

"""simple docstring"""

import unittest

import numpy as np

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

if is_vision_available():
							from PIL import Image

							from transformers import ViltImageProcessor


class 			_snake_case     (						unittest.TestCase       ):







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=7     ,							UpperCAmelCase_=3     ,							UpperCAmelCase_=1_8     ,							UpperCAmelCase_=3_0     ,							UpperCAmelCase_=4_0_0     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=None     ,							UpperCAmelCase_=2     ,							UpperCAmelCase_=True     ,							UpperCAmelCase_=[0.5, 0.5, 0.5]     ,							UpperCAmelCase_=[0.5, 0.5, 0.5]     ,							)			->						Tuple:
											__lowercase												=			size if size is not None else {'''shortest_edge''': 3_0}
											__lowercase												=			parent
											__lowercase												=			batch_size
											__lowercase												=			num_channels
											__lowercase												=			image_size
											__lowercase												=			min_resolution
											__lowercase												=			max_resolution
											__lowercase												=			do_resize
											__lowercase												=			size
											__lowercase												=			size_divisor
											__lowercase												=			do_normalize
											__lowercase												=			image_mean
											__lowercase												=			image_std







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "size_divisor": self.size_divisor,
											}







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=False							)			->						Optional[int]:
											if not batched:
																__lowercase												=			self.size['''shortest_edge''']
																__lowercase												=			image_inputs[0]
																if isinstance(UpperCAmelCase_     ,							Image.Image							):
																					__lowercase						, __lowercase												=			image.size
																else:
																					__lowercase						, __lowercase												=			image.shape[1], image.shape[2]
																__lowercase												=			size / min(UpperCAmelCase_     ,							UpperCAmelCase_							)
																if h < w:
																					__lowercase						, __lowercase												=			size, scale * w
																else:
																					__lowercase						, __lowercase												=			scale * h, size

																__lowercase												=			int((1_3_3_3 / 8_0_0) * size							)
																if max(UpperCAmelCase_     ,							UpperCAmelCase_							) > max_size:
																					__lowercase												=			max_size / max(UpperCAmelCase_     ,							UpperCAmelCase_							)
																					__lowercase												=			newh * scale
																					__lowercase												=			neww * scale

																__lowercase						, __lowercase												=			int(newh + 0.5							), int(neww + 0.5							)
																__lowercase						, __lowercase												=			(
																    newh // self.size_divisor * self.size_divisor,
																    neww // self.size_divisor * self.size_divisor,
																)

											else:
																__lowercase												=			[]
																for image in image_inputs:
																					__lowercase						, __lowercase												=			self.get_expected_values([image]							)
																					expected_values.append((expected_height, expected_width)							)
																__lowercase												=			max(UpperCAmelCase_     ,							key=lambda UpperCAmelCase_							: item[0]							)[0]
																__lowercase												=			max(UpperCAmelCase_     ,							key=lambda UpperCAmelCase_							: item[1]							)[1]

											return expected_height, expected_width



@require_torch
@require_vision
class 			_snake_case     (						UpperCamelCase						,			unittest.TestCase       ):
						__snake_case 			=     ViltImageProcessor if is_vision_available() else None







						def        _UpperCamelCase    (self							)			->						int:
											__lowercase												=			ViltImageProcessingTester(self							)







						@property
						def        _UpperCamelCase    (self							)			->						List[str]:
											return self.image_processor_tester.prepare_image_processor_dict()







						def        _UpperCamelCase    (self							)			->						Any:
											__lowercase												=			self.image_processing_class(**self.image_processor_dict							)
											self.assertTrue(hasattr(UpperCAmelCase_     ,							'''image_mean'''							)							)
											self.assertTrue(hasattr(UpperCAmelCase_     ,							'''image_std'''							)							)
											self.assertTrue(hasattr(UpperCAmelCase_     ,							'''do_normalize'''							)							)
											self.assertTrue(hasattr(UpperCAmelCase_     ,							'''do_resize'''							)							)
											self.assertTrue(hasattr(UpperCAmelCase_     ,							'''size'''							)							)
											self.assertTrue(hasattr(UpperCAmelCase_     ,							'''size_divisor'''							)							)







						def        _UpperCamelCase    (self							)			->						List[str]:
											__lowercase												=			self.image_processing_class.from_dict(self.image_processor_dict							)
											self.assertEqual(image_processor.size     ,							{'''shortest_edge''': 3_0}							)

											__lowercase												=			self.image_processing_class.from_dict(self.image_processor_dict     ,							size=4_2							)
											self.assertEqual(image_processor.size     ,							{'''shortest_edge''': 4_2}							)







						def        _UpperCamelCase    (self							)			->						List[str]:
											pass







						def        _UpperCamelCase    (self							)			->						Tuple:
											# Initialize image_processing
											__lowercase												=			self.image_processing_class(**self.image_processor_dict							)
											# create random PIL images
											__lowercase												=			prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=UpperCAmelCase_							)
											for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_     ,							Image.Image							)

											# Test not batched input
											__lowercase												=			image_processing(image_inputs[0]     ,							return_tensors='''pt'''							).pixel_values

											__lowercase						, __lowercase												=			self.image_processor_tester.get_expected_values(UpperCAmelCase_							)
											self.assertEqual(
											    encoded_images.shape     ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,							)

											# Test batched
											__lowercase												=			image_processing(UpperCAmelCase_     ,							return_tensors='''pt'''							).pixel_values

											__lowercase						, __lowercase												=			self.image_processor_tester.get_expected_values(UpperCAmelCase_     ,							batched=UpperCAmelCase_							)
											self.assertEqual(
											    encoded_images.shape     ,							(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )     ,							)







						def        _UpperCamelCase    (self							)			->						Tuple:
											# Initialize image_processing
											__lowercase												=			self.image_processing_class(**self.image_processor_dict							)
											# create random numpy tensors
											__lowercase												=			prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=UpperCAmelCase_     ,							numpify=UpperCAmelCase_							)
											for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_     ,							np.ndarray							)

											# Test not batched input
											__lowercase												=			image_processing(image_inputs[0]     ,							return_tensors='''pt'''							).pixel_values

											__lowercase						, __lowercase												=			self.image_processor_tester.get_expected_values(UpperCAmelCase_							)
											self.assertEqual(
											    encoded_images.shape     ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,							)

											# Test batched
											__lowercase												=			image_processing(UpperCAmelCase_     ,							return_tensors='''pt'''							).pixel_values

											__lowercase						, __lowercase												=			self.image_processor_tester.get_expected_values(UpperCAmelCase_     ,							batched=UpperCAmelCase_							)
											self.assertEqual(
											    encoded_images.shape     ,							(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )     ,							)







						def        _UpperCamelCase    (self							)			->						Tuple:
											# Initialize image_processing
											__lowercase												=			self.image_processing_class(**self.image_processor_dict							)
											# create random PyTorch tensors
											__lowercase												=			prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=UpperCAmelCase_     ,							torchify=UpperCAmelCase_							)
											for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase_     ,							torch.Tensor							)

											# Test not batched input
											__lowercase												=			image_processing(image_inputs[0]     ,							return_tensors='''pt'''							).pixel_values

											__lowercase						, __lowercase												=			self.image_processor_tester.get_expected_values(UpperCAmelCase_							)
											self.assertEqual(
											    encoded_images.shape     ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,							)

											# Test batched
											__lowercase												=			image_processing(UpperCAmelCase_     ,							return_tensors='''pt'''							).pixel_values

											__lowercase						, __lowercase												=			self.image_processor_tester.get_expected_values(UpperCAmelCase_     ,							batched=UpperCAmelCase_							)
											self.assertEqual(
											    encoded_images.shape     ,							(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )     ,							)





"""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


a__ :	int						= logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     ['safety_checker', 'feature_extractor']







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = True     ,							)			->						Optional[int]:
											super().__init__()
											if hasattr(scheduler.config     ,							'''steps_offset'''							) and scheduler.config.steps_offset != 1:
																__lowercase												=			(
																    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'''     ,							UpperCAmelCase_     ,							standard_warn=UpperCAmelCase_							)
																__lowercase												=			dict(scheduler.config							)
																__lowercase												=			1
																__lowercase												=			FrozenDict(UpperCAmelCase_							)

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







						@property
						def        _UpperCamelCase    (self							)			->						Dict[str, Any]:
											return {k: getattr(self     ,							UpperCAmelCase_							) for k in self.config.keys() if not k.startswith('''_'''							)}







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_ = "auto"							)			->						str:
											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__lowercase												=			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						int:
											self.enable_attention_slicing(UpperCAmelCase_							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						str:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionInpaintPipelineLegacy(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							mask_image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							**UpperCAmelCase_     ,							)			->						List[str]:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionImgaImgPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						Optional[int]:
											# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
											return StableDiffusionPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							height=UpperCAmelCase_     ,							width=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							latents=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)


