






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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 ):





							"""simple docstring"""







							def       _a   ( self							):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()





							def       _a   ( self							):
									UpperCamelCase_: Any   				=    FlaxControlNetModel.from_pretrained(
									    'lllyasviel/sd-controlnet-canny'  ,      from_pt=_lowerCamelCase  ,      dtype=jnp.bfloataa							)
									UpperCamelCase_: Optional[Any]   				=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'  ,      controlnet=_lowerCamelCase  ,      from_pt=_lowerCamelCase  ,      dtype=jnp.bfloataa							)
									UpperCamelCase_: str   				=    controlnet_params

									UpperCamelCase_: int   				=    'bird'
									UpperCamelCase_: Union[str, Any]   				=    jax.device_count()
									UpperCamelCase_: Union[str, Any]   				=    pipe.prepare_text_inputs([prompts] * num_samples							)

									UpperCamelCase_: Optional[int]   				=    load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
									UpperCamelCase_: Optional[Any]   				=    pipe.prepare_image_inputs([canny_image] * num_samples							)

									UpperCamelCase_: List[Any]   				=    jax.random.PRNGKey(0							)
									UpperCamelCase_: Optional[Any]   				=    jax.random.split(_lowerCamelCase  ,      jax.device_count()							)

									UpperCamelCase_: Any   				=    replicate(_lowerCamelCase							)
									UpperCamelCase_: Tuple   				=    shard(_lowerCamelCase							)
									UpperCamelCase_: int   				=    shard(_lowerCamelCase							)

									UpperCamelCase_: str   				=    pipe(
									    prompt_ids=_lowerCamelCase  ,      image=_lowerCamelCase  ,      params=_lowerCamelCase  ,      prng_seed=_lowerCamelCase  ,      num_inference_steps=5_0  ,      jit=_lowerCamelCase  ,      ).images
									assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

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

									UpperCamelCase_: Any   				=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
									UpperCamelCase_: List[str]   				=    jnp.array(
									    [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]							)
									print(f'''output_slice: {output_slice}'''							)
									assert jnp.abs(output_slice - expected_slice							).max() < 1e-2





							def       _a   ( self							):
									UpperCamelCase_: List[str]   				=    FlaxControlNetModel.from_pretrained(
									    'lllyasviel/sd-controlnet-openpose'  ,      from_pt=_lowerCamelCase  ,      dtype=jnp.bfloataa							)
									UpperCamelCase_: List[str]   				=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'  ,      controlnet=_lowerCamelCase  ,      from_pt=_lowerCamelCase  ,      dtype=jnp.bfloataa							)
									UpperCamelCase_: int   				=    controlnet_params

									UpperCamelCase_: Union[str, Any]   				=    'Chef in the kitchen'
									UpperCamelCase_: List[Any]   				=    jax.device_count()
									UpperCamelCase_: Tuple   				=    pipe.prepare_text_inputs([prompts] * num_samples							)

									UpperCamelCase_: List[str]   				=    load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'							)
									UpperCamelCase_: Tuple   				=    pipe.prepare_image_inputs([pose_image] * num_samples							)

									UpperCamelCase_: Any   				=    jax.random.PRNGKey(0							)
									UpperCamelCase_: str   				=    jax.random.split(_lowerCamelCase  ,      jax.device_count()							)

									UpperCamelCase_: Optional[int]   				=    replicate(_lowerCamelCase							)
									UpperCamelCase_: int   				=    shard(_lowerCamelCase							)
									UpperCamelCase_: Dict   				=    shard(_lowerCamelCase							)

									UpperCamelCase_: int   				=    pipe(
									    prompt_ids=_lowerCamelCase  ,      image=_lowerCamelCase  ,      params=_lowerCamelCase  ,      prng_seed=_lowerCamelCase  ,      num_inference_steps=5_0  ,      jit=_lowerCamelCase  ,      ).images
									assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

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

									UpperCamelCase_: Dict   				=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
									UpperCamelCase_: int   				=    jnp.array(
									    [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]							)
									print(f'''output_slice: {output_slice}'''							)
									assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







from collections import OrderedDict
from typing import Mapping

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


A_		:		List[Any]		     =			logging.get_logger(__name__)

A_		:		Optional[Any]		     =			{
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									='''convbert'''





       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=0.0_2  ,      _lowerCamelCase=1e-12  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=2  ,      _lowerCamelCase=9  ,      _lowerCamelCase=1  ,      _lowerCamelCase=None  ,      **_lowerCamelCase  ,      ):
         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )

         UpperCamelCase_: Dict   				=    vocab_size
         UpperCamelCase_: Tuple   				=    hidden_size
         UpperCamelCase_: Optional[int]   				=    num_hidden_layers
         UpperCamelCase_: Optional[int]   				=    num_attention_heads
         UpperCamelCase_: Optional[Any]   				=    intermediate_size
         UpperCamelCase_: Tuple   				=    hidden_act
         UpperCamelCase_: Any   				=    hidden_dropout_prob
         UpperCamelCase_: Any   				=    attention_probs_dropout_prob
         UpperCamelCase_: List[Any]   				=    max_position_embeddings
         UpperCamelCase_: List[Any]   				=    type_vocab_size
         UpperCamelCase_: Optional[int]   				=    initializer_range
         UpperCamelCase_: Tuple   				=    layer_norm_eps
         UpperCamelCase_: List[str]   				=    embedding_size
         UpperCamelCase_: int   				=    head_ratio
         UpperCamelCase_: Dict   				=    conv_kernel_size
         UpperCamelCase_: List[Any]   				=    num_groups
         UpperCamelCase_: Dict   				=    classifier_dropout







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @property
       def       _a   ( self							):
         if self.task == "multiple-choice":
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
                 ('token_type_ids', dynamic_axis),
             ]							)