"""simple docstring"""


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 							a					(		unittest.TestCase ):


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _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([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]       )
      print(f'''output_slice: {output_slice}'''       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2


    def   lowerCAmelCase_	(       self						:	int       ):
      _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([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]       )
      print(f'''output_slice: {output_slice}'''       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2

"""simple docstring"""


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Dict  				=				CustomTokenizer
    pass
