


import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
						import onnxruntime as ort







class 				snake_case__(							_UpperCAmelCase,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  List[str]					=							"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""




						def 						lowercase_ (				self   ,						__lowercase=0							)					->    List[str]:
										lowerCAmelCase_ :      List[str]    				=				np.random.RandomState(lowercase__							)
										lowerCAmelCase_ :      List[str]    				=				{
										    """prompt""": """A painting of a squirrel eating a burger""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 7.5,
										    """output_type""": """numpy""",
										}
										return inputs




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Dict    				=				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,						provider='''CPUExecutionProvider'''							)
										pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      Optional[Any]    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      Dict    				=				pipe(**lowercase__							).images
										lowerCAmelCase_ :      Tuple    				=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_2_8, 1_2_8, 3)
										lowerCAmelCase_ :      Union[str, Any]    				=				np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      str    				=				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,						provider='''CPUExecutionProvider'''							)
										lowerCAmelCase_ :      Union[str, Any]    				=				PNDMScheduler.from_config(pipe.scheduler.config   ,						skip_prk_steps=lowercase__							)
										pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      Optional[Any]    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      str    				=				pipe(**lowercase__							).images
										lowerCAmelCase_ :      Optional[int]    				=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_2_8, 1_2_8, 3)
										lowerCAmelCase_ :      Tuple    				=				np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      str    				=				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,						provider='''CPUExecutionProvider'''							)
										lowerCAmelCase_ :      Any    				=				LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
										pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      int    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      Any    				=				pipe(**lowercase__							).images
										lowerCAmelCase_ :      List[str]    				=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_2_8, 1_2_8, 3)
										lowerCAmelCase_ :      Dict    				=				np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      List[Any]    				=				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,						provider='''CPUExecutionProvider'''							)
										lowerCAmelCase_ :      Optional[int]    				=				EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
										pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      Optional[Any]    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      Optional[Any]    				=				pipe(**lowercase__							).images
										lowerCAmelCase_ :      Dict    				=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_2_8, 1_2_8, 3)
										lowerCAmelCase_ :      Tuple    				=				np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      int    				=				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,						provider='''CPUExecutionProvider'''							)
										lowerCAmelCase_ :      Optional[int]    				=				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
										pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      Union[str, Any]    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      Optional[Any]    				=				pipe(**lowercase__							).images
										lowerCAmelCase_ :      Dict    				=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_2_8, 1_2_8, 3)
										lowerCAmelCase_ :      str    				=				np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Any    				=				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,						provider='''CPUExecutionProvider'''							)
										lowerCAmelCase_ :      Union[str, Any]    				=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
										pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      int    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      Optional[int]    				=				pipe(**lowercase__							).images
										lowerCAmelCase_ :      List[Any]    				=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 1_2_8, 1_2_8, 3)
										lowerCAmelCase_ :      Tuple    				=				np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Dict    				=				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,						provider='''CPUExecutionProvider'''							)
										pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      Tuple    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      List[Any]    				=				3 * [inputs["""prompt"""]]

										# forward
										lowerCAmelCase_ :      Optional[Any]    				=				pipe(**lowercase__							)
										lowerCAmelCase_ :      Optional[Any]    				=				output.images[0, -3:, -3:, -1]

										lowerCAmelCase_ :      Optional[int]    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      Optional[int]    				=				3 * [inputs.pop('''prompt'''							)]

										lowerCAmelCase_ :      str    				=				pipe.tokenizer(
										    lowercase__   ,						padding='''max_length'''   ,						max_length=pipe.tokenizer.model_max_length   ,						truncation=lowercase__   ,						return_tensors='''np'''   ,						)
										lowerCAmelCase_ :      Any    				=				text_inputs["""input_ids"""]

										lowerCAmelCase_ :      Dict    				=				pipe.text_encoder(input_ids=text_inputs.astype(np.intaa							)							)[0]

										lowerCAmelCase_ :      Optional[int]    				=				prompt_embeds

										# forward
										lowerCAmelCase_ :      Optional[int]    				=				pipe(**lowercase__							)
										lowerCAmelCase_ :      int    				=				output.images[0, -3:, -3:, -1]

										assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()							).max() < 1e-4




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      str    				=				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,						provider='''CPUExecutionProvider'''							)
										pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      Tuple    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      List[Any]    				=				3 * ["""this is a negative prompt"""]
										lowerCAmelCase_ :      Any    				=				negative_prompt
										lowerCAmelCase_ :      Dict    				=				3 * [inputs["""prompt"""]]

										# forward
										lowerCAmelCase_ :      Optional[int]    				=				pipe(**lowercase__							)
										lowerCAmelCase_ :      List[Any]    				=				output.images[0, -3:, -3:, -1]

										lowerCAmelCase_ :      Optional[int]    				=				self.get_dummy_inputs()
										lowerCAmelCase_ :      List[Any]    				=				3 * [inputs.pop('''prompt'''							)]

										lowerCAmelCase_ :      List[Any]    				=				[]
										for p in [prompt, negative_prompt]:
														lowerCAmelCase_ :      str    				=				pipe.tokenizer(
														    lowercase__   ,						padding='''max_length'''   ,						max_length=pipe.tokenizer.model_max_length   ,						truncation=lowercase__   ,						return_tensors='''np'''   ,						)
														lowerCAmelCase_ :      Any    				=				text_inputs["""input_ids"""]

														embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa							)							)[0]							)

										lowerCAmelCase_ :      str    				=				embeds

										# forward
										lowerCAmelCase_ :      str    				=				pipe(**lowercase__							)
										lowerCAmelCase_ :      int    				=				output.images[0, -3:, -3:, -1]

										assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()							).max() < 1e-4







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







						'''simple docstring'''




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




						@property
						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      str    				=				ort.SessionOptions()
										lowerCAmelCase_ :      Any    				=				False
										return options




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										# using the PNDM scheduler by default
										lowerCAmelCase_ :      List[Any]    				=				OnnxStableDiffusionPipeline.from_pretrained(
										    '''CompVis/stable-diffusion-v1-4'''   ,						revision='''onnx'''   ,						safety_checker=lowercase__   ,						feature_extractor=lowercase__   ,						provider=self.gpu_provider   ,						sess_options=self.gpu_options   ,						)
										sd_pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      Any    				=				"""A painting of a squirrel eating a burger"""
										np.random.seed(0							)
										lowerCAmelCase_ :      Dict    				=				sd_pipe([prompt]   ,						guidance_scale=6.0   ,						num_inference_steps=1_0   ,						output_type='''np'''							)
										lowerCAmelCase_ :      List[Any]    				=				output.images

										lowerCAmelCase_ :      Optional[Any]    				=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										lowerCAmelCase_ :      Dict    				=				np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]							)

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




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_ :      str    				=				DDIMScheduler.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''   ,						subfolder='''scheduler'''   ,						revision='''onnx'''							)
										lowerCAmelCase_ :      List[str]    				=				OnnxStableDiffusionPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''   ,						revision='''onnx'''   ,						scheduler=lowercase__   ,						safety_checker=lowercase__   ,						feature_extractor=lowercase__   ,						provider=self.gpu_provider   ,						sess_options=self.gpu_options   ,						)
										sd_pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      List[Any]    				=				"""open neural network exchange"""
										lowerCAmelCase_ :      str    				=				np.random.RandomState(0							)
										lowerCAmelCase_ :      Dict    				=				sd_pipe([prompt]   ,						guidance_scale=7.5   ,						num_inference_steps=1_0   ,						generator=lowercase__   ,						output_type='''np'''							)
										lowerCAmelCase_ :      int    				=				output.images
										lowerCAmelCase_ :      Optional[Any]    				=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										lowerCAmelCase_ :      str    				=				np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]							)

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




						def 						lowercase_ (				self							)					->    Optional[int]:
										lowerCAmelCase_ :      Optional[Any]    				=				LMSDiscreteScheduler.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''   ,						subfolder='''scheduler'''   ,						revision='''onnx'''							)
										lowerCAmelCase_ :      List[Any]    				=				OnnxStableDiffusionPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''   ,						revision='''onnx'''   ,						scheduler=lowercase__   ,						safety_checker=lowercase__   ,						feature_extractor=lowercase__   ,						provider=self.gpu_provider   ,						sess_options=self.gpu_options   ,						)
										sd_pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      Dict    				=				"""open neural network exchange"""
										lowerCAmelCase_ :      Any    				=				np.random.RandomState(0							)
										lowerCAmelCase_ :      str    				=				sd_pipe([prompt]   ,						guidance_scale=7.5   ,						num_inference_steps=1_0   ,						generator=lowercase__   ,						output_type='''np'''							)
										lowerCAmelCase_ :      Union[str, Any]    				=				output.images
										lowerCAmelCase_ :      str    				=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										lowerCAmelCase_ :      Optional[int]    				=				np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]							)

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




						def 						lowercase_ (				self							)					->    str:
										lowerCAmelCase_ :      str    				=				0

										def test_callback_fn(__lowercase   ,						__lowercase   ,						__lowercase							) -> None:
														lowerCAmelCase_ :      Optional[int]    				=				True
														nonlocal number_of_steps
														number_of_steps += 1
														if step == 0:
																		assert latents.shape == (1, 4, 6_4, 6_4)
																		lowerCAmelCase_ :      str    				=				latents[0, -3:, -3:, -1]
																		lowerCAmelCase_ :      str    				=				np.array(
																		    [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]							)

																		assert np.abs(latents_slice.flatten() - expected_slice							).max() < 1e-3
														elif step == 5:
																		assert latents.shape == (1, 4, 6_4, 6_4)
																		lowerCAmelCase_ :      Union[str, Any]    				=				latents[0, -3:, -3:, -1]
																		lowerCAmelCase_ :      Optional[Any]    				=				np.array(
																		    [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]							)

																		assert np.abs(latents_slice.flatten() - expected_slice							).max() < 1e-3

										lowerCAmelCase_ :      Union[str, Any]    				=				False

										lowerCAmelCase_ :      Any    				=				OnnxStableDiffusionPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''   ,						revision='''onnx'''   ,						safety_checker=lowercase__   ,						feature_extractor=lowercase__   ,						provider=self.gpu_provider   ,						sess_options=self.gpu_options   ,						)
										pipe.set_progress_bar_config(disable=lowercase__							)

										lowerCAmelCase_ :      Optional[int]    				=				"""Andromeda galaxy in a bottle"""

										lowerCAmelCase_ :      Union[str, Any]    				=				np.random.RandomState(0							)
										pipe(
										    prompt=lowercase__   ,						num_inference_steps=5   ,						guidance_scale=7.5   ,						generator=lowercase__   ,						callback=lowercase__   ,						callback_steps=1   ,						)
										assert test_callback_fn.has_been_called
										assert number_of_steps == 6




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Optional[int]    				=				OnnxStableDiffusionPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''   ,						revision='''onnx'''   ,						safety_checker=lowercase__   ,						feature_extractor=lowercase__   ,						provider=self.gpu_provider   ,						sess_options=self.gpu_options   ,						)
										assert isinstance(lowercase__   ,						lowercase__							)
										assert pipe.safety_checker is None

										lowerCAmelCase_ :      int    				=				pipe('''example prompt'''   ,						num_inference_steps=2							).images[0]
										assert image is not None

										# check that there's no error when saving a pipeline with one of the models being None
										with tempfile.TemporaryDirectory() as tmpdirname:
														pipe.save_pretrained(lowercase__							)
														lowerCAmelCase_ :      int    				=				OnnxStableDiffusionPipeline.from_pretrained(lowercase__							)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										lowerCAmelCase_ :      Optional[Any]    				=				pipe('''example prompt'''   ,						num_inference_steps=2							).images[0]
										assert image is not None



import re



def 			lowerCAmelCase (		lowerCAmelCase_ )-> bool:
				lowerCAmelCase_ :      Tuple    				=				re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
				if match := re.search(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								return match.string == phone
				return False


if __name__ == "__main__":
						print(indian_phone_validator("""+918827897895"""))