


'''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   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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

							UpperCamelCase__  :Any				   =					'''bird'''
							UpperCamelCase__  :str				   =					jax.device_count()
							UpperCamelCase__  :int				   =					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/bird_canny.png'''				)
							UpperCamelCase__  :Union[str, Any]				   =					pipe.prepare_image_inputs([canny_image] * num_samples				)

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

							UpperCamelCase__  :Union[str, Any]				   =					replicate(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					shard(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					shard(UpperCamelCase_				)

							UpperCamelCase__  :List[Any]				   =					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)

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

							UpperCamelCase__  :Union[str, Any]				   =					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
							UpperCamelCase__  :int				   =					jnp.array(
							    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]				)
							print(F'''output_slice: {output_slice}'''				)
							assert jnp.abs(output_slice - expected_slice				).max() < 1e-2




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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

							UpperCamelCase__  :List[str]				   =					'''Chef in the kitchen'''
							UpperCamelCase__  :Any				   =					jax.device_count()
							UpperCamelCase__  :str				   =					pipe.prepare_text_inputs([prompts] * num_samples				)

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

							UpperCamelCase__  :Any				   =					jax.random.PRNGKey(0				)
							UpperCamelCase__  :Any				   =					jax.random.split(UpperCamelCase_       ,  jax.device_count()				)

							UpperCamelCase__  :Optional[Any]				   =					replicate(UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					shard(UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					shard(UpperCamelCase_				)

							UpperCamelCase__  :Union[str, Any]				   =					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)

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

							UpperCamelCase__  :Any				   =					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
							UpperCamelCase__  :List[Any]				   =					jnp.array(
							    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]				)
							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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__snake_case				 =	{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	['''CLIPTokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	['''CLIPFeatureExtractor''']
		__snake_case				 =	['''CLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPModel''',
		    '''CLIPPreTrainedModel''',
		    '''CLIPTextModel''',
		    '''CLIPTextModelWithProjection''',
		    '''CLIPVisionModel''',
		    '''CLIPVisionModelWithProjection''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCLIPModel''',
		    '''TFCLIPPreTrainedModel''',
		    '''TFCLIPTextModel''',
		    '''TFCLIPVisionModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''FlaxCLIPModel''',
		    '''FlaxCLIPPreTrainedModel''',
		    '''FlaxCLIPTextModel''',
		    '''FlaxCLIPTextPreTrainedModel''',
		    '''FlaxCLIPVisionModel''',
		    '''FlaxCLIPVisionPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

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