
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    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Optional[int]     		=   FlaxStableDiffusionPipeline.from_pretrained(
       """stabilityai/stable-diffusion-2"""       ,	revision="""bf16"""       ,	dtype=jnp.bfloataa       ,	)

   lowercase__	: Optional[int]     		=   """A painting of a squirrel eating a burger"""
   lowercase__	: Optional[Any]     		=   jax.device_count()
   lowercase__	: Optional[int]     		=   num_samples * [prompt]
   lowercase__	: Tuple     		=   sd_pipe.prepare_inputs(SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   replicate(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   shard(SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   jax.random.PRNGKey(0)
   lowercase__	: Tuple     		=   jax.random.split(SCREAMING_SNAKE_CASE_       ,	jax.device_count())

   lowercase__	: Optional[int]     		=   sd_pipe(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	num_inference_steps=25       ,	jit=SCREAMING_SNAKE_CASE_)[0]
   assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

   lowercase__	: List[str]     		=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
   lowercase__	: Optional[Any]     		=   images[0, 2_53:2_56, 2_53:2_56, -1]

   lowercase__	: Dict     		=   jnp.asarray(jax.device_get(image_slice.flatten()))
   lowercase__	: Dict     		=   jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2])
   print(f'output_slice: {output_slice}')
   assert jnp.abs(output_slice - expected_slice).max() < 1E-2



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   """stabilityai/stable-diffusion-2"""
   lowercase__		,     lowercase__	: List[str]     		=   FlaxDPMSolverMultistepScheduler.from_pretrained(SCREAMING_SNAKE_CASE_       ,	subfolder="""scheduler""")
   lowercase__		,     lowercase__	: int     		=   FlaxStableDiffusionPipeline.from_pretrained(
       SCREAMING_SNAKE_CASE_       ,	scheduler=SCREAMING_SNAKE_CASE_       ,	revision="""bf16"""       ,	dtype=jnp.bfloataa       ,	)
   lowercase__	: str     		=   scheduler_params

   lowercase__	: List[Any]     		=   """A painting of a squirrel eating a burger"""
   lowercase__	: List[Any]     		=   jax.device_count()
   lowercase__	: str     		=   num_samples * [prompt]
   lowercase__	: Optional[Any]     		=   sd_pipe.prepare_inputs(SCREAMING_SNAKE_CASE_)

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

   lowercase__	: str     		=   jax.random.PRNGKey(0)
   lowercase__	: Any     		=   jax.random.split(SCREAMING_SNAKE_CASE_       ,	jax.device_count())

   lowercase__	: Tuple     		=   sd_pipe(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	num_inference_steps=25       ,	jit=SCREAMING_SNAKE_CASE_)[0]
   assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

   lowercase__	: Union[str, Any]     		=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
   lowercase__	: List[Any]     		=   images[0, 2_53:2_56, 2_53:2_56, -1]

   lowercase__	: List[Any]     		=   jnp.asarray(jax.device_get(image_slice.flatten()))
   lowercase__	: Union[str, Any]     		=   jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7])
   print(f'output_slice: {output_slice}')
   assert jnp.abs(output_slice - expected_slice).max() < 1E-2














from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	Optional[int]								=logging.get_logger(__name__)

_UpperCamelCase			:	Tuple								={
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Any     =   "canine"







		def __init__(					self       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=16384       ,A__=16       ,A__=0.02       ,A__=1E-12       ,A__=0       ,A__=0xE_000       ,A__=0xE_001       ,A__=4       ,A__=4       ,A__=8       ,A__=16384       ,A__=128       ,**A__       ,):
						super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__		)

						_A	:				int   =      max_position_embeddings
						_A	:				Dict   =      hidden_size
						_A	:				List[str]   =      num_hidden_layers
						_A	:				Optional[Any]   =      num_attention_heads
						_A	:				Optional[Any]   =      intermediate_size
						_A	:				Optional[Any]   =      hidden_act
						_A	:				str   =      hidden_dropout_prob
						_A	:				Optional[int]   =      attention_probs_dropout_prob
						_A	:				Union[str, Any]   =      initializer_range
						_A	:				Dict   =      type_vocab_size
						_A	:				Optional[Any]   =      layer_norm_eps

						# Character config:
						_A	:				Union[str, Any]   =      downsampling_rate
						_A	:				List[str]   =      upsampling_kernel_size
						_A	:				str   =      num_hash_functions
						_A	:				Optional[Any]   =      num_hash_buckets
						_A	:				Optional[Any]   =      local_transformer_stride



