
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       ):




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




				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_	,  lowerCAmelCase_						=	FlaxControlNetModel.from_pretrained(
							    "lllyasviel/sd-controlnet-canny"				,				from_pt=_UpperCamelCase				,				dtype=jnp.bfloataa     )
							lowerCAmelCase_	,  lowerCAmelCase_						=	FlaxStableDiffusionControlNetPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"				,				controlnet=_UpperCamelCase				,				from_pt=_UpperCamelCase				,				dtype=jnp.bfloataa     )
							lowerCAmelCase_						=	controlnet_params

							lowerCAmelCase_						=	"bird"
							lowerCAmelCase_						=	jax.device_count()
							lowerCAmelCase_						=	pipe.prepare_text_inputs([prompts] * num_samples     )

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

							lowerCAmelCase_						=	jax.random.PRNGKey(0     )
							lowerCAmelCase_						=	jax.random.split(_UpperCamelCase				,				jax.device_count()     )

							lowerCAmelCase_						=	replicate(_UpperCamelCase     )
							lowerCAmelCase_						=	shard(_UpperCamelCase     )
							lowerCAmelCase_						=	shard(_UpperCamelCase     )

							lowerCAmelCase_						=	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)

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

							lowerCAmelCase_						=	jnp.asarray(jax.device_get(image_slice.flatten()     )     )
							lowerCAmelCase_						=	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      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_	,  lowerCAmelCase_						=	FlaxControlNetModel.from_pretrained(
							    "lllyasviel/sd-controlnet-openpose"				,				from_pt=_UpperCamelCase				,				dtype=jnp.bfloataa     )
							lowerCAmelCase_	,  lowerCAmelCase_						=	FlaxStableDiffusionControlNetPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"				,				controlnet=_UpperCamelCase				,				from_pt=_UpperCamelCase				,				dtype=jnp.bfloataa     )
							lowerCAmelCase_						=	controlnet_params

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

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

							lowerCAmelCase_						=	jax.random.PRNGKey(0     )
							lowerCAmelCase_						=	jax.random.split(_UpperCamelCase				,				jax.device_count()     )

							lowerCAmelCase_						=	replicate(_UpperCamelCase     )
							lowerCAmelCase_						=	shard(_UpperCamelCase     )
							lowerCAmelCase_						=	shard(_UpperCamelCase     )

							lowerCAmelCase_						=	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)

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

							lowerCAmelCase_						=	jnp.asarray(jax.device_get(image_slice.flatten()     )     )
							lowerCAmelCase_						=	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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A   =     {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["DistilBertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "DistilBertForMaskedLM",
			    "DistilBertForMultipleChoice",
			    "DistilBertForQuestionAnswering",
			    "DistilBertForSequenceClassification",
			    "DistilBertForTokenClassification",
			    "DistilBertModel",
			    "DistilBertPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFDistilBertForMaskedLM",
			    "TFDistilBertForMultipleChoice",
			    "TFDistilBertForQuestionAnswering",
			    "TFDistilBertForSequenceClassification",
			    "TFDistilBertForTokenClassification",
			    "TFDistilBertMainLayer",
			    "TFDistilBertModel",
			    "TFDistilBertPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxDistilBertForMaskedLM",
			    "FlaxDistilBertForMultipleChoice",
			    "FlaxDistilBertForQuestionAnswering",
			    "FlaxDistilBertForSequenceClassification",
			    "FlaxDistilBertForTokenClassification",
			    "FlaxDistilBertModel",
			    "FlaxDistilBertPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_distilbert import (
			    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DistilBertConfig,
			    DistilBertOnnxConfig,
			)
			from .tokenization_distilbert import DistilBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_distilbert_fast import DistilBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_distilbert import (
						    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DistilBertForMaskedLM,
						    DistilBertForMultipleChoice,
						    DistilBertForQuestionAnswering,
						    DistilBertForSequenceClassification,
						    DistilBertForTokenClassification,
						    DistilBertModel,
						    DistilBertPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_distilbert import (
						    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDistilBertForMaskedLM,
						    TFDistilBertForMultipleChoice,
						    TFDistilBertForQuestionAnswering,
						    TFDistilBertForSequenceClassification,
						    TFDistilBertForTokenClassification,
						    TFDistilBertMainLayer,
						    TFDistilBertModel,
						    TFDistilBertPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						    FlaxDistilBertPreTrainedModel,
						)

else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



