


from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 	snake_case_       (lowerCamelCase_				):
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Union[str, Any]:
												return [
												    {"col_1": 3, "col_2": "a"},
												    {"col_1": 2, "col_2": "b"},
												    {"col_1": 1, "col_2": "c"},
												    {"col_1": 0, "col_2": "d"},
												]
					def 			lowerCamelCase__(				self       :Any				) ->   Any:
												a__					=					{'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
												return Dataset.from_dict(__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Tuple:
												a__					=					self._create_example_records()
												a__					=					Dataset.from_list(__snake_case				)
												self.assertListEqual(dset.column_names      ,['col_1', 'col_2']				)
												for i, r in enumerate(__snake_case				):
																			self.assertDictEqual(__snake_case      ,example_records[i]				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Dict:
												a__					=					self._create_example_records()
												a__					=					Dataset.from_list(__snake_case				)
												a__					=					Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}				)
												self.assertEqual(dset.info      ,dset_from_dict.info				)
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Union[str, Any]:  # checks what happens with missing columns
												a__					=					[{'col_1': 1}, {'col_2': 'x'}]
												a__					=					Dataset.from_list(__snake_case				)
												self.assertDictEqual(dset[0]      ,{'col_1': 1}				)
												self.assertDictEqual(dset[1]      ,{'col_1': None}				)  # NB: first record is used for columns
					def 			lowerCamelCase__(				self       :Any				) ->   Tuple:  # checks if the type can be inferred from the second record
												a__					=					[{'col_1': []}, {'col_1': [1, 2]}]
												a__					=					Dataset.from_list(__snake_case				)
												self.assertEqual(dset.info.features['col_1']      ,Sequence(Value('int64'				)				)				)



					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Optional[Any]:
												a__					=					Dataset.from_list([]				)
												self.assertEqual(len(__snake_case				)      ,0				)
												self.assertListEqual(dset.column_names      ,[]				)








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 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :List[Any]				) ->   str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
					def 			lowerCamelCase__(				self       :int				) ->   Optional[Any]:
												a__				, a__					=					FlaxControlNetModel.from_pretrained(
												    'lllyasviel/sd-controlnet-canny'      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__				, a__					=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    'runwayml/stable-diffusion-v1-5'      ,controlnet=__snake_case      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__					=					controlnet_params

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

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

												a__					=					jax.random.PRNGKey(0				)
												a__					=					jax.random.split(__snake_case      ,jax.device_count()				)

												a__					=					replicate(__snake_case				)
												a__					=					shard(__snake_case				)
												a__					=					shard(__snake_case				)

												a__					=					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, 7_68, 5_12, 3)

												a__					=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
												a__					=					images[0, 2_53:2_56, 2_53:2_56, -1]

												a__					=					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
												a__					=					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 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[Any]:
												a__				, a__					=					FlaxControlNetModel.from_pretrained(
												    'lllyasviel/sd-controlnet-openpose'      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__				, a__					=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    'runwayml/stable-diffusion-v1-5'      ,controlnet=__snake_case      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__					=					controlnet_params

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

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

												a__					=					jax.random.PRNGKey(0				)
												a__					=					jax.random.split(__snake_case      ,jax.device_count()				)

												a__					=					replicate(__snake_case				)
												a__					=					shard(__snake_case				)
												a__					=					shard(__snake_case				)

												a__					=					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, 7_68, 5_12, 3)

												a__					=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
												a__					=					images[0, 2_53:2_56, 2_53:2_56, -1]

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




