

"""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        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      FlaxControlNetModel.from_pretrained(
          '''lllyasviel/sd-controlnet-canny''' ,						from_pt=UpperCamelCase_ ,						dtype=jnp.bfloataa       )
      __magic_name__   ,						__magic_name__     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5''' ,						controlnet=UpperCamelCase_ ,						from_pt=UpperCamelCase_ ,						dtype=jnp.bfloataa       )
      __magic_name__     =      controlnet_params

      __magic_name__     =      '''bird'''
      __magic_name__     =      jax.device_count()
      __magic_name__     =      pipe.prepare_text_inputs([prompts] * num_samples       )

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

      __magic_name__     =      jax.random.PRNGKey(0       )
      __magic_name__     =      jax.random.split(UpperCamelCase_ ,						jax.device_count()       )

      __magic_name__     =      replicate(UpperCamelCase_       )
      __magic_name__     =      shard(UpperCamelCase_       )
      __magic_name__     =      shard(UpperCamelCase_       )

      __magic_name__     =      pipe(
          prompt_ids=UpperCamelCase_ ,						image=UpperCamelCase_ ,						params=UpperCamelCase_ ,						prng_seed=UpperCamelCase_ ,						num_inference_steps=50 ,						jit=UpperCamelCase_ ,						).images
      assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

      __magic_name__     =      jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      __magic_name__     =      jnp.array(
          [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]       )
      print(f'''output_slice: {output_slice}'''       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      FlaxControlNetModel.from_pretrained(
          '''lllyasviel/sd-controlnet-openpose''' ,						from_pt=UpperCamelCase_ ,						dtype=jnp.bfloataa       )
      __magic_name__   ,						__magic_name__     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5''' ,						controlnet=UpperCamelCase_ ,						from_pt=UpperCamelCase_ ,						dtype=jnp.bfloataa       )
      __magic_name__     =      controlnet_params

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

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

      __magic_name__     =      jax.random.PRNGKey(0       )
      __magic_name__     =      jax.random.split(UpperCamelCase_ ,						jax.device_count()       )

      __magic_name__     =      replicate(UpperCamelCase_       )
      __magic_name__     =      shard(UpperCamelCase_       )
      __magic_name__     =      shard(UpperCamelCase_       )

      __magic_name__     =      pipe(
          prompt_ids=UpperCamelCase_ ,						image=UpperCamelCase_ ,						params=UpperCamelCase_ ,						prng_seed=UpperCamelCase_ ,						num_inference_steps=50 ,						jit=UpperCamelCase_ ,						).images
      assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

      __magic_name__     =      jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      __magic_name__     =      jnp.array(
          [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]       )
      print(f'''output_slice: {output_slice}'''       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2




"""simple docstring"""






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCamelCase										=		[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]





def 	lowercase      (   __UpperCamelCase=True						) ->      List[Any]:
 if with_config:
  return [
      {
          "testcase_name": d["dataset"] + "/" + d["config_name"],
          "dataset": d["dataset"],
          "config_name": d["config_name"],
      }
      for d in DATASETS_ON_HF_GCP
  ]
 else:
  return [
      {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
  ]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__UpperCAmelCase     )     )
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       None
     _lowerCamelCase     =       None







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      with TemporaryDirectory() as tmp_dir:
       __magic_name__     =      dataset_module_factory(UpperCamelCase_ ,						cache_dir=UpperCamelCase_       )

       __magic_name__     =      import_main_class(dataset_module.module_path ,						dataset=UpperCamelCase_       )

       __magic_name__     =      builder_cls(
           cache_dir=UpperCamelCase_ ,						config_name=UpperCamelCase_ ,						hash=dataset_module.hash ,						)

       __magic_name__     =      '''/'''.join(
           [
               HF_GCP_BASE_URL,
               builder_instance._relative_data_dir(with_hash=UpperCamelCase_       ).replace(os.sep ,						'''/'''       ),
               config.DATASET_INFO_FILENAME,
           ]       )
       __magic_name__     =      cached_path(UpperCamelCase_ ,						cache_dir=UpperCamelCase_       )
       self.assertTrue(os.path.exists(UpperCamelCase_       )       )





@pytest.mark.integration
def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      tmp_path_factory.mktemp('''test_hf_gcp'''						) / '''test_wikipedia_simple'''
 __magic_name__     =      dataset_module_factory('''wikipedia'''      ,   cache_dir=__UpperCamelCase						)
 __magic_name__     =      import_main_class(dataset_module.module_path						)
 __magic_name__     =      builder_cls(
     cache_dir=__UpperCamelCase      ,   config_name='''20220301.frr'''      ,   hash=dataset_module.hash      ,   )
 # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
 __magic_name__     =      None
 builder_instance.download_and_prepare()
 __magic_name__     =      builder_instance.as_dataset()
 assert ds





@pytest.mark.integration
def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      dataset_module_factory('''wikipedia'''      ,   cache_dir=__UpperCamelCase						)
 __magic_name__     =      import_main_class(dataset_module.module_path      ,   dataset=__UpperCamelCase						)
 __magic_name__     =      builder_cls(
     cache_dir=__UpperCamelCase      ,   config_name='''20220301.frr'''      ,   hash=dataset_module.hash      ,   )
 __magic_name__     =      builder_instance.as_streaming_dataset()
 assert ds
 assert isinstance(__UpperCamelCase      ,   __UpperCamelCase						)
 assert "train" in ds
 assert isinstance(ds['''train''']      ,   __UpperCamelCase						)
 assert next(iter(ds['''train''']						)						)

