



"""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        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Tuple						)					->		List[Any]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



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

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

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

				lowercase__   :    str				=			jax.random.PRNGKey(0						)
				lowercase__   :    Dict				=			jax.random.split(_snake_case			,jax.device_count()						)

				lowercase__   :    Any				=			replicate(_snake_case						)
				lowercase__   :    List[str]				=			shard(_snake_case						)
				lowercase__   :    Dict				=			shard(_snake_case						)

				lowercase__   :    Optional[Any]				=			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)

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

				lowercase__   :    str				=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
				lowercase__   :    Any				=			jnp.array(
				    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]						)
				print(f"""output_slice: {output_slice}"""						)
				assert jnp.abs(output_slice - expected_slice						).max() < 1e-2







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		str:






				"""simple docstring"""



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

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

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

				lowercase__   :    Dict				=			jax.random.PRNGKey(0						)
				lowercase__   :    Optional[int]				=			jax.random.split(_snake_case			,jax.device_count()						)

				lowercase__   :    Any				=			replicate(_snake_case						)
				lowercase__   :    Optional[int]				=			shard(_snake_case						)
				lowercase__   :    List[str]				=			shard(_snake_case						)

				lowercase__   :    str				=			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)

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

				lowercase__   :    Union[str, Any]				=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
				lowercase__   :    Tuple				=			jnp.array(
				    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]						)
				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_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase_   						=   {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SpeechT5ForSpeechToText',
							    'SpeechT5ForSpeechToSpeech',
							    'SpeechT5ForTextToSpeech',
							    'SpeechT5Model',
							    'SpeechT5PreTrainedModel',
							    'SpeechT5HifiGan',
							]

if TYPE_CHECKING:
							from .configuration_speechta import (
							    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
							    SpeechTaConfig,
							    SpeechTaHifiGanConfig,
							)
							from .feature_extraction_speechta import SpeechTaFeatureExtractor
							from .processing_speechta import SpeechTaProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_speechta import SpeechTaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_speechta import (
														    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SpeechTaForSpeechToSpeech,
														    SpeechTaForSpeechToText,
														    SpeechTaForTextToSpeech,
														    SpeechTaHifiGan,
														    SpeechTaModel,
														    SpeechTaPreTrainedModel,
														)

else:
							import sys

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