




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						={
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
UpperCAmelCase_						=[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]

def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		{}
							with open(_snake_case						, '''r'''							) as file:
														for line_number, line in enumerate(_snake_case							):
																					lowerCAmelCase   =		line.strip()
																					if line:
																												lowerCAmelCase   =		line.split()
																												lowerCAmelCase   =		line_number
																												lowerCAmelCase   =		words[0]
																												lowerCAmelCase   =		value
							return result

def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							):
							for attribute in key.split('''.'''							):
														lowerCAmelCase   =		getattr(_snake_case						, _snake_case							)

							lowerCAmelCase   =		None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_snake_case							):
																					lowerCAmelCase   =		PARAM_MAPPING[full_name.split('''.'''							)[-1]]
																					lowerCAmelCase   =		'''param'''

							if weight_type is not None and weight_type != "param":
														lowerCAmelCase   =		getattr(_snake_case						, _snake_case							).shape
							elif weight_type is not None and weight_type == "param":
														lowerCAmelCase   =		hf_pointer
														for attribute in hf_param_name.split('''.'''							):
																					lowerCAmelCase   =		getattr(_snake_case						, _snake_case							)
														lowerCAmelCase   =		shape_pointer.shape

														# let's reduce dimension
														lowerCAmelCase   =		value[0]
							else:
														lowerCAmelCase   =		hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
														    F""" {value.shape} for {full_name}"""							)

							if weight_type == "weight":
														lowerCAmelCase   =		value
							elif weight_type == "weight_g":
														lowerCAmelCase   =		value
							elif weight_type == "weight_v":
														lowerCAmelCase   =		value
							elif weight_type == "bias":
														lowerCAmelCase   =		value
							elif weight_type == "param":
														for attribute in hf_param_name.split('''.'''							):
																					lowerCAmelCase   =		getattr(_snake_case						, _snake_case							)
														lowerCAmelCase   =		value
							else:
														lowerCAmelCase   =		value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""							)

def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_snake_case							):
																					lowerCAmelCase   =		PARAM_MAPPING[full_name.split('''.'''							)[-1]]
																					lowerCAmelCase   =		'''param'''

							if weight_type is not None and weight_type != "param":
														lowerCAmelCase   =		'''.'''.join([key, weight_type]							)
							elif weight_type is not None and weight_type == "param":
														lowerCAmelCase   =		'''.'''.join([key, hf_param_name]							)
							else:
														lowerCAmelCase   =		key

							lowerCAmelCase   =		value if '''lm_head''' in full_key else value[0]


UpperCAmelCase_						={
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}

def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case=None						, _snake_case=None							):
							lowerCAmelCase   =		False
							for key, mapped_key in MAPPING.items():
														lowerCAmelCase   =		'''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
														if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
																					lowerCAmelCase   =		True
																					if "*" in mapped_key:
																												lowerCAmelCase   =		name.split(_snake_case							)[0].split('''.'''							)[-2]
																												lowerCAmelCase   =		mapped_key.replace('''*'''						, _snake_case							)
																					if "weight_g" in name:
																												lowerCAmelCase   =		'''weight_g'''
																					elif "weight_v" in name:
																												lowerCAmelCase   =		'''weight_v'''
																					elif "bias" in name:
																												lowerCAmelCase   =		'''bias'''
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												lowerCAmelCase   =		'''weight'''
																					else:
																												lowerCAmelCase   =		None
																					if hf_dict is not None:
																												rename_dict(_snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							)
																					else:
																												set_recursively(_snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							)
																					return is_used
							return is_used

def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[]
							lowerCAmelCase   =		fairseq_model.state_dict()

							lowerCAmelCase   =		hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCAmelCase   =		False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _snake_case						, _snake_case						, _snake_case						, _snake_case						, hf_model.config.feat_extract_norm == '''group'''						, )
																					lowerCAmelCase   =		True
														else:
																					lowerCAmelCase   =		load_wavaveca_layer(_snake_case						, _snake_case						, _snake_case							)
														if not is_used:
																					unused_weights.append(_snake_case							)

							logger.warning(F"""Unused weights: {unused_weights}"""							)

def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		full_name.split('''conv_layers.'''							)[-1]
							lowerCAmelCase   =		name.split('''.'''							)
							lowerCAmelCase   =		int(items[0]							)
							lowerCAmelCase   =		int(items[1]							)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""							)
																					lowerCAmelCase   =		value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""							)
																					lowerCAmelCase   =		value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""							)
																					lowerCAmelCase   =		value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""							)
																					lowerCAmelCase   =		value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
							else:
														unused_weights.append(_snake_case							)

@torch.no_grad()
def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case=None						, _snake_case=None						, _snake_case=True						, _snake_case=False							):
							if config_path is not None:
														lowerCAmelCase   =		WavaVecaConfig.from_pretrained(_snake_case							)
							else:
														lowerCAmelCase   =		WavaVecaConfig()

							if is_seq_class:
														lowerCAmelCase   =		read_txt_into_dict(_snake_case							)
														lowerCAmelCase   =		idalabel
														lowerCAmelCase   =		WavaVecaForSequenceClassification(_snake_case							)
														lowerCAmelCase   =		WavaVecaFeatureExtractor(
														    feature_size=1						, sampling_rate=16000						, padding_value=0						, do_normalize=_snake_case						, return_attention_mask=_snake_case						, )
														feature_extractor.save_pretrained(_snake_case							)

							elif is_finetuned:
														if dict_path:
																					lowerCAmelCase   =		Dictionary.load(_snake_case							)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					lowerCAmelCase   =		target_dict.pad_index
																					lowerCAmelCase   =		target_dict.bos_index
																					lowerCAmelCase   =		target_dict.eos_index
																					lowerCAmelCase   =		len(target_dict.symbols							)
																					lowerCAmelCase   =		os.path.join(_snake_case						, '''vocab.json'''							)
																					if not os.path.isdir(_snake_case							):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case							)							)
																												return
																					os.makedirs(_snake_case						, exist_ok=_snake_case							)
																					lowerCAmelCase   =		target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					lowerCAmelCase   =		0
																					lowerCAmelCase   =		1
																					with open(_snake_case						, '''w'''						, encoding='''utf-8'''							) as vocab_handle:
																												json.dump(_snake_case						, _snake_case							)
																					lowerCAmelCase   =		WavaVecaCTCTokenizer(
																					    _snake_case						, unk_token=target_dict.unk_word						, pad_token=target_dict.pad_word						, bos_token=target_dict.bos_word						, eos_token=target_dict.eos_word						, word_delimiter_token='''|'''						, do_lower_case=_snake_case						, )
																					lowerCAmelCase   =		True if config.feat_extract_norm == '''layer''' else False
																					lowerCAmelCase   =		WavaVecaFeatureExtractor(
																					    feature_size=1						, sampling_rate=16000						, padding_value=0						, do_normalize=_snake_case						, return_attention_mask=_snake_case						, )
																					lowerCAmelCase   =		WavaVecaProcessor(feature_extractor=_snake_case						, tokenizer=_snake_case							)
																					processor.save_pretrained(_snake_case							)

														lowerCAmelCase   =		WavaVecaForCTC(_snake_case							)
							else:
														lowerCAmelCase   =		WavaVecaForPreTraining(_snake_case							)

							if is_finetuned or is_seq_class:
														lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]						, arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
							else:
														lowerCAmelCase   =		argparse.Namespace(task='''audio_pretraining'''							)
														lowerCAmelCase   =		fairseq.tasks.setup_task(_snake_case							)

														lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						, task=_snake_case							)

							lowerCAmelCase   =		model[0].eval()

							recursively_load_weights(_snake_case						, _snake_case						, not is_finetuned							)

							hf_wavavec.save_pretrained(_snake_case							)


if __name__ == "__main__":
		UpperCAmelCase_						=argparse.ArgumentParser()
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
		parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
		parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
		parser.add_argument(
		    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
		)
		parser.add_argument(
		    """--is_seq_class""",
		    action="""store_true""",
		    help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
		)
		UpperCAmelCase_						=parser.parse_args()

		UpperCAmelCase_						=not args.not_finetuned and not args.is_seq_class
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.dict_path,
		    is_finetuned,
		    args.is_seq_class,
		)









import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 							__UpperCamelCase			( __UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Any										="""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



				def       __snake_case		( self       , UpperCAmelCase_=0						):
											lowerCAmelCase   =		floats_tensor((1, 3, 1_28, 1_28)       , rng=random.Random(UpperCAmelCase_						)						)
											lowerCAmelCase   =		np.random.RandomState(UpperCAmelCase_						)
											lowerCAmelCase   =		{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''image''': image,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''strength''': 0.75,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]						)
											assert np.abs(image_slice - expected_slice						).max() < 1E-1



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		PNDMScheduler.from_config(pipe.scheduler.config       , skip_prk_steps=UpperCAmelCase_						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											# warmup pass to apply optimizations
											lowerCAmelCase   =		pipe(**self.get_dummy_inputs()						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

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






				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]						)

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




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





				'''simple docstring'''



				@property
				def       __snake_case		( self						):
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)



				@property
				def       __snake_case		( self						):
											lowerCAmelCase   =		ort.SessionOptions()
											lowerCAmelCase   =		False
											return options



				def       __snake_case		( self						):
											lowerCAmelCase   =		load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/img2img/sketch-mountains-input.jpg'''						)
											lowerCAmelCase   =		init_image.resize((7_68, 5_12)						)
											# using the PNDM scheduler by default
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(
											    '''CompVis/stable-diffusion-v1-4'''       , revision='''onnx'''       , safety_checker=UpperCAmelCase_       , feature_extractor=UpperCAmelCase_       , provider=self.gpu_provider       , sess_options=self.gpu_options       , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		'''A fantasy landscape, trending on artstation'''

											lowerCAmelCase   =		np.random.RandomState(0						)
											lowerCAmelCase   =		pipe(
											    prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , strength=0.75       , guidance_scale=7.5       , num_inference_steps=10       , generator=UpperCAmelCase_       , output_type='''np'''       , )
											lowerCAmelCase   =		output.images
											lowerCAmelCase   =		images[0, 2_55:2_58, 3_83:3_86, -1]

											assert images.shape == (1, 5_12, 7_68, 3)
											lowerCAmelCase   =		np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]						)
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 2E-2






				def       __snake_case		( self						):
											lowerCAmelCase   =		load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/img2img/sketch-mountains-input.jpg'''						)
											lowerCAmelCase   =		init_image.resize((7_68, 5_12)						)
											lowerCAmelCase   =		LMSDiscreteScheduler.from_pretrained(
											    '''runwayml/stable-diffusion-v1-5'''       , subfolder='''scheduler'''       , revision='''onnx'''						)
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(
											    '''runwayml/stable-diffusion-v1-5'''       , revision='''onnx'''       , scheduler=UpperCAmelCase_       , safety_checker=UpperCAmelCase_       , feature_extractor=UpperCAmelCase_       , provider=self.gpu_provider       , sess_options=self.gpu_options       , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		'''A fantasy landscape, trending on artstation'''

											lowerCAmelCase   =		np.random.RandomState(0						)
											lowerCAmelCase   =		pipe(
											    prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , strength=0.75       , guidance_scale=7.5       , num_inference_steps=20       , generator=UpperCAmelCase_       , output_type='''np'''       , )
											lowerCAmelCase   =		output.images
											lowerCAmelCase   =		images[0, 2_55:2_58, 3_83:3_86, -1]

											assert images.shape == (1, 5_12, 7_68, 3)
											lowerCAmelCase   =		np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]						)
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 2E-2



