





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							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()

 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=FlaxControlNetModel.from_pretrained(
        "lllyasviel/sd-controlnet-canny",		from_pt=A_,		dtype=jnp.bfloataa		)
    UpperCAmelCase__			,							UpperCAmelCase__						=FlaxStableDiffusionControlNetPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",		controlnet=A_,		from_pt=A_,		dtype=jnp.bfloataa		)
    UpperCAmelCase__						=controlnet_params

    UpperCAmelCase__						="bird"
    UpperCAmelCase__						=jax.device_count()
    UpperCAmelCase__						=pipe.prepare_text_inputs([prompts] * num_samples		)

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

    UpperCAmelCase__						=jax.random.PRNGKey(0		)
    UpperCAmelCase__						=jax.random.split(A_,		jax.device_count()		)

    UpperCAmelCase__						=replicate(A_		)
    UpperCAmelCase__						=shard(A_		)
    UpperCAmelCase__						=shard(A_		)

    UpperCAmelCase__						=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)

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

    UpperCAmelCase__						=jnp.asarray(jax.device_get(image_slice.flatten()		)		)
    UpperCAmelCase__						=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  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=FlaxControlNetModel.from_pretrained(
        "lllyasviel/sd-controlnet-openpose",		from_pt=A_,		dtype=jnp.bfloataa		)
    UpperCAmelCase__			,							UpperCAmelCase__						=FlaxStableDiffusionControlNetPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",		controlnet=A_,		from_pt=A_,		dtype=jnp.bfloataa		)
    UpperCAmelCase__						=controlnet_params

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

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

    UpperCAmelCase__						=jax.random.PRNGKey(0		)
    UpperCAmelCase__						=jax.random.split(A_,		jax.device_count()		)

    UpperCAmelCase__						=replicate(A_		)
    UpperCAmelCase__						=shard(A_		)
    UpperCAmelCase__						=shard(A_		)

    UpperCAmelCase__						=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)

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

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










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 				_UpperCAmelCase			(    A				,							A=7     ):




   '''simple docstring'''

   UpperCAmelCase__						=None
   if token is not None:
      UpperCAmelCase__						={"Accept": "application/vnd.github+json", "Authorization": F"""Bearer {token}"""}

   # The id of a workflow (not of a workflow run)
   UpperCAmelCase__						="636036"

   UpperCAmelCase__						=F"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
   # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
   url += F"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

   UpperCAmelCase__						=requests.get(A				,							headers=A     ).json()

   return result["workflow_runs"]






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_daily_ci_runs(A     )
   UpperCAmelCase__						=None
   for workflow_run in workflow_runs:
      if workflow_run["status"] == "completed":
         UpperCAmelCase__						=workflow_run["id"]
         break

   return workflow_run_id






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_last_daily_ci_runs(A     )
   if workflow_run_id is not None:
      UpperCAmelCase__						=get_artifacts_links(worflow_run_id=A				,							token=A     )
      for artifact_name in artifact_names:
         if artifact_name in artifacts_links:
            UpperCAmelCase__						=artifacts_links[artifact_name]
            download_artifact(
                artifact_name=A				,							artifact_url=A				,							output_dir=A				,							token=A     )






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   get_last_daily_ci_artifacts(A				,							A				,							A     )

   UpperCAmelCase__						={}
   for artifact_name in artifact_names:
      UpperCAmelCase__						=os.path.join(A				,							F"""{artifact_name}.zip"""     )
      if os.path.isfile(A     ):
         UpperCAmelCase__						={}
         with zipfile.ZipFile(A     ) as z:
            for filename in z.namelist():
               if not os.path.isdir(A     ):
                  # read the file
                  with z.open(A     ) as f:
                     UpperCAmelCase__						=f.read().decode("UTF-8"     )

   return results



