
import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					:			Any			      =		logging.get_logger(__name__)

__lowerCAmelCase					:			Dict			      =		{
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			int        =    '''data2vec-audio'''




      def __init__(      self		:       str				,       _snake_case		:       List[str]=32				,       _snake_case		:       Optional[Any]=768				,       _snake_case		:       Any=12				,       _snake_case		:       Any=12				,       _snake_case		:       Any=3072				,       _snake_case		:       int="gelu"				,       _snake_case		:       Union[str, Any]=0.1				,       _snake_case		:       int=0.1				,       _snake_case		:       Dict=0.1				,       _snake_case		:       int=0.0				,       _snake_case		:       Dict=0.1				,       _snake_case		:       Optional[int]=0.1				,       _snake_case		:       List[Any]=0.02				,       _snake_case		:       List[Any]=1E-5				,       _snake_case		:       List[str]="gelu"				,       _snake_case		:       Union[str, Any]=(512, 512, 512, 512, 512, 512, 512)				,       _snake_case		:       Optional[Any]=(5, 2, 2, 2, 2, 2, 2)				,       _snake_case		:       str=(10, 3, 3, 3, 3, 2, 2)				,       _snake_case		:       Tuple=False				,       _snake_case		:       Any=16				,       _snake_case		:       List[str]=19				,       _snake_case		:       Optional[int]=5				,       _snake_case		:       Optional[int]=0.05				,       _snake_case		:       Optional[Any]=10				,       _snake_case		:       int=2				,       _snake_case		:       str=0.0				,       _snake_case		:       Optional[Any]=10				,       _snake_case		:       str=0				,       _snake_case		:       Any="sum"				,       _snake_case		:       List[str]=False				,       _snake_case		:       Any=False				,       _snake_case		:       Tuple=256				,       _snake_case		:       Optional[Any]=(512, 512, 512, 512, 1500)				,       _snake_case		:       Tuple=(5, 3, 3, 1, 1)				,       _snake_case		:       Dict=(1, 2, 3, 1, 1)				,       _snake_case		:       Tuple=512				,       _snake_case		:       Dict=0				,       _snake_case		:       List[str]=1				,       _snake_case		:       Dict=2				,       _snake_case		:       Optional[int]=False				,       _snake_case		:       List[Any]=3				,       _snake_case		:       Union[str, Any]=2				,       _snake_case		:       Optional[Any]=3				,       _snake_case		:       Tuple=None				,       **_snake_case		:       List[Any]				,       ):
          super().__init__(**_snake_case				,       pad_token_id=_snake_case				,       bos_token_id=_snake_case				,       eos_token_id=_snake_case				)
          __lowercase		:			List[Any]	    =       hidden_size
          __lowercase		:			Tuple	    =       feat_extract_activation
          __lowercase		:			int	    =       list(_snake_case				)
          __lowercase		:			Any	    =       list(_snake_case				)
          __lowercase		:			Tuple	    =       list(_snake_case				)
          __lowercase		:			Optional[Any]	    =       conv_bias
          __lowercase		:			List[Any]	    =       num_conv_pos_embeddings
          __lowercase		:			int	    =       num_conv_pos_embedding_groups
          __lowercase		:			Optional[Any]	    =       conv_pos_kernel_size
          __lowercase		:			int	    =       len(self.conv_dim				)
          __lowercase		:			str	    =       num_hidden_layers
          __lowercase		:			Dict	    =       intermediate_size
          __lowercase		:			List[Any]	    =       hidden_act
          __lowercase		:			List[Any]	    =       num_attention_heads
          __lowercase		:			Optional[Any]	    =       hidden_dropout
          __lowercase		:			Optional[int]	    =       attention_dropout
          __lowercase		:			Any	    =       activation_dropout
          __lowercase		:			List[Any]	    =       feat_proj_dropout
          __lowercase		:			Dict	    =       final_dropout
          __lowercase		:			int	    =       layerdrop
          __lowercase		:			Tuple	    =       layer_norm_eps
          __lowercase		:			List[Any]	    =       initializer_range
          __lowercase		:			List[str]	    =       vocab_size
          __lowercase		:			Tuple	    =       use_weighted_layer_sum

          if (
              (len(self.conv_stride				) != self.num_feat_extract_layers)
              or (len(self.conv_kernel				) != self.num_feat_extract_layers)
              or (len(self.conv_dim				) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                  ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                  F' {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,'
                  F' `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'				)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          __lowercase		:			List[Any]	    =       mask_time_prob
          __lowercase		:			List[str]	    =       mask_time_length
          __lowercase		:			Optional[Any]	    =       mask_time_min_masks
          __lowercase		:			Tuple	    =       mask_feature_prob
          __lowercase		:			Dict	    =       mask_feature_length
          __lowercase		:			Dict	    =       mask_feature_min_masks

          # ctc loss
          __lowercase		:			Dict	    =       ctc_loss_reduction
          __lowercase		:			List[Any]	    =       ctc_zero_infinity

          # adapter
          __lowercase		:			Optional[Any]	    =       add_adapter
          __lowercase		:			Optional[Any]	    =       adapter_kernel_size
          __lowercase		:			Dict	    =       adapter_stride
          __lowercase		:			Tuple	    =       num_adapter_layers
          __lowercase		:			Dict	    =       output_hidden_size or hidden_size

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          __lowercase		:			str	    =       classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          __lowercase		:			Union[str, Any]	    =       list(_snake_case				)
          __lowercase		:			Optional[int]	    =       list(_snake_case				)
          __lowercase		:			Union[str, Any]	    =       list(_snake_case				)
          __lowercase		:			List[Any]	    =       xvector_output_dim




      @property
      def 		snake_case_			(      self		:       Dict				):
          return math.prod(self.conv_stride				)




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


@nightly
@require_onnxruntime
@require_torch_gpu
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @property
      def 		snake_case_			(      self		:       Union[str, Any]				):
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Tuple	    =       ort.SessionOptions()
          __lowercase		:			List[Any]	    =       False
          return options




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
          __lowercase		:			Optional[Any]	    =       load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'''				)

          # using the PNDM scheduler by default
          __lowercase		:			Union[str, Any]	    =       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''				,       revision='''onnx'''				,       safety_checker=_snake_case				,       feature_extractor=_snake_case				,       provider=self.gpu_provider				,       sess_options=self.gpu_options				,       )
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Tuple	    =       '''A red cat sitting on a park bench'''

          __lowercase		:			int	    =       np.random.RandomState(0				)
          __lowercase		:			int	    =       pipe(
              prompt=_snake_case				,       image=_snake_case				,       mask_image=_snake_case				,       strength=0.75				,       guidance_scale=7.5				,       num_inference_steps=15				,       generator=_snake_case				,       output_type='''np'''				,       )

          __lowercase		:			Dict	    =       output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image				).max() < 1E-2


