




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  A		(  unittest.TestCase     ):



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_      			=  FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',	from_pt=UpperCamelCase__,	dtype=jnp.bfloataa   )
								lowerCAmelCase_			,					lowerCAmelCase_      			=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',	controlnet=UpperCamelCase__,	from_pt=UpperCamelCase__,	dtype=jnp.bfloataa   )
								lowerCAmelCase_      			=  controlnet_params

								lowerCAmelCase_      			=  '''bird'''
								lowerCAmelCase_      			=  jax.device_count()
								lowerCAmelCase_      			=  pipe.prepare_text_inputs([prompts] * num_samples   )

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

								lowerCAmelCase_      			=  jax.random.PRNGKey(0   )
								lowerCAmelCase_      			=  jax.random.split(UpperCamelCase__,	jax.device_count()   )

								lowerCAmelCase_      			=  replicate(UpperCamelCase__   )
								lowerCAmelCase_      			=  shard(UpperCamelCase__   )
								lowerCAmelCase_      			=  shard(UpperCamelCase__   )

								lowerCAmelCase_      			=  pipe(
								    prompt_ids=UpperCamelCase__,	image=UpperCamelCase__,	params=UpperCamelCase__,	prng_seed=UpperCamelCase__,	num_inference_steps=50,	jit=UpperCamelCase__,	).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

								lowerCAmelCase_      			=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
								lowerCAmelCase_      			=  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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_      			=  FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',	from_pt=UpperCamelCase__,	dtype=jnp.bfloataa   )
								lowerCAmelCase_			,					lowerCAmelCase_      			=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',	controlnet=UpperCamelCase__,	from_pt=UpperCamelCase__,	dtype=jnp.bfloataa   )
								lowerCAmelCase_      			=  controlnet_params

								lowerCAmelCase_      			=  '''Chef in the kitchen'''
								lowerCAmelCase_      			=  jax.device_count()
								lowerCAmelCase_      			=  pipe.prepare_text_inputs([prompts] * num_samples   )

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

								lowerCAmelCase_      			=  jax.random.PRNGKey(0   )
								lowerCAmelCase_      			=  jax.random.split(UpperCamelCase__,	jax.device_count()   )

								lowerCAmelCase_      			=  replicate(UpperCamelCase__   )
								lowerCAmelCase_      			=  shard(UpperCamelCase__   )
								lowerCAmelCase_      			=  shard(UpperCamelCase__   )

								lowerCAmelCase_      			=  pipe(
								    prompt_ids=UpperCamelCase__,	image=UpperCamelCase__,	params=UpperCamelCase__,	prng_seed=UpperCamelCase__,	num_inference_steps=50,	jit=UpperCamelCase__,	).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

								lowerCAmelCase_      			=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
								lowerCAmelCase_      			=  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










import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
						import torch

						from transformers import AutoModelForImageClassification

if is_vision_available():
						from transformers import AutoImageProcessor


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



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								lowerCAmelCase_      			=  AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								model.to(UpperCamelCase__   )

								from datasets import load_dataset

								lowerCAmelCase_      			=  load_dataset('''nielsr/rvlcdip-demo'''   )

								lowerCAmelCase_      			=  dataset['''train'''][0]['''image'''].convert('''RGB'''   )

								lowerCAmelCase_      			=  image_processor(UpperCamelCase__,	return_tensors='''pt'''   ).to(UpperCamelCase__   )

								# forward pass
								with torch.no_grad():
														lowerCAmelCase_      			=  model(**UpperCamelCase__   )
														lowerCAmelCase_      			=  outputs.logits

								lowerCAmelCase_      			=  torch.Size((1, 16)   )
								self.assertEqual(logits.shape,	UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347],	device=UpperCamelCase__,	dtype=torch.float,	)
								self.assertTrue(torch.allclose(logits[0, :3],	UpperCamelCase__,	atol=1E-4   )   )




