






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					List[str] 					=				'data2vec-text'







						def __init__(    self   ,				lowerCAmelCase_=3_0_5_2_2   ,				lowerCAmelCase_=7_6_8   ,				lowerCAmelCase_=1_2   ,				lowerCAmelCase_=1_2   ,				lowerCAmelCase_=3_0_7_2   ,				lowerCAmelCase_="gelu"   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=5_1_2   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=0.02   ,				lowerCAmelCase_=1e-12   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=0   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_="absolute"   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=None   ,				**lowerCAmelCase_   ,				)		->							List[str]:
										super().__init__(pad_token_id=lowerCAmelCase_   ,				bos_token_id=lowerCAmelCase_   ,				eos_token_id=lowerCAmelCase_   ,				**lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    vocab_size
										_SCREAMING_SNAKE_CASE   :		Dict       		=    hidden_size
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_hidden_layers
										_SCREAMING_SNAKE_CASE   :		int       		=    num_attention_heads
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    hidden_act
										_SCREAMING_SNAKE_CASE   :		Dict       		=    intermediate_size
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Any       		=    max_position_embeddings
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    type_vocab_size
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    initializer_range
										_SCREAMING_SNAKE_CASE   :		int       		=    layer_norm_eps
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    position_embedding_type
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    use_cache
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    classifier_dropout







class _lowerCAmelCase						(    __UpperCAmelCase       ):







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









"""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 _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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)

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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2'
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_params

										_SCREAMING_SNAKE_CASE   :		int       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		int       		=    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)

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

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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

