





'''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       UpperCAmelCase (						unittest.TestCase  ):




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




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

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

						lowercase         = replicate(A__				)
						lowercase         = shard(A__				)

						lowercase         = jax.random.PRNGKey(0				)
						lowercase         = jax.random.split(A__							,			jax.device_count()				)

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

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

						lowercase         = jnp.asarray(jax.device_get(image_slice.flatten()				)				)
						lowercase         = 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 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Union[str, Any]:
						lowercase         = "stabilityai/stable-diffusion-2"
						lowercase   ,							lowercase         = FlaxDPMSolverMultistepScheduler.from_pretrained(A__							,			subfolder="scheduler"				)
						lowercase   ,							lowercase         = FlaxStableDiffusionPipeline.from_pretrained(
						    A__							,			scheduler=A__							,			revision="bf16"							,			dtype=jnp.bfloataa							,			)
						lowercase         = scheduler_params

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

						lowercase         = replicate(A__				)
						lowercase         = shard(A__				)

						lowercase         = jax.random.PRNGKey(0				)
						lowercase         = jax.random.split(A__							,			jax.device_count()				)

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

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

						lowercase         = jnp.asarray(jax.device_get(image_slice.flatten()				)				)
						lowercase         = 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











'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)

__lowerCamelCase : str											=			{
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	List[str] 			=		'''data2vec-text'''




		def __init__(self      :	List[str]							,			A__      :	str=3_0_5_2_2							,			A__      :	Tuple=7_6_8							,			A__      :	Any=1_2							,			A__      :	Optional[int]=1_2							,			A__      :	str=3_0_7_2							,			A__      :	List[str]="gelu"							,			A__      :	List[Any]=0.1							,			A__      :	Optional[int]=0.1							,			A__      :	Union[str, Any]=5_1_2							,			A__      :	Any=2							,			A__      :	str=0.0_2							,			A__      :	int=1e-12							,			A__      :	Union[str, Any]=1							,			A__      :	Optional[int]=0							,			A__      :	Union[str, Any]=2							,			A__      :	Optional[int]="absolute"							,			A__      :	Tuple=True							,			A__      :	int=None							,			**A__      :	Any							,			)					-> Any:
						super().__init__(pad_token_id=A__							,			bos_token_id=A__							,			eos_token_id=A__							,			**A__				)

						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = hidden_act
						lowercase         = intermediate_size
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = position_embedding_type
						lowercase         = use_cache
						lowercase         = classifier_dropout




class       UpperCAmelCase (						_lowercase  ):




		@property
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
										lowercase         = {0: "batch", 1: "choice", 2: "sequence"}
						else:
										lowercase         = {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]				)




