

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 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
											snake_case_          =   AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
											model.to(snake_case	)

											from datasets import load_dataset

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

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

											snake_case_          =   image_processor(snake_case				,       return_tensors='pt'	).to(snake_case	)

											# forward pass
											with torch.no_grad():
																		snake_case_          =   model(**snake_case	)
																		snake_case_          =   outputs.logits

											snake_case_          =   torch.Size((1, 16)	)
											self.assertEqual(logits.shape				,       snake_case	)

											snake_case_          =   torch.tensor(
											    [-0.41_58, -0.40_92, -0.43_47]				,       device=snake_case				,       dtype=torch.float				,       )
											self.assertTrue(torch.allclose(logits[0, :3]				,       snake_case				,       atol=1e-4	)	)







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 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-canny'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_          =   controlnet_params

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

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

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

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

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

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   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    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-openpose'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_          =   controlnet_params

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

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

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

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

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

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




