

import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							def __init__(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=None       ,SCREAMING_SNAKE_CASE__		:      str=True       ,SCREAMING_SNAKE_CASE__		:      int=None       ,**SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    Any 	=			parent
								__lowerCamelCase    :    List[str] 	=			config_class
								__lowerCamelCase    :    Union[str, Any] 	=			has_text_modality
								__lowerCamelCase    :    Dict 	=			kwargs
								__lowerCamelCase    :    str 	=			common_properties
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    str 	=			self.config_class(**self.inputs_dict)
								__lowerCamelCase    :    int 	=			(
								    ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
								    if self.common_properties is None
								    else self.common_properties
								)

								# Add common fields for text models
								if self.has_text_modality:
									common_properties.extend(['vocab_size'])

								# Test that config has the common properties as getters
								for prop in common_properties:
									self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)       ,msg=F"`{prop}` does not exist")

								# Test that config has the common properties as setter
								for idx, name in enumerate(SCREAMING_SNAKE_CASE__):
									try:
										setattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
										self.parent.assertEqual(
										    getattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)       ,SCREAMING_SNAKE_CASE__       ,msg=F"`{name} value {idx} expected, but was {getattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)}")
									except NotImplementedError:
										# Some models might not be able to implement setters for common_properties
										# In that case, a NotImplementedError is raised
										pass

        # Test if config class can be called with Config(prop_name=..)
								for idx, name in enumerate(SCREAMING_SNAKE_CASE__):
									try:
										__lowerCamelCase    :    Dict 	=			self.config_class(**{name: idx})
										self.parent.assertEqual(
										    getattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)       ,SCREAMING_SNAKE_CASE__       ,msg=F"`{name} value {idx} expected, but was {getattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)}")
									except NotImplementedError:
										# Some models might not be able to implement setters for common_properties
										# In that case, a NotImplementedError is raised
										pass
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Optional[Any] 	=			self.config_class(**self.inputs_dict)
								__lowerCamelCase    :    Optional[int] 	=			json.loads(config.to_json_string())
								for key, value in self.inputs_dict.items():
									self.parent.assertEqual(obj[key]       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Any 	=			self.config_class(**self.inputs_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
									__lowerCamelCase    :    Optional[Any] 	=			os.path.join(SCREAMING_SNAKE_CASE__       ,'config.json')
									config_first.to_json_file(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    List[str] 	=			self.config_class.from_json_file(SCREAMING_SNAKE_CASE__)

								self.parent.assertEqual(config_second.to_dict()       ,config_first.to_dict())
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			self.config_class(**self.inputs_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
									config_first.save_pretrained(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Optional[Any] 	=			self.config_class.from_pretrained(SCREAMING_SNAKE_CASE__)

								self.parent.assertEqual(config_second.to_dict()       ,config_first.to_dict())
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Any 	=			self.config_class(**self.inputs_dict)

								__lowerCamelCase    :    Tuple 	=			'test'
								with tempfile.TemporaryDirectory() as tmpdirname:
									__lowerCamelCase    :    str 	=			os.path.join(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
									config_first.save_pretrained(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Union[str, Any] 	=			self.config_class.from_pretrained(SCREAMING_SNAKE_CASE__       ,subfolder=SCREAMING_SNAKE_CASE__)

								self.parent.assertEqual(config_second.to_dict()       ,config_first.to_dict())
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Any 	=			self.config_class(**self.inputs_dict       ,num_labels=5)
								self.parent.assertEqual(len(config.idalabel)       ,5)
								self.parent.assertEqual(len(config.labelaid)       ,5)

								__lowerCamelCase    :    List[str] 	=			3
								self.parent.assertEqual(len(config.idalabel)       ,3)
								self.parent.assertEqual(len(config.labelaid)       ,3)
							def    lowerCAmelCase					(		self		:      Dict):
								if self.config_class.is_composition:
									return
								__lowerCamelCase    :    List[str] 	=			self.config_class()
								self.parent.assertIsNotNone(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Optional[int] 	=			copy.deepcopy(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    int 	=			self.config_class(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			[]
								for key, value in config_common_kwargs.items():
									if key == "torch_dtype":
										if not is_torch_available():
											continue
										else:
											import torch

											if config.torch_dtype != torch.floataa:
												wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa))
									elif getattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__) != value:
										wrong_values.append((key, getattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__), value))

								if len(SCREAMING_SNAKE_CASE__) > 0:
									__lowerCamelCase    :    int 	=			'\n'.join([F"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values])
									raise ValueError(F"The following keys were not properly set in the config:\n{errors}")


							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								self.create_and_test_config_common_properties()
								self.create_and_test_config_to_json_string()
								self.create_and_test_config_to_json_file()
								self.create_and_test_config_from_and_save_pretrained()
								self.create_and_test_config_from_and_save_pretrained_subfolder()
								self.create_and_test_config_with_num_labels()
								self.check_config_can_be_init_without_params()
								self.check_config_arguments_init()







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""")




