"""simple docstring"""






import re
from pathlib import Path
from unittest import TestCase

import pytest

@pytest.mark.integration
class 	__SCREAMING_SNAKE_CASE     (				__a       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  str					)-> Union[str, Any]:



								with open(UpperCamelCase__,  encoding='''utf-8'''					) as input_file:
											lowerCamelCase__		:		Union[str, Any]		     =re.compile(r'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''					)
											lowerCamelCase__		:		Optional[Any]		     =input_file.read()
											lowerCamelCase__		:		Union[str, Any]		     =regexp.search(UpperCamelCase__					)

								return match



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  str					)-> int:



								with open(UpperCamelCase__,  encoding='''utf-8'''					) as input_file:
											lowerCamelCase__		:		Optional[Any]		     =re.compile(r'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''',  re.DOTALL					)
											lowerCamelCase__		:		Optional[int]		     =input_file.read()
											# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
											lowerCamelCase__		:		str		     =regexp.finditer(UpperCamelCase__					)

								lowerCamelCase__		:		str		     =[match for match in matches if match is not None and match.group(1					) is not None]
								return matches[0] if matches else None



					def 		snake_case	(	self							:  Any					)-> Optional[int]:
								lowerCamelCase__		:		Union[str, Any]		     =Path('''./datasets'''					)
								lowerCamelCase__		:		Any		     =list(dataset_paths.absolute().glob('''**/*.py'''					)					)

								for dataset in dataset_files:
											if self._no_encoding_on_file_open(str(UpperCamelCase__					)					):
														raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}'''					)




					def 		snake_case	(	self							:  Tuple					)-> Any:
								lowerCamelCase__		:		Tuple		     =Path('''./datasets'''					)
								lowerCamelCase__		:		str		     =list(dataset_paths.absolute().glob('''**/*.py'''					)					)

								for dataset in dataset_files:
											if self._no_print_statements(str(UpperCamelCase__					)					):
														raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.'''					)









"""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




