

import functools
import operator

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


a		=logging.get_logger(__name__)

a		={
    """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 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  int										=				'''sew-d'''
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str]=3_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=7_6_8       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=3_0_7_2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=2       ,SCREAMING_SNAKE_CASE__		:      int=5_1_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=2_5_6       ,SCREAMING_SNAKE_CASE__		:      str=True       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,SCREAMING_SNAKE_CASE__		:      str=("p2c", "c2p")       ,SCREAMING_SNAKE_CASE__		:      int="layer_norm"       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="gelu_python"       ,SCREAMING_SNAKE_CASE__		:      Dict=0.1       ,SCREAMING_SNAKE_CASE__		:      str=0.1       ,SCREAMING_SNAKE_CASE__		:      str=0.1       ,SCREAMING_SNAKE_CASE__		:      int=0.0       ,SCREAMING_SNAKE_CASE__		:      str=0.1       ,SCREAMING_SNAKE_CASE__		:      List[str]=0.02       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1E-7       ,SCREAMING_SNAKE_CASE__		:      str=1E-5       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="group"       ,SCREAMING_SNAKE_CASE__		:      int="gelu"       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=(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)       ,SCREAMING_SNAKE_CASE__		:      str=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,SCREAMING_SNAKE_CASE__		:      Any=False       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_2_8       ,SCREAMING_SNAKE_CASE__		:      Any=1_6       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=True       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.05       ,SCREAMING_SNAKE_CASE__		:      int=1_0       ,SCREAMING_SNAKE_CASE__		:      str=2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=0.0       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_0       ,SCREAMING_SNAKE_CASE__		:      List[str]=0       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="mean"       ,SCREAMING_SNAKE_CASE__		:      List[str]=False       ,SCREAMING_SNAKE_CASE__		:      List[str]=False       ,SCREAMING_SNAKE_CASE__		:      Tuple=2_5_6       ,SCREAMING_SNAKE_CASE__		:      Dict=0       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=2       ,**SCREAMING_SNAKE_CASE__		:      str       ,):
								super().__init__(**_a       ,pad_token_id=_a       ,bos_token_id=_a       ,eos_token_id=_a)
								__lowerCamelCase    :    Any 	=			hidden_size
								__lowerCamelCase    :    Optional[int] 	=			feat_extract_norm
								__lowerCamelCase    :    str 	=			feat_extract_activation
								__lowerCamelCase    :    List[str] 	=			list(_a)
								__lowerCamelCase    :    List[Any] 	=			list(_a)
								__lowerCamelCase    :    int 	=			list(_a)
								__lowerCamelCase    :    List[str] 	=			conv_bias
								__lowerCamelCase    :    Dict 	=			num_conv_pos_embeddings
								__lowerCamelCase    :    Union[str, Any] 	=			num_conv_pos_embedding_groups
								__lowerCamelCase    :    List[str] 	=			len(self.conv_dim)
								__lowerCamelCase    :    str 	=			num_hidden_layers
								__lowerCamelCase    :    Tuple 	=			intermediate_size
								__lowerCamelCase    :    List[str] 	=			squeeze_factor
								__lowerCamelCase    :    List[Any] 	=			max_position_embeddings
								__lowerCamelCase    :    int 	=			position_buckets
								__lowerCamelCase    :    Union[str, Any] 	=			share_att_key
								__lowerCamelCase    :    List[Any] 	=			relative_attention
								__lowerCamelCase    :    Optional[int] 	=			norm_rel_ebd
								__lowerCamelCase    :    int 	=			list(_a)
								__lowerCamelCase    :    List[Any] 	=			hidden_act
								__lowerCamelCase    :    Tuple 	=			num_attention_heads
								__lowerCamelCase    :    Any 	=			hidden_dropout
								__lowerCamelCase    :    Tuple 	=			attention_dropout
								__lowerCamelCase    :    List[Any] 	=			activation_dropout
								__lowerCamelCase    :    Union[str, Any] 	=			feat_proj_dropout
								__lowerCamelCase    :    List[str] 	=			final_dropout
								__lowerCamelCase    :    Optional[Any] 	=			layer_norm_eps
								__lowerCamelCase    :    int 	=			feature_layer_norm_eps
								__lowerCamelCase    :    Any 	=			initializer_range
								__lowerCamelCase    :    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
								__lowerCamelCase    :    Dict 	=			apply_spec_augment
								__lowerCamelCase    :    List[Any] 	=			mask_time_prob
								__lowerCamelCase    :    Optional[int] 	=			mask_time_length
								__lowerCamelCase    :    Optional[Any] 	=			mask_time_min_masks
								__lowerCamelCase    :    List[str] 	=			mask_feature_prob
								__lowerCamelCase    :    List[Any] 	=			mask_feature_length
								__lowerCamelCase    :    Optional[int] 	=			mask_feature_min_masks

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

								# sequence classification
								__lowerCamelCase    :    str 	=			use_weighted_layer_sum
								__lowerCamelCase    :    Optional[Any] 	=			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 	A_		(	unittest.TestCase    ):
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)
							@property
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			ort.SessionOptions()
								__lowerCamelCase    :    Tuple 	=			False
								return options


							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Tuple 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png')
								__lowerCamelCase    :    List[str] 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
								__lowerCamelCase    :    Optional[int] 	=			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
								__lowerCamelCase    :    Dict 	=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'       ,revision='onnx'       ,safety_checker=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			'A red cat sitting on a park bench'

								__lowerCamelCase    :    Any 	=			np.random.RandomState(0)
								__lowerCamelCase    :    List[Any] 	=			pipe(
								    prompt=SCREAMING_SNAKE_CASE__       ,image=SCREAMING_SNAKE_CASE__       ,mask_image=SCREAMING_SNAKE_CASE__       ,strength=0.75       ,guidance_scale=7.5       ,num_inference_steps=1_5       ,generator=SCREAMING_SNAKE_CASE__       ,output_type='np'       ,)

								__lowerCamelCase    :    Union[str, Any] 	=			output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image).max() < 1E-2




