





'''simple docstring'''

import math

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-audio'''


    def __init__(		self ,		A=32 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=0.1 ,		A=0.0 ,		A=0.1 ,		A=0.1 ,		A=0.02 ,		A=1e-5 ,		A="gelu" ,		A=(512, 512, 512, 512, 512, 512, 512) ,		A=(5, 2, 2, 2, 2, 2, 2) ,		A=(10, 3, 3, 3, 3, 2, 2) ,		A=False ,		A=16 ,		A=19 ,		A=5 ,		A=0.05 ,		A=10 ,		A=2 ,		A=0.0 ,		A=10 ,		A=0 ,		A="sum" ,		A=False ,		A=False ,		A=256 ,		A=(512, 512, 512, 512, 1500) ,		A=(5, 3, 3, 1, 1) ,		A=(1, 2, 3, 1, 1) ,		A=512 ,		A=0 ,		A=1 ,		A=2 ,		A=False ,		A=3 ,		A=2 ,		A=3 ,		A=None ,		**A ,		)		->   Dict:
        super().__init__(**A ,		pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A       )
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			feat_extract_activation
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			conv_bias
        _SCREAMING_SNAKE_CASE									=			num_conv_pos_embeddings
        _SCREAMING_SNAKE_CASE									=			num_conv_pos_embedding_groups
        _SCREAMING_SNAKE_CASE									=			conv_pos_kernel_size
        _SCREAMING_SNAKE_CASE									=			len(self.conv_dim       )
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_dropout
        _SCREAMING_SNAKE_CASE									=			attention_dropout
        _SCREAMING_SNAKE_CASE									=			activation_dropout
        _SCREAMING_SNAKE_CASE									=			feat_proj_dropout
        _SCREAMING_SNAKE_CASE									=			final_dropout
        _SCREAMING_SNAKE_CASE									=			layerdrop
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			mask_time_prob
        _SCREAMING_SNAKE_CASE									=			mask_time_length
        _SCREAMING_SNAKE_CASE									=			mask_time_min_masks
        _SCREAMING_SNAKE_CASE									=			mask_feature_prob
        _SCREAMING_SNAKE_CASE									=			mask_feature_length
        _SCREAMING_SNAKE_CASE									=			mask_feature_min_masks

        # ctc loss
        _SCREAMING_SNAKE_CASE									=			ctc_loss_reduction
        _SCREAMING_SNAKE_CASE									=			ctc_zero_infinity

        # adapter
        _SCREAMING_SNAKE_CASE									=			add_adapter
        _SCREAMING_SNAKE_CASE									=			adapter_kernel_size
        _SCREAMING_SNAKE_CASE									=			adapter_stride
        _SCREAMING_SNAKE_CASE									=			num_adapter_layers
        _SCREAMING_SNAKE_CASE									=			output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        _SCREAMING_SNAKE_CASE									=			classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			xvector_output_dim





    @property
    def 			snake_case_(		self       )		->   str:
        return math.prod(self.conv_stride       )







"""simple docstring"""




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 							__A						(	self				)     ->							List[Any]:
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)







		@property
		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					ort.SessionOptions()
								SCREAMING_SNAKE_CASE													=					False
								return options







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
								SCREAMING_SNAKE_CASE													=					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
								SCREAMING_SNAKE_CASE													=					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'						,       revision='onnx'						,       safety_checker=lowerCAmelCase__						,       feature_extractor=lowerCAmelCase__						,       provider=self.gpu_provider						,       sess_options=self.gpu_options						,       )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					'A red cat sitting on a park bench'

								SCREAMING_SNAKE_CASE													=					np.random.RandomState(0				)
								SCREAMING_SNAKE_CASE													=					pipe(
								    prompt=lowerCAmelCase__						,       image=lowerCAmelCase__						,       mask_image=lowerCAmelCase__						,       strength=0.75						,       guidance_scale=7.5						,       num_inference_steps=15						,       generator=lowerCAmelCase__						,       output_type='np'						,       )

								SCREAMING_SNAKE_CASE													=					output.images[0]

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


