prompts] * num_samples					)

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

								lowerCamelCase__		:		Optional[int]		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Dict		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		Tuple		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		Any		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		Optional[Any]		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Dict		     =jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[Any]		     =controlnet_params

								lowerCamelCase__		:		int		     ='''Chef in the kitchen'''
								lowerCamelCase__		:		Optional[Any]		     =jax.device_count()
								lowerCamelCase__		:		Any		     =pipe.prepare_text_inputs([prompts] * num_samples					)

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

								lowerCamelCase__		:		Tuple		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Optional[Any]		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		int		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		int		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		List[str]		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		int		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Any		     =jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




