





'''simple docstring'''

import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort





class lowerCamelCase						(a__						,      unittest.TestCase					):
  # FIXME: add fast tests
  pass





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







  @property
  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )







  @property
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	int					     =     ort.SessionOptions()
        _snake_case		:	str					     =     False
        return options







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	Any					     =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        _snake_case		:	Optional[int]					     =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        _snake_case		:	List[str]					     =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''     ,     revision='''onnx'''     ,     safety_checker=lowercase__     ,     feature_extractor=lowercase__     ,     provider=self.gpu_provider     ,     sess_options=self.gpu_options     ,     )
        pipe.set_progress_bar_config(disable=lowercase__		)

        _snake_case		:	Dict					     =     '''A red cat sitting on a park bench'''

        _snake_case		:	Tuple					     =     np.random.RandomState(0		)
        _snake_case		:	Dict					     =     pipe(
            prompt=lowercase__     ,     image=lowercase__     ,     mask_image=lowercase__     ,     guidance_scale=7.5     ,     num_inference_steps=10     ,     generator=lowercase__     ,     output_type='''np'''     ,     )
        _snake_case		:	Union[str, Any]					     =     output.images
        _snake_case		:	List[str]					     =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        _snake_case		:	List[Any]					     =     np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	int					     =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        _snake_case		:	List[str]					     =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        _snake_case		:	str					     =     LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''     ,     subfolder='''scheduler'''     ,     revision='''onnx'''		)
        _snake_case		:	Union[str, Any]					     =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''     ,     revision='''onnx'''     ,     scheduler=lowercase__     ,     safety_checker=lowercase__     ,     feature_extractor=lowercase__     ,     provider=self.gpu_provider     ,     sess_options=self.gpu_options     ,     )
        pipe.set_progress_bar_config(disable=lowercase__		)

        _snake_case		:	Dict					     =     '''A red cat sitting on a park bench'''

        _snake_case		:	int					     =     np.random.RandomState(0		)
        _snake_case		:	int					     =     pipe(
            prompt=lowercase__     ,     image=lowercase__     ,     mask_image=lowercase__     ,     guidance_scale=7.5     ,     num_inference_steps=20     ,     generator=lowercase__     ,     output_type='''np'''     ,     )
        _snake_case		:	List[Any]					     =     output.images
        _snake_case		:	Dict					     =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        _snake_case		:	Optional[int]					     =     np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3









'''simple docstring'''

import functools
import operator

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


UpperCAmelCase					:		List[str]		=							logging.get_logger(__name__)

UpperCAmelCase					:		Dict		=							{
    '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 lowerCamelCase						(a__					):
  _lowercase  :	List[str]       					=				"""sew-d"""







  def __init__(		self     ,     lowercase__=32     ,     lowercase__=768     ,     lowercase__=12     ,     lowercase__=12     ,     lowercase__=3_072     ,     lowercase__=2     ,     lowercase__=512     ,     lowercase__=256     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=("p2c", "c2p")     ,     lowercase__="layer_norm"     ,     lowercase__="gelu_python"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=0.0     ,     lowercase__=0.1     ,     lowercase__=0.02     ,     lowercase__=1E-7     ,     lowercase__=1E-5     ,     lowercase__="group"     ,     lowercase__="gelu"     ,     lowercase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,     lowercase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,     lowercase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,     lowercase__=False     ,     lowercase__=128     ,     lowercase__=16     ,     lowercase__=True     ,     lowercase__=0.05     ,     lowercase__=10     ,     lowercase__=2     ,     lowercase__=0.0     ,     lowercase__=10     ,     lowercase__=0     ,     lowercase__="mean"     ,     lowercase__=False     ,     lowercase__=False     ,     lowercase__=256     ,     lowercase__=0     ,     lowercase__=1     ,     lowercase__=2     ,     **lowercase__     ,     )		->						Dict:






        """simple docstring"""





        super().__init__(**lowercase__     ,     pad_token_id=lowercase__     ,     bos_token_id=lowercase__     ,     eos_token_id=lowercase__		)
        _snake_case		:	List[str]					     =     hidden_size
        _snake_case		:	Optional[Any]					     =     feat_extract_norm
        _snake_case		:	Tuple					     =     feat_extract_activation
        _snake_case		:	Tuple					     =     list(lowercase__		)
        _snake_case		:	Any					     =     list(lowercase__		)
        _snake_case		:	Any					     =     list(lowercase__		)
        _snake_case		:	Any					     =     conv_bias
        _snake_case		:	List[Any]					     =     num_conv_pos_embeddings
        _snake_case		:	Any					     =     num_conv_pos_embedding_groups
        _snake_case		:	Union[str, Any]					     =     len(self.conv_dim		)
        _snake_case		:	Optional[Any]					     =     num_hidden_layers
        _snake_case		:	Optional[int]					     =     intermediate_size
        _snake_case		:	Any					     =     squeeze_factor
        _snake_case		:	Optional[Any]					     =     max_position_embeddings
        _snake_case		:	Tuple					     =     position_buckets
        _snake_case		:	Tuple					     =     share_att_key
        _snake_case		:	Any					     =     relative_attention
        _snake_case		:	Optional[int]					     =     norm_rel_ebd
        _snake_case		:	Optional[Any]					     =     list(lowercase__		)
        _snake_case		:	List[Any]					     =     hidden_act
        _snake_case		:	List[Any]					     =     num_attention_heads
        _snake_case		:	Dict					     =     hidden_dropout
        _snake_case		:	Tuple					     =     attention_dropout
        _snake_case		:	Union[str, Any]					     =     activation_dropout
        _snake_case		:	List[Any]					     =     feat_proj_dropout
        _snake_case		:	Optional[int]					     =     final_dropout
        _snake_case		:	Optional[Any]					     =     layer_norm_eps
        _snake_case		:	Dict					     =     feature_layer_norm_eps
        _snake_case		:	List[Any]					     =     initializer_range
        _snake_case		:	Dict					     =     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
        _snake_case		:	Union[str, Any]					     =     apply_spec_augment
        _snake_case		:	Any					     =     mask_time_prob
        _snake_case		:	List[str]					     =     mask_time_length
        _snake_case		:	Dict					     =     mask_time_min_masks
        _snake_case		:	Union[str, Any]					     =     mask_feature_prob
        _snake_case		:	Tuple					     =     mask_feature_length
        _snake_case		:	Union[str, Any]					     =     mask_feature_min_masks

        # ctc loss
        _snake_case		:	Optional[Any]					     =     ctc_loss_reduction
        _snake_case		:	Optional[Any]					     =     ctc_zero_infinity

        # sequence classification
        _snake_case		:	List[Any]					     =     use_weighted_layer_sum
        _snake_case		:	Any					     =     classifier_proj_size







  @property
  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        return functools.reduce(operator.mul     ,     self.conv_stride     ,     1		)


