





'''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  _A (					unittest.TestCase		):

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Any			)					->    Tuple:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Dict							      =     FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-canny"""  ,      from_pt=__magic_name__  ,      dtype=jnp.bfloataa			)
					__snake_case      ,     __snake_case					:      Tuple							      =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""  ,      controlnet=__magic_name__  ,      from_pt=__magic_name__  ,      dtype=jnp.bfloataa			)
					__snake_case					:      Tuple							      =     controlnet_params

					__snake_case					:      Tuple							      =     """bird"""
					__snake_case					:      Dict							      =     jax.device_count()
					__snake_case					:      Any							      =     pipe.prepare_text_inputs([prompts] * num_samples			)

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

					__snake_case					:      int							      =     jax.random.PRNGKey(0			)
					__snake_case					:      Dict							      =     jax.random.split(__magic_name__  ,      jax.device_count()			)

					__snake_case					:      Any							      =     replicate(__magic_name__			)
					__snake_case					:      str							      =     shard(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     shard(__magic_name__			)

					__snake_case					:      List[str]							      =     pipe(
					    prompt_ids=__magic_name__  ,      image=__magic_name__  ,      params=__magic_name__  ,      prng_seed=__magic_name__  ,      num_inference_steps=50  ,      jit=__magic_name__  ,      ).images
					assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

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

					__snake_case					:      str							      =     jnp.asarray(jax.device_get(image_slice.flatten()			)			)
					__snake_case					:      Optional[int]							      =     jnp.array(
					    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]			)
					print(f'''output_slice: {output_slice}'''			)
					assert jnp.abs(output_slice - expected_slice			).max() < 1E-2

		def 	lowercase__     (   self					:   Any			)					->    List[str]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Any							      =     FlaxControlNetModel.from_pretrained(
					    """lllyasviel/sd-controlnet-openpose"""  ,      from_pt=__magic_name__  ,      dtype=jnp.bfloataa			)
					__snake_case      ,     __snake_case					:      Tuple							      =     FlaxStableDiffusionControlNetPipeline.from_pretrained(
					    """runwayml/stable-diffusion-v1-5"""  ,      controlnet=__magic_name__  ,      from_pt=__magic_name__  ,      dtype=jnp.bfloataa			)
					__snake_case					:      List[Any]							      =     controlnet_params

					__snake_case					:      Dict							      =     """Chef in the kitchen"""
					__snake_case					:      Dict							      =     jax.device_count()
					__snake_case					:      str							      =     pipe.prepare_text_inputs([prompts] * num_samples			)

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

					__snake_case					:      Any							      =     jax.random.PRNGKey(0			)
					__snake_case					:      Optional[Any]							      =     jax.random.split(__magic_name__  ,      jax.device_count()			)

					__snake_case					:      List[Any]							      =     replicate(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     shard(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     shard(__magic_name__			)

					__snake_case					:      Optional[int]							      =     pipe(
					    prompt_ids=__magic_name__  ,      image=__magic_name__  ,      params=__magic_name__  ,      prng_seed=__magic_name__  ,      num_inference_steps=50  ,      jit=__magic_name__  ,      ).images
					assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

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

					__snake_case					:      Any							      =     jnp.asarray(jax.device_get(image_slice.flatten()			)			)
					__snake_case					:      Optional[int]							      =     jnp.array(
					    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]			)
					print(f'''output_slice: {output_slice}'''			)
					assert jnp.abs(output_slice - expected_slice			).max() < 1E-2







# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowerCamelCase		       =      subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
lowerCamelCase		       =      subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()

lowerCamelCase		       =      """|""".join(sys.argv[1:])
lowerCamelCase		       =      re.compile(RF'^({joined_dirs}).*?\.py$')

lowerCamelCase		       =      [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


