

import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard

       from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline



@require_flax
class       __snake_case  (    unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      with tempfile.TemporaryDirectory() as tmpdirname:
          # pipeline has Flax weights
          SCREAMING_SNAKE_CASE_									=						FlaxDiffusionPipeline.from_pretrained(
              'hf-internal-testing/tiny-stable-diffusion-pipe'						,		safety_checker=_A						,		cache_dir=_A)

          SCREAMING_SNAKE_CASE_									=						[t[-1] for t in os.walk(os.path.join(_A						,		os.listdir(_A)[0]						,		'snapshots'))]
          SCREAMING_SNAKE_CASE_									=						[item for sublist in all_root_files for item in sublist]

          # None of the downloaded files should be a PyTorch file even if we have some here:
          # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
          assert not any(f.endswith('.bin') for f in files)






@slow
@require_flax
class       __snake_case  (    unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						FlaxStableDiffusionPipeline.from_pretrained(
          'hf-internal-testing/tiny-stable-diffusion-pipe'						,		safety_checker=_A)

      SCREAMING_SNAKE_CASE_									=						(
          'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
          ' field, close up, split lighting, cinematic'
      )

      SCREAMING_SNAKE_CASE_									=						jax.random.PRNGKey(0)
      SCREAMING_SNAKE_CASE_									=						4

      SCREAMING_SNAKE_CASE_									=						jax.device_count()
      SCREAMING_SNAKE_CASE_									=						num_samples * [prompt]
      SCREAMING_SNAKE_CASE_									=						pipeline.prepare_inputs(_A)

      # shard inputs and rng
      SCREAMING_SNAKE_CASE_									=						replicate(_A)
      SCREAMING_SNAKE_CASE_									=						jax.random.split(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						shard(_A)

      SCREAMING_SNAKE_CASE_									=						pipeline(_A						,		_A						,		_A						,		_A						,		jit=_A).images

      assert images.shape == (num_samples, 1, 64, 64, 3)
      if jax.device_count() == 8:
          assert np.abs(np.abs(images[0, 0, :2, :2, -2:]						,		dtype=np.floataa).sum() - 4.1_5_1_4_7_4_5) < 1E-3
          assert np.abs(np.abs(_A						,		dtype=np.floataa).sum() - 4_9_9_4_7.8_7_5) < 5E-1

      SCREAMING_SNAKE_CASE_									=						pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
      assert len(_A) == num_samples

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						FlaxStableDiffusionPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'						,		revision='flax'						,		safety_checker=_A)

      SCREAMING_SNAKE_CASE_									=						(
          'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
          ' field, close up, split lighting, cinematic'
      )

      SCREAMING_SNAKE_CASE_									=						jax.random.PRNGKey(0)
      SCREAMING_SNAKE_CASE_									=						50

      SCREAMING_SNAKE_CASE_									=						jax.device_count()
      SCREAMING_SNAKE_CASE_									=						num_samples * [prompt]
      SCREAMING_SNAKE_CASE_									=						pipeline.prepare_inputs(_A)

      # shard inputs and rng
      SCREAMING_SNAKE_CASE_									=						replicate(_A)
      SCREAMING_SNAKE_CASE_									=						jax.random.split(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						shard(_A)

      SCREAMING_SNAKE_CASE_									=						pipeline(_A						,		_A						,		_A						,		_A						,		jit=_A).images

      assert images.shape == (num_samples, 1, 512, 512, 3)
      if jax.device_count() == 8:
          assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,		dtype=np.floataa).sum() - 0.0_5_6_5_2_4_0_1)) < 1E-3
          assert np.abs((np.abs(_A						,		dtype=np.floataa).sum() - 2_3_8_3_8_0_8.2)) < 5E-1

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						FlaxStableDiffusionPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'						,		revision='bf16'						,		dtype=jnp.bfloataa						,		safety_checker=_A)

      SCREAMING_SNAKE_CASE_									=						(
          'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
          ' field, close up, split lighting, cinematic'
      )

      SCREAMING_SNAKE_CASE_									=						jax.random.PRNGKey(0)
      SCREAMING_SNAKE_CASE_									=						50

      SCREAMING_SNAKE_CASE_									=						jax.device_count()
      SCREAMING_SNAKE_CASE_									=						num_samples * [prompt]
      SCREAMING_SNAKE_CASE_									=						pipeline.prepare_inputs(_A)

      # shard inputs and rng
      SCREAMING_SNAKE_CASE_									=						replicate(_A)
      SCREAMING_SNAKE_CASE_									=						jax.random.split(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						shard(_A)

      SCREAMING_SNAKE_CASE_									=						pipeline(_A						,		_A						,		_A						,		_A						,		jit=_A).images

      assert images.shape == (num_samples, 1, 512, 512, 3)
      if jax.device_count() == 8:
          assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,		dtype=np.floataa).sum() - 0.0_4_0_0_3_9_0_6)) < 1E-3
          assert np.abs((np.abs(_A						,		dtype=np.floataa).sum() - 2_3_7_3_5_1_6.7_5)) < 5E-1

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						FlaxStableDiffusionPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'						,		revision='bf16'						,		dtype=jnp.bfloataa)

      SCREAMING_SNAKE_CASE_									=						(
          'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
          ' field, close up, split lighting, cinematic'
      )

      SCREAMING_SNAKE_CASE_									=						jax.random.PRNGKey(0)
      SCREAMING_SNAKE_CASE_									=						50

      SCREAMING_SNAKE_CASE_									=						jax.device_count()
      SCREAMING_SNAKE_CASE_									=						num_samples * [prompt]
      SCREAMING_SNAKE_CASE_									=						pipeline.prepare_inputs(_A)

      # shard inputs and rng
      SCREAMING_SNAKE_CASE_									=						replicate(_A)
      SCREAMING_SNAKE_CASE_									=						jax.random.split(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						shard(_A)

      SCREAMING_SNAKE_CASE_									=						pipeline(_A						,		_A						,		_A						,		_A						,		jit=_A).images

      assert images.shape == (num_samples, 1, 512, 512, 3)
      if jax.device_count() == 8:
          assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,		dtype=np.floataa).sum() - 0.0_4_0_0_3_9_0_6)) < 1E-3
          assert np.abs((np.abs(_A						,		dtype=np.floataa).sum() - 2_3_7_3_5_1_6.7_5)) < 5E-1

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						FlaxDDIMScheduler(
          beta_start=0.0_0_0_8_5						,		beta_end=0.0_1_2						,		beta_schedule='scaled_linear'						,		set_alpha_to_one=_A						,		steps_offset=1						,		)

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						FlaxStableDiffusionPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'						,		revision='bf16'						,		dtype=jnp.bfloataa						,		scheduler=_A						,		safety_checker=_A						,		)
      SCREAMING_SNAKE_CASE_									=						scheduler.create_state()

      SCREAMING_SNAKE_CASE_									=						scheduler_state

      SCREAMING_SNAKE_CASE_									=						(
          'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
          ' field, close up, split lighting, cinematic'
      )

      SCREAMING_SNAKE_CASE_									=						jax.random.PRNGKey(0)
      SCREAMING_SNAKE_CASE_									=						50

      SCREAMING_SNAKE_CASE_									=						jax.device_count()
      SCREAMING_SNAKE_CASE_									=						num_samples * [prompt]
      SCREAMING_SNAKE_CASE_									=						pipeline.prepare_inputs(_A)

      # shard inputs and rng
      SCREAMING_SNAKE_CASE_									=						replicate(_A)
      SCREAMING_SNAKE_CASE_									=						jax.random.split(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						shard(_A)

      SCREAMING_SNAKE_CASE_									=						pipeline(_A						,		_A						,		_A						,		_A						,		jit=_A).images

      assert images.shape == (num_samples, 1, 512, 512, 3)
      if jax.device_count() == 8:
          assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,		dtype=np.floataa).sum() - 0.0_4_5_0_4_3_9_4_5)) < 1E-3
          assert np.abs((np.abs(_A						,		dtype=np.floataa).sum() - 2_3_4_7_6_9_3.5)) < 5E-1


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						(
          'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
          ' field, close up, split lighting, cinematic'
      )

      SCREAMING_SNAKE_CASE_									=						jax.device_count()
      SCREAMING_SNAKE_CASE_									=						num_samples * [prompt]
      SCREAMING_SNAKE_CASE_									=						jax.random.split(jax.random.PRNGKey(0)						,		_A)

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						FlaxStableDiffusionPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'						,		revision='bf16'						,		dtype=jnp.bfloataa						,		safety_checker=_A						,		)

      SCREAMING_SNAKE_CASE_									=						replicate(_A)
      SCREAMING_SNAKE_CASE_									=						pipeline.prepare_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						shard(_A)
      SCREAMING_SNAKE_CASE_									=						pipeline(_A						,		_A						,		_A						,		jit=_A).images
      assert images.shape == (num_samples, 1, 512, 512, 3)
      SCREAMING_SNAKE_CASE_									=						images[2, 0, 256, 10:17, 1]

      # With memory efficient attention
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						FlaxStableDiffusionPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'						,		revision='bf16'						,		dtype=jnp.bfloataa						,		safety_checker=_A						,		use_memory_efficient_attention=_A						,		)

      SCREAMING_SNAKE_CASE_									=						replicate(_A)
      SCREAMING_SNAKE_CASE_									=						pipeline.prepare_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						shard(_A)
      SCREAMING_SNAKE_CASE_									=						pipeline(_A						,		_A						,		_A						,		jit=_A).images
      assert images_eff.shape == (num_samples, 1, 512, 512, 3)
      SCREAMING_SNAKE_CASE_									=						images[2, 0, 256, 10:17, 1]

      # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
      # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
      assert abs(slice_eff - slice).max() < 1E-2





import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		int=7       ):





    """simple docstring"""


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

    # The id of a workflow (not of a workflow run)
    SCREAMING_SNAKE_CASE_									=						'636036'

    SCREAMING_SNAKE_CASE_									=						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}"""

    SCREAMING_SNAKE_CASE_									=						requests.get(_SCREAMING_SNAKE_CASE ,   headers=_SCREAMING_SNAKE_CASE       ).json()

    return result["workflow_runs"]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						get_daily_ci_runs(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						None
    for workflow_run in workflow_runs:
        if workflow_run["status"] == "completed":
            SCREAMING_SNAKE_CASE_									=						workflow_run['id']
            break

    return workflow_run_id


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


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





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    get_last_daily_ci_artifacts(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

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

    return results


