
'''simple docstring'''

import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

UpperCAmelCase__				:	Tuple					=						[[12, 10], [3, 9]]
UpperCAmelCase__				:	int					=						[[3, 4], [7, 4]]
UpperCAmelCase__				:	str					=						[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
UpperCAmelCase__				:	Optional[Any]					=						[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
UpperCAmelCase__				:	List[str]					=						[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
UpperCAmelCase__				:	List[str]					=						[1]
UpperCAmelCase__				:	int					=						[2]

UpperCAmelCase__				:	str					=						logging.getLogger()
UpperCAmelCase__				:	List[Any]					=						logging.DEBUG
UpperCAmelCase__				:	Dict					=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")  ,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)] )
def 					A       ( UpperCamelCase_   :       Tuple  ,		UpperCamelCase_   :       Dict )   ->    Optional[int]:







			'''simple docstring'''



			if (np.array(UpperCamelCase_ )).shape < (2, 2) or (np.array(UpperCamelCase_ )).shape < (2, 2):
						with pytest.raises(UpperCamelCase_ ):
									logger.info(F"""\n\t{test_addition.__name__} returned integer""" )
									matop.add(UpperCamelCase_  ,		UpperCamelCase_ )
			elif (np.array(UpperCamelCase_ )).shape == (np.array(UpperCamelCase_ )).shape:
						logger.info(F"""\n\t{test_addition.__name__} with same matrix dims""" )
						lowerCAmelCase__									=							(np.array(UpperCamelCase_ ) + np.array(UpperCamelCase_ )).tolist()
						lowerCAmelCase__									=							matop.add(UpperCamelCase_  ,		UpperCamelCase_ )
						assert theo == act
			else:
						with pytest.raises(UpperCamelCase_ ):
									logger.info(F"""\n\t{test_addition.__name__} with different matrix dims""" )
									matop.add(UpperCamelCase_  ,		UpperCamelCase_ )







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")  ,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)] )
def 					A       ( UpperCamelCase_   :       Dict  ,		UpperCamelCase_   :       List[Any] )   ->    Union[str, Any]:







			'''simple docstring'''



			if (np.array(UpperCamelCase_ )).shape < (2, 2) or (np.array(UpperCamelCase_ )).shape < (2, 2):
						with pytest.raises(UpperCamelCase_ ):
									logger.info(F"""\n\t{test_subtraction.__name__} returned integer""" )
									matop.subtract(UpperCamelCase_  ,		UpperCamelCase_ )
			elif (np.array(UpperCamelCase_ )).shape == (np.array(UpperCamelCase_ )).shape:
						logger.info(F"""\n\t{test_subtraction.__name__} with same matrix dims""" )
						lowerCAmelCase__									=							(np.array(UpperCamelCase_ ) - np.array(UpperCamelCase_ )).tolist()
						lowerCAmelCase__									=							matop.subtract(UpperCamelCase_  ,		UpperCamelCase_ )
						assert theo == act
			else:
						with pytest.raises(UpperCamelCase_ ):
									logger.info(F"""\n\t{test_subtraction.__name__} with different matrix dims""" )
									assert matop.subtract(UpperCamelCase_  ,		UpperCamelCase_ )







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")  ,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)] )
def 					A       ( UpperCamelCase_   :       Union[str, Any]  ,		UpperCamelCase_   :       Optional[int] )   ->    Optional[int]:







			'''simple docstring'''



			if (np.array(UpperCamelCase_ )).shape < (2, 2) or (np.array(UpperCamelCase_ )).shape < (2, 2):
						logger.info(F"""\n\t{test_multiplication.__name__} returned integer""" )
						with pytest.raises(UpperCamelCase_ ):
									matop.add(UpperCamelCase_  ,		UpperCamelCase_ )
			elif (np.array(UpperCamelCase_ )).shape == (np.array(UpperCamelCase_ )).shape:
						logger.info(F"""\n\t{test_multiplication.__name__} meets dim requirements""" )
						lowerCAmelCase__									=							(np.matmul(UpperCamelCase_  ,		UpperCamelCase_ )).tolist()
						lowerCAmelCase__									=							matop.multiply(UpperCamelCase_  ,		UpperCamelCase_ )
						assert theo == act
			else:
						with pytest.raises(UpperCamelCase_ ):
									logger.info(
									    F"""\n\t{test_multiplication.__name__} does not meet dim requirements""" )
									assert matop.subtract(UpperCamelCase_  ,		UpperCamelCase_ )







@pytest.mark.mat_ops
def 					A       ( )   ->    str:







			'''simple docstring'''



			lowerCAmelCase__									=							(3.5 * np.array(UpperCamelCase_ )).tolist()
			lowerCAmelCase__									=							matop.scalar_multiply(UpperCamelCase_  ,		3.5 )
			assert theo == act







@pytest.mark.mat_ops
def 					A       ( )   ->    List[str]:







			'''simple docstring'''



			lowerCAmelCase__									=							(np.identity(5 )).tolist()
			lowerCAmelCase__									=							matop.identity(5 )
			assert theo == act







@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"  ,		[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f] )
def 					A       ( UpperCamelCase_   :       Union[str, Any] )   ->    List[Any]:







			'''simple docstring'''



			if (np.array(UpperCamelCase_ )).shape < (2, 2):
						with pytest.raises(UpperCamelCase_ ):
									logger.info(F"""\n\t{test_transpose.__name__} returned integer""" )
									matop.transpose(UpperCamelCase_ )
			else:
						lowerCAmelCase__									=							(np.transpose(UpperCamelCase_ )).tolist()
						lowerCAmelCase__									=							matop.transpose(UpperCamelCase_  ,		return_map=UpperCamelCase_ )
						assert theo == act







'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCAmelCase__				:	Tuple					=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Dict				       =       ['safety_checker', 'feature_extractor']





	def __init__(   self     :    Optional[Any] ,							__magic_name__     :    AutoencoderKL ,							__magic_name__     :    CLIPTextModel ,							__magic_name__     :    CLIPTokenizer ,							__magic_name__     :    UNetaDConditionModel ,							__magic_name__     :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,							__magic_name__     :    StableDiffusionSafetyChecker ,							__magic_name__     :    CLIPImageProcessor ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				super().__init__()
				if hasattr(scheduler.config ,							"steps_offset"							) and scheduler.config.steps_offset != 1:
							lowerCAmelCase__									=							(
							    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
							    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
							    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
							    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
							    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
							    " file"
							)
							deprecate("steps_offset!=1" ,							"1.0.0" ,							__magic_name__ ,							standard_warn=__magic_name__							)
							lowerCAmelCase__									=							dict(scheduler.config							)
							lowerCAmelCase__									=							1
							lowerCAmelCase__									=							FrozenDict(__magic_name__							)

				self.register_modules(
				    vae=__magic_name__ ,							text_encoder=__magic_name__ ,							tokenizer=__magic_name__ ,							unet=__magic_name__ ,							scheduler=__magic_name__ ,							safety_checker=__magic_name__ ,							feature_extractor=__magic_name__ ,							)
				self.register_to_config(requires_safety_checker=__magic_name__							)





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				return {k: getattr(self ,							__magic_name__							) for k in self.config.keys() if not k.startswith("_"							)}





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Optional[Union[str, int]] = "auto"							):


				"""simple docstring"""

				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							lowerCAmelCase__									=							self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				self.enable_attention_slicing(__magic_name__							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionInpaintPipelineLegacy(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							mask_image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							**__magic_name__     :    List[str] ,							):


				"""simple docstring"""

				return StableDiffusionImgaImgPipeline(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)







	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 50 ,							__magic_name__     :    float = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    float = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[torch.FloatTensor] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionPipeline(**self.components							)(
				    prompt=__magic_name__ ,							height=__magic_name__ ,							width=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							latents=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)





