




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       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Dict:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Optional[int]       =      FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-canny'''       ,from_pt=_SCREAMING_SNAKE_CASE       ,dtype=jnp.bfloataa  )
         UpperCAmelCase_      :							Any       =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''       ,controlnet=_SCREAMING_SNAKE_CASE       ,from_pt=_SCREAMING_SNAKE_CASE       ,dtype=jnp.bfloataa  )
         UpperCAmelCase_      :							Optional[int]       =      controlnet_params

         UpperCAmelCase_      :							Union[str, Any]       =      '''bird'''
         UpperCAmelCase_      :							List[str]       =      jax.device_count()
         UpperCAmelCase_      :							Any       =      pipe.prepare_text_inputs([prompts] * num_samples  )

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

         UpperCAmelCase_      :							Optional[Any]       =      jax.random.PRNGKey(0  )
         UpperCAmelCase_      :							List[Any]       =      jax.random.split(_SCREAMING_SNAKE_CASE       ,jax.device_count()  )

         UpperCAmelCase_      :							Any       =      replicate(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      shard(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[int]       =      shard(_SCREAMING_SNAKE_CASE  )

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

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

         UpperCAmelCase_      :							Optional[Any]       =      jnp.asarray(jax.device_get(image_slice.flatten()  )  )
         UpperCAmelCase_      :							List[Any]       =      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 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[str]       =      FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-openpose'''       ,from_pt=_SCREAMING_SNAKE_CASE       ,dtype=jnp.bfloataa  )
         UpperCAmelCase_      :							Any       =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''       ,controlnet=_SCREAMING_SNAKE_CASE       ,from_pt=_SCREAMING_SNAKE_CASE       ,dtype=jnp.bfloataa  )
         UpperCAmelCase_      :							Tuple       =      controlnet_params

         UpperCAmelCase_      :							Optional[int]       =      '''Chef in the kitchen'''
         UpperCAmelCase_      :							str       =      jax.device_count()
         UpperCAmelCase_      :							Union[str, Any]       =      pipe.prepare_text_inputs([prompts] * num_samples  )

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

         UpperCAmelCase_      :							List[str]       =      jax.random.PRNGKey(0  )
         UpperCAmelCase_      :							str       =      jax.random.split(_SCREAMING_SNAKE_CASE       ,jax.device_count()  )

         UpperCAmelCase_      :							str       =      replicate(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      shard(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      shard(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      pipe(
             prompt_ids=_SCREAMING_SNAKE_CASE       ,image=_SCREAMING_SNAKE_CASE       ,params=_SCREAMING_SNAKE_CASE       ,prng_seed=_SCREAMING_SNAKE_CASE       ,num_inference_steps=50       ,jit=_SCREAMING_SNAKE_CASE       ,).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

         UpperCAmelCase_      :							Union[str, Any]       =      jnp.asarray(jax.device_get(image_slice.flatten()  )  )
         UpperCAmelCase_      :							int       =      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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =			{
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			['BloomTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			[
         'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BloomForCausalLM',
         'BloomModel',
         'BloomPreTrainedModel',
         'BloomForSequenceClassification',
         'BloomForTokenClassification',
         'BloomForQuestionAnswering',
     ]

if TYPE_CHECKING:
     from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bloom_fast import BloomTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bloom import (
              BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
              BloomForCausalLM,
              BloomForQuestionAnswering,
              BloomForSequenceClassification,
              BloomForTokenClassification,
              BloomModel,
              BloomPreTrainedModel,
          )

else:
     import sys

     __a          =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)