




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  __a      (  self		:     Union[str, Any]				)		->   Tuple:






       """simple docstring"""







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






  def  __a      (  self		:     Optional[int]				)		->   Tuple:






       """simple docstring"""







       lowercase :       List[Any]       =			FlaxControlNetModel.from_pretrained(
           '''lllyasviel/sd-controlnet-canny'''				,						from_pt=_A				,						dtype=jnp.bfloataa				)
       lowercase :       Any       =			FlaxStableDiffusionControlNetPipeline.from_pretrained(
           '''runwayml/stable-diffusion-v1-5'''				,						controlnet=_A				,						from_pt=_A				,						dtype=jnp.bfloataa				)
       lowercase :       List[Any]       =			controlnet_params

       lowercase :       Tuple       =			'''bird'''
       lowercase :       List[Any]       =			jax.device_count()
       lowercase :       List[Any]       =			pipe.prepare_text_inputs([prompts] * num_samples				)

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

       lowercase :       int       =			jax.random.PRNGKey(0				)
       lowercase :       Optional[Any]       =			jax.random.split(_A				,						jax.device_count()				)

       lowercase :       Optional[int]       =			replicate(_A				)
       lowercase :       Optional[int]       =			shard(_A				)
       lowercase :       List[str]       =			shard(_A				)

       lowercase :       Dict       =			pipe(
           prompt_ids=_A				,						image=_A				,						params=_A				,						prng_seed=_A				,						num_inference_steps=50				,						jit=_A				,						).images
       assert images.shape == (jax.device_count(), 1, 768, 512, 3)

       lowercase :       List[str]       =			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
       lowercase :       Tuple       =			images[0, 253:256, 253:256, -1]

       lowercase :       Dict       =			jnp.asarray(jax.device_get(image_slice.flatten()				)				)
       lowercase :       int       =			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  __a      (  self		:     Union[str, Any]				)		->   int:






       """simple docstring"""







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

       lowercase :       Optional[Any]       =			'''Chef in the kitchen'''
       lowercase :       List[str]       =			jax.device_count()
       lowercase :       List[Any]       =			pipe.prepare_text_inputs([prompts] * num_samples				)

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

       lowercase :       Any       =			jax.random.PRNGKey(0				)
       lowercase :       List[Any]       =			jax.random.split(_A				,						jax.device_count()				)

       lowercase :       Tuple       =			replicate(_A				)
       lowercase :       Optional[Any]       =			shard(_A				)
       lowercase :       Union[str, Any]       =			shard(_A				)

       lowercase :       str       =			pipe(
           prompt_ids=_A				,						image=_A				,						params=_A				,						prng_seed=_A				,						num_inference_steps=50				,						jit=_A				,						).images
       assert images.shape == (jax.device_count(), 1, 768, 512, 3)

       lowercase :       List[str]       =			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
       lowercase :       Optional[Any]       =			images[0, 253:256, 253:256, -1]

       lowercase :       str       =			jnp.asarray(jax.device_get(image_slice.flatten()				)				)
       lowercase :       Dict       =			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





from typing import Union

import fire
import torch
from tqdm import tqdm






def  snake_case(    __magic_name__			,		__magic_name__ = "cpu"			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     lowercase :       int       =			torch.load(__magic_name__			,		map_location=__magic_name__   )
     for k, v in tqdm(state_dict.items()   ):
          if not isinstance(__magic_name__			,		torch.Tensor   ):
               raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''   )
          lowercase :       Any       =			v.half()
     if save_path is None:  # overwrite src_path
          lowercase :       str       =			src_path
     torch.save(__magic_name__			,		__magic_name__   )


if __name__ == "__main__":
 fire.Fire(convert)