



"""simple docstring"""
import functools
import operator

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


_lowerCAmelCase    :Dict     =					logging.get_logger(__name__)

_lowerCAmelCase    :Optional[Any]     =					{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class 							_UpperCAmelCase							( _SCREAMING_SNAKE_CASE      ):





     '''simple docstring'''



     a__									='''sew-d'''





     def __init__(					self       ,	A=3_2       ,	A=7_6_8       ,	A=1_2       ,	A=1_2       ,	A=3_0_7_2       ,	A=2       ,	A=5_1_2       ,	A=2_5_6       ,	A=True       ,	A=True       ,	A=("p2c", "c2p")       ,	A="layer_norm"       ,	A="gelu_python"       ,	A=0.1       ,	A=0.1       ,	A=0.1       ,	A=0.0       ,	A=0.1       ,	A=0.02       ,	A=1E-7       ,	A=1E-5       ,	A="group"       ,	A="gelu"       ,	A=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)       ,	A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,	A=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,	A=False       ,	A=1_2_8       ,	A=1_6       ,	A=True       ,	A=0.05       ,	A=1_0       ,	A=2       ,	A=0.0       ,	A=1_0       ,	A=0       ,	A="mean"       ,	A=False       ,	A=False       ,	A=2_5_6       ,	A=0       ,	A=1       ,	A=2       ,	**A       ,	)							->					List[Any]:
         super().__init__(**_lowerCAmelCase       ,	pad_token_id=_lowerCAmelCase       ,	bos_token_id=_lowerCAmelCase       ,	eos_token_id=_lowerCAmelCase		)
         _UpperCAmelCase							:  Optional[Any]											=    hidden_size
         _UpperCAmelCase							:  Optional[Any]											=    feat_extract_norm
         _UpperCAmelCase							:  Tuple											=    feat_extract_activation
         _UpperCAmelCase							:  List[Any]											=    list(_lowerCAmelCase		)
         _UpperCAmelCase							:  Any											=    list(_lowerCAmelCase		)
         _UpperCAmelCase							:  Any											=    list(_lowerCAmelCase		)
         _UpperCAmelCase							:  Optional[int]											=    conv_bias
         _UpperCAmelCase							:  List[Any]											=    num_conv_pos_embeddings
         _UpperCAmelCase							:  Tuple											=    num_conv_pos_embedding_groups
         _UpperCAmelCase							:  List[Any]											=    len(self.conv_dim		)
         _UpperCAmelCase							:  int											=    num_hidden_layers
         _UpperCAmelCase							:  Optional[Any]											=    intermediate_size
         _UpperCAmelCase							:  List[str]											=    squeeze_factor
         _UpperCAmelCase							:  Tuple											=    max_position_embeddings
         _UpperCAmelCase							:  Tuple											=    position_buckets
         _UpperCAmelCase							:  List[str]											=    share_att_key
         _UpperCAmelCase							:  Optional[Any]											=    relative_attention
         _UpperCAmelCase							:  List[Any]											=    norm_rel_ebd
         _UpperCAmelCase							:  List[str]											=    list(_lowerCAmelCase		)
         _UpperCAmelCase							:  Dict											=    hidden_act
         _UpperCAmelCase							:  Dict											=    num_attention_heads
         _UpperCAmelCase							:  Tuple											=    hidden_dropout
         _UpperCAmelCase							:  str											=    attention_dropout
         _UpperCAmelCase							:  str											=    activation_dropout
         _UpperCAmelCase							:  str											=    feat_proj_dropout
         _UpperCAmelCase							:  List[Any]											=    final_dropout
         _UpperCAmelCase							:  Dict											=    layer_norm_eps
         _UpperCAmelCase							:  List[Any]											=    feature_layer_norm_eps
         _UpperCAmelCase							:  str											=    initializer_range
         _UpperCAmelCase							:  List[str]											=    vocab_size

         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)`,'''
                 f'but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'
                 f'= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'		)

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         _UpperCAmelCase							:  str											=    apply_spec_augment
         _UpperCAmelCase							:  Union[str, Any]											=    mask_time_prob
         _UpperCAmelCase							:  Union[str, Any]											=    mask_time_length
         _UpperCAmelCase							:  List[Any]											=    mask_time_min_masks
         _UpperCAmelCase							:  Union[str, Any]											=    mask_feature_prob
         _UpperCAmelCase							:  int											=    mask_feature_length
         _UpperCAmelCase							:  str											=    mask_feature_min_masks

         # ctc loss
         _UpperCAmelCase							:  Any											=    ctc_loss_reduction
         _UpperCAmelCase							:  Any											=    ctc_zero_infinity

         # sequence classification
         _UpperCAmelCase							:  Union[str, Any]											=    use_weighted_layer_sum
         _UpperCAmelCase							:  List[str]											=    classifier_proj_size





     @property
     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         return functools.reduce(operator.mul       ,	self.conv_stride       ,	1		)








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       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			ort.SessionOptions()
       SCREAMING_SNAKE_CASE_							=			False
       return options




 def       lowerCAmelCase_  (							self  :		Tuple						):
       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