

"""simple docstring"""




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					a		=False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor






class 	A_		(	unittest.TestCase    ):
							def __init__(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=7       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=3       ,SCREAMING_SNAKE_CASE__		:      List[str]=1_8       ,SCREAMING_SNAKE_CASE__		:      List[str]=3_0       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=4_0_0       ,SCREAMING_SNAKE_CASE__		:      Any=None       ,SCREAMING_SNAKE_CASE__		:      int=True       ,SCREAMING_SNAKE_CASE__		:      Tuple=True       ,SCREAMING_SNAKE_CASE__		:      int=None       ,):
								__lowerCamelCase    :    Any 	=			size if size is not None else {'height': 2_0, 'width': 2_0}
								__lowerCamelCase    :    str 	=			parent
								__lowerCamelCase    :    List[str] 	=			batch_size
								__lowerCamelCase    :    int 	=			num_channels
								__lowerCamelCase    :    List[str] 	=			image_size
								__lowerCamelCase    :    int 	=			min_resolution
								__lowerCamelCase    :    Union[str, Any] 	=			max_resolution
								__lowerCamelCase    :    List[Any] 	=			size
								__lowerCamelCase    :    str 	=			do_normalize
								__lowerCamelCase    :    Tuple 	=			do_convert_rgb
								__lowerCamelCase    :    List[Any] 	=			[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
								__lowerCamelCase    :    Tuple 	=			patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}
							def    lowerCAmelCase					(		self		:      List[Any]):
								return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}


							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Dict 	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
								__lowerCamelCase    :    Dict 	=			Image.open(requests.get(SCREAMING_SNAKE_CASE__       ,stream=SCREAMING_SNAKE_CASE__).raw).convert('RGB')
								return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''      ,							)
@require_torch
@require_vision
class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  str										=				PixaStructImageProcessor if is_vision_available() else None
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    List[str] 	=			PixaStructImageProcessingTester(self)
							@property
							def    lowerCAmelCase					(		self		:      Any):
								return self.image_processor_tester.prepare_image_processor_dict()
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Dict 	=			self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_normalize'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_convert_rgb'))
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[Any] 	=			self.image_processor_tester.prepare_dummy_image()

								__lowerCamelCase    :    int 	=			self.image_processing_class(**self.image_processor_dict)
								__lowerCamelCase    :    Optional[int] 	=			2_0_4_8

								__lowerCamelCase    :    Any 	=			image_processor(SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,torch.tensor(0.0606)       ,atol=1E-3       ,rtol=1E-3))
							def    lowerCAmelCase					(		self		:      Tuple):
								# Initialize image_processor
								__lowerCamelCase    :    Any 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase    :    Dict 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,Image.Image)

								# Test not batched input
								__lowerCamelCase    :    int 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									__lowerCamelCase    :    Union[str, Any] 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    Optional[int] 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								# Initialize image_processor
								__lowerCamelCase    :    str 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase    :    List[str] 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,Image.Image)

								# Test not batched input
								__lowerCamelCase    :    str 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								__lowerCamelCase    :    str 	=			True

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(SCREAMING_SNAKE_CASE__):
										__lowerCamelCase    :    Optional[Any] 	=			image_processor(
										    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches

									__lowerCamelCase    :    Union[str, Any] 	=			'Hello'

									__lowerCamelCase    :    Optional[Any] 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__       ,header_text=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    int 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__       ,header_text=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								# Initialize image_processor
								__lowerCamelCase    :    Tuple 	=			self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								__lowerCamelCase    :    Dict 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__       ,numpify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,np.ndarray)

								__lowerCamelCase    :    Any 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									__lowerCamelCase    :    Tuple 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    str 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)


							def    lowerCAmelCase					(		self		:      Dict):
								# Initialize image_processor
								__lowerCamelCase    :    Any 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								__lowerCamelCase    :    int 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__       ,torchify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor)

								# Test not batched input
								__lowerCamelCase    :    Optional[int] 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									__lowerCamelCase    :    Union[str, Any] 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    str 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''      ,							)
@require_torch
@require_vision
class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  List[str]										=				PixaStructImageProcessor if is_vision_available() else None
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    str 	=			PixaStructImageProcessingTester(self       ,num_channels=4)
								__lowerCamelCase    :    Optional[Any] 	=			3
							@property
							def    lowerCAmelCase					(		self		:      int):
								return self.image_processor_tester.prepare_image_processor_dict()
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    str 	=			self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_normalize'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_convert_rgb'))


							def    lowerCAmelCase					(		self		:      Dict):
								# Initialize image_processor
								__lowerCamelCase    :    Dict 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase    :    int 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,Image.Image)

								# Test not batched input
								__lowerCamelCase    :    int 	=			(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * (self.image_processor_tester.num_channels - 1)
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									__lowerCamelCase    :    Tuple 	=			image_processor(
									    image_inputs[0]       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(1, max_patch, expected_hidden_dim)       ,)

									# Test batched
									__lowerCamelCase    :    Optional[Any] 	=			image_processor(
									    SCREAMING_SNAKE_CASE__       ,return_tensors='pt'       ,max_patches=SCREAMING_SNAKE_CASE__).flattened_patches
									self.assertEqual(
									    encoded_images.shape       ,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,)







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a		=argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
a		=parser.parse_args()


a		="""cpu"""
a		="""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

a		="""path-to-your-trained-model"""
a		=StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					a		=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
a		=pipe.to(device)

# to channels last
a		=pipe.unet.to(memory_format=torch.channels_last)
a		=pipe.vae.to(memory_format=torch.channels_last)
a		=pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					a		=pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
a		=torch.randn(2, 4, 64, 64)
a		=torch.rand(1) * 999
a		=torch.randn(2, 77, 768)
a		=(sample, timestep, encoder_hidden_status)
try:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					a		=ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
a		=666
a		=torch.Generator(device).manual_seed(seed)
a		={"""generator""": generator}
if args.steps is not None:
					a		=args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					a		=pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")




