






'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


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

__UpperCamelCase      :      Union[str, Any]     						=						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:List[str]   =       'camembert'







	def __init__(  self      :							List[Any]       ,				_lowerCAmelCase      :							Optional[Any]=3_0522       ,				_lowerCAmelCase      :							str=768       ,				_lowerCAmelCase      :							List[str]=12       ,				_lowerCAmelCase      :							List[Any]=12       ,				_lowerCAmelCase      :							int=3072       ,				_lowerCAmelCase      :							Any="gelu"       ,				_lowerCAmelCase      :							Dict=0.1       ,				_lowerCAmelCase      :							List[Any]=0.1       ,				_lowerCAmelCase      :							List[str]=512       ,				_lowerCAmelCase      :							int=2       ,				_lowerCAmelCase      :							int=0.02       ,				_lowerCAmelCase      :							Dict=1e-12       ,				_lowerCAmelCase      :							Optional[Any]=1       ,				_lowerCAmelCase      :							Any=0       ,				_lowerCAmelCase      :							Dict=2       ,				_lowerCAmelCase      :							Any="absolute"       ,				_lowerCAmelCase      :							List[Any]=True       ,				_lowerCAmelCase      :							Union[str, Any]=None       ,				**_lowerCAmelCase      :							List[str]       ,				)		-> List[Any]:
		"""simple docstring"""







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

		__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    (     _lowerCAmelCase   ):







	@property
	def  _a   (  self      :							List[Any]			)		-> Mapping[str, Mapping[int, str]]:
		"""simple docstring"""







		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),
		    ]			)









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  _a   (  self      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







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







	def  _a   (  self      :							List[str]			)		-> int:
		"""simple docstring"""







		__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(_lowerCAmelCase			)

		__lowercase             =  replicate(_lowerCAmelCase			)
		__lowercase             =  shard(_lowerCAmelCase			)

		__lowercase             =  jax.random.PRNGKey(0			)
		__lowercase             =  jax.random.split(_lowerCAmelCase       ,				jax.device_count()			)

		__lowercase             =  sd_pipe(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				num_inference_steps=25       ,				jit=_lowerCAmelCase			)[0]
		assert images.shape == (jax.device_count(), 1, 768, 768, 3)

		__lowercase             =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
		__lowercase             =  images[0, 253:256, 253:256, -1]

		__lowercase             =  jnp.asarray(jax.device_get(image_slice.flatten()			)			)
		__lowercase             =  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      :							str			)		-> List[Any]:
		"""simple docstring"""







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

		__lowercase             =  replicate(_lowerCAmelCase			)
		__lowercase             =  shard(_lowerCAmelCase			)

		__lowercase             =  jax.random.PRNGKey(0			)
		__lowercase             =  jax.random.split(_lowerCAmelCase       ,				jax.device_count()			)

		__lowercase             =  sd_pipe(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				num_inference_steps=25       ,				jit=_lowerCAmelCase			)[0]
		assert images.shape == (jax.device_count(), 1, 768, 768, 3)

		__lowercase             =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
		__lowercase             =  images[0, 253:256, 253:256, -1]

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

