



import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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    A__		(			self     ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()



     def    A__		(			self     ):
       UpperCAmelCase_					,						UpperCAmelCase_						=				FlaxStableDiffusionPipeline.from_pretrained(
           "stabilityai/stable-diffusion-2"    ,	revision="bf16"    ,	dtype=jnp.bfloataa    ,	)

       UpperCAmelCase_						=				"A painting of a squirrel eating a burger"
       UpperCAmelCase_						=				jax.device_count()
       UpperCAmelCase_						=				num_samples * [prompt]
       UpperCAmelCase_						=				sd_pipe.prepare_inputs(lowerCAmelCase     )

       UpperCAmelCase_						=				replicate(lowerCAmelCase     )
       UpperCAmelCase_						=				shard(lowerCAmelCase     )

       UpperCAmelCase_						=				jax.random.PRNGKey(0     )
       UpperCAmelCase_						=				jax.random.split(lowerCAmelCase    ,	jax.device_count()     )

       UpperCAmelCase_						=				sd_pipe(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	num_inference_steps=25    ,	jit=lowerCAmelCase     )[0]
       assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

       UpperCAmelCase_						=				jnp.asarray(jax.device_get(image_slice.flatten()     )     )
       UpperCAmelCase_						=				jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]     )
       print(f'''output_slice: {output_slice}'''     )
       assert jnp.abs(output_slice - expected_slice     ).max() < 1e-2



     def    A__		(			self     ):
       UpperCAmelCase_						=				"stabilityai/stable-diffusion-2"
       UpperCAmelCase_					,						UpperCAmelCase_						=				FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase    ,	subfolder="scheduler"     )
       UpperCAmelCase_					,						UpperCAmelCase_						=				FlaxStableDiffusionPipeline.from_pretrained(
           lowerCAmelCase    ,	scheduler=lowerCAmelCase    ,	revision="bf16"    ,	dtype=jnp.bfloataa    ,	)
       UpperCAmelCase_						=				scheduler_params

       UpperCAmelCase_						=				"A painting of a squirrel eating a burger"
       UpperCAmelCase_						=				jax.device_count()
       UpperCAmelCase_						=				num_samples * [prompt]
       UpperCAmelCase_						=				sd_pipe.prepare_inputs(lowerCAmelCase     )

       UpperCAmelCase_						=				replicate(lowerCAmelCase     )
       UpperCAmelCase_						=				shard(lowerCAmelCase     )

       UpperCAmelCase_						=				jax.random.PRNGKey(0     )
       UpperCAmelCase_						=				jax.random.split(lowerCAmelCase    ,	jax.device_count()     )

       UpperCAmelCase_						=				sd_pipe(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	num_inference_steps=25    ,	jit=lowerCAmelCase     )[0]
       assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

       UpperCAmelCase_						=				jnp.asarray(jax.device_get(image_slice.flatten()     )     )
       UpperCAmelCase_						=				jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]     )
       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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizer"]

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FNetForMaskedLM",
			    "FNetForMultipleChoice",
			    "FNetForNextSentencePrediction",
			    "FNetForPreTraining",
			    "FNetForQuestionAnswering",
			    "FNetForSequenceClassification",
			    "FNetForTokenClassification",
			    "FNetLayer",
			    "FNetModel",
			    "FNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

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

