

from typing import TYPE_CHECKING

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


_UpperCAmelCase		:       Optional[int]						    =	{
    """configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       List[str]						    =	[
					    """ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ErnieForCausalLM""",
					    """ErnieForMaskedLM""",
					    """ErnieForMultipleChoice""",
					    """ErnieForNextSentencePrediction""",
					    """ErnieForPreTraining""",
					    """ErnieForQuestionAnswering""",
					    """ErnieForSequenceClassification""",
					    """ErnieForTokenClassification""",
					    """ErnieModel""",
					    """ErniePreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ernie import (
										    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ErnieForCausalLM,
										    ErnieForMaskedLM,
										    ErnieForMultipleChoice,
										    ErnieForNextSentencePrediction,
										    ErnieForPreTraining,
										    ErnieForQuestionAnswering,
										    ErnieForSequenceClassification,
										    ErnieForTokenClassification,
										    ErnieModel,
										    ErniePreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase		:       str						    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







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




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

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

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

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

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

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

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   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	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-openpose'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_          =   controlnet_params

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

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

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

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

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

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




