





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     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[Any]					)							->						Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()






  def     lowercase		(    self			:		Any					)							->						List[Any]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'stabilityai/stable-diffusion-2' ,  revision='bf16' ,  dtype=jnp.bfloataa ,  )

     __lowerCAmelCase							     =     'A painting of a squirrel eating a burger'
     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     sd_pipe.prepare_inputs(lowerCAmelCase_					)

     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     sd_pipe(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  num_inference_steps=2_5 ,  jit=lowerCAmelCase_					)[0]
     assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

     __lowerCAmelCase							     =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
     __lowerCAmelCase							     =     images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

     __lowerCAmelCase							     =     jnp.asarray(jax.device_get(image_slice.flatten()					)					)
     __lowerCAmelCase							     =     jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12]					)
     print(f"""output_slice: {output_slice}"""					)
     assert jnp.abs(output_slice - expected_slice					).max() < 1e-2






  def     lowercase		(    self			:		List[str]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     'stabilityai/stable-diffusion-2'
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_ ,  subfolder='scheduler'					)
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         lowerCAmelCase_ ,  scheduler=lowerCAmelCase_ ,  revision='bf16' ,  dtype=jnp.bfloataa ,  )
     __lowerCAmelCase							     =     scheduler_params

     __lowerCAmelCase							     =     'A painting of a squirrel eating a burger'
     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     sd_pipe.prepare_inputs(lowerCAmelCase_					)

     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     sd_pipe(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  num_inference_steps=2_5 ,  jit=lowerCAmelCase_					)[0]
     assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

     __lowerCAmelCase							     =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
     __lowerCAmelCase							     =     images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

     __lowerCAmelCase							     =     jnp.asarray(jax.device_get(image_slice.flatten()					)					)
     __lowerCAmelCase							     =     jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97]					)
     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,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[str]						   =  [
         '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

     _snake_case  :   str						   =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

