


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     ):




			def 	lowerCamelCase_       (    self				:		List[Any]    ):







						"""simple docstring"""







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




			def 	lowerCamelCase_       (    self				:		Optional[int]    ):







						"""simple docstring"""







						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(__SCREAMING_SNAKE_CASE    )

						UpperCamelCase								=       replicate(__SCREAMING_SNAKE_CASE    )
						UpperCamelCase								=       shard(__SCREAMING_SNAKE_CASE    )

						UpperCamelCase								=       jax.random.PRNGKey(0    )
						UpperCamelCase								=       jax.random.split(__SCREAMING_SNAKE_CASE							,							jax.device_count()    )

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

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







						"""simple docstring"""







						UpperCamelCase								=       """stabilityai/stable-diffusion-2"""
						UpperCamelCase	,	UpperCamelCase								=       FlaxDPMSolverMultistepScheduler.from_pretrained(__SCREAMING_SNAKE_CASE							,							subfolder="""scheduler"""    )
						UpperCamelCase	,	UpperCamelCase								=       FlaxStableDiffusionPipeline.from_pretrained(
						    __SCREAMING_SNAKE_CASE							,							scheduler=__SCREAMING_SNAKE_CASE							,							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(__SCREAMING_SNAKE_CASE    )

						UpperCamelCase								=       replicate(__SCREAMING_SNAKE_CASE    )
						UpperCamelCase								=       shard(__SCREAMING_SNAKE_CASE    )

						UpperCamelCase								=       jax.random.PRNGKey(0    )
						UpperCamelCase								=       jax.random.split(__SCREAMING_SNAKE_CASE							,							jax.device_count()    )

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

						UpperCamelCase								=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
						UpperCamelCase								=       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






