


'''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     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 def 			snake_case__ (   self    ):

  """simple docstring"""







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


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		Optional[Any]	  =      FlaxControlNetModel.from_pretrained(
      'lllyasviel/sd-controlnet-canny'   ,   from_pt=UpperCamelCase__   ,   dtype=jnp.bfloataa    )
  __A  ,__A	:		Optional[int]	  =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
      'runwayml/stable-diffusion-v1-5'   ,   controlnet=UpperCamelCase__   ,   from_pt=UpperCamelCase__   ,   dtype=jnp.bfloataa    )
  __A	:		Dict	  =      controlnet_params

  __A	:		Any	  =      'bird'
  __A	:		Optional[int]	  =      jax.device_count()
  __A	:		Optional[Any]	  =      pipe.prepare_text_inputs([prompts] * num_samples    )

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

  __A	:		Dict	  =      jax.random.PRNGKey(0    )
  __A	:		List[Any]	  =      jax.random.split(UpperCamelCase__   ,   jax.device_count()    )

  __A	:		List[Any]	  =      replicate(UpperCamelCase__    )
  __A	:		Any	  =      shard(UpperCamelCase__    )
  __A	:		List[Any]	  =      shard(UpperCamelCase__    )

  __A	:		str	  =      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)

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

  __A	:		List[str]	  =      jnp.asarray(jax.device_get(image_slice.flatten()    )    )
  __A	:		Tuple	  =      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 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		Optional[Any]	  =      FlaxControlNetModel.from_pretrained(
      'lllyasviel/sd-controlnet-openpose'   ,   from_pt=UpperCamelCase__   ,   dtype=jnp.bfloataa    )
  __A  ,__A	:		Any	  =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
      'runwayml/stable-diffusion-v1-5'   ,   controlnet=UpperCamelCase__   ,   from_pt=UpperCamelCase__   ,   dtype=jnp.bfloataa    )
  __A	:		Optional[int]	  =      controlnet_params

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

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

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

  __A	:		str	  =      replicate(UpperCamelCase__    )
  __A	:		Any	  =      shard(UpperCamelCase__    )
  __A	:		List[Any]	  =      shard(UpperCamelCase__    )

  __A	:		Dict	  =      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)

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

  __A	:		Dict	  =      jnp.asarray(jax.device_get(image_slice.flatten()    )    )
  __A	:		Optional[Any]	  =      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'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase    :     Any         =       {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''MaskFormerFeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''MaskFormerImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Tuple         =       [
       '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''MaskFormerForInstanceSegmentation''',
       '''MaskFormerModel''',
       '''MaskFormerPreTrainedModel''',
   ]
   _UpperCAmelCase    :     int         =       [
       '''MaskFormerSwinBackbone''',
       '''MaskFormerSwinModel''',
       '''MaskFormerSwinPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
   from .configuration_maskformer_swin import MaskFormerSwinConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_maskformer import MaskFormerFeatureExtractor
      from .image_processing_maskformer import MaskFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskformer import (
          MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskFormerForInstanceSegmentation,
          MaskFormerModel,
          MaskFormerPreTrainedModel,
      )
      from .modeling_maskformer_swin import (
          MaskFormerSwinBackbone,
          MaskFormerSwinModel,
          MaskFormerSwinPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase    :     str         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)



