





'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 				__SCREAMING_SNAKE_CASE  (__a       ,							__a       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =StableDiffusionSAGPipeline
				__a            =TEXT_TO_IMAGE_PARAMS
				__a            =TEXT_TO_IMAGE_BATCH_PARAMS
				__a            =TEXT_TO_IMAGE_IMAGE_PARAMS
				__a            =TEXT_TO_IMAGE_IMAGE_PARAMS
				__a            =False




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								torch.manual_seed(0					)
								_a								= UNetaDConditionModel(
								    block_out_channels=(32, 64)					,				layers_per_block=2					,				sample_size=32					,				in_channels=4					,				out_channels=4					,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,				up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,				cross_attention_dim=32					,				)
								_a								= DDIMScheduler(
								    beta_start=0.00085					,				beta_end=0.012					,				beta_schedule="scaled_linear"					,				clip_sample=__a					,				set_alpha_to_one=__a					,				)
								torch.manual_seed(0					)
								_a								= AutoencoderKL(
								    block_out_channels=[32, 64]					,				in_channels=3					,				out_channels=3					,				down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,				up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,				latent_channels=4					,				)
								torch.manual_seed(0					)
								_a								= CLIPTextConfig(
								    bos_token_id=0					,				eos_token_id=2					,				hidden_size=32					,				intermediate_size=37					,				layer_norm_eps=1e-0_5					,				num_attention_heads=4					,				num_hidden_layers=5					,				pad_token_id=1					,				vocab_size=10_00					,				)
								_a								= CLIPTextModel(__a					)
								_a								= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

								_a								= {
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								return components




				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					Dict					,				__a     :					Optional[int]=0					):

								if str(__a					).startswith("mps"					):
												_a								= torch.manual_seed(__a					)
								else:
												_a								= torch.Generator(device=__a					).manual_seed(__a					)
								_a								= {
								    "prompt": ".",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "guidance_scale": 1.0,
								    "sag_scale": 1.0,
								    "output_type": "numpy",
								}
								return inputs




				def   UpperCamelCase__   (  self     :					List[str]					):
								super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


@slow
@require_torch_gpu
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Any					):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"					)
								_a								= sag_pipe.to(__a					)
								sag_pipe.set_progress_bar_config(disable=__a					)

								_a								= "."
								_a								= torch.manual_seed(0					)
								_a								= sag_pipe(
								    [prompt]					,				generator=__a					,				guidance_scale=7.5					,				sag_scale=1.0					,				num_inference_steps=20					,				output_type="np"					)

								_a								= output.images

								_a								= image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_12, 5_12, 3)
								_a								= np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-2




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"					)
								_a								= sag_pipe.to(__a					)
								sag_pipe.set_progress_bar_config(disable=__a					)

								_a								= "."
								_a								= torch.manual_seed(0					)
								_a								= sag_pipe(
								    [prompt]					,				generator=__a					,				guidance_scale=7.5					,				sag_scale=1.0					,				num_inference_steps=20					,				output_type="np"					)

								_a								= output.images

								_a								= image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_12, 5_12, 3)
								_a								= np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-2




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"					)
								_a								= sag_pipe.to(__a					)
								sag_pipe.set_progress_bar_config(disable=__a					)

								_a								= "."
								_a								= torch.manual_seed(0					)
								_a								= sag_pipe(
								    [prompt]					,				width=7_68					,				height=5_12					,				generator=__a					,				guidance_scale=7.5					,				sag_scale=1.0					,				num_inference_steps=20					,				output_type="np"					,				)

								_a								= output.images

								assert image.shape == (1, 5_12, 7_68, 3)






'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			_a  (	__a ):
       __a				:     int   				=					["""image_processor""", """tokenizer"""]
       __a				:     Union[str, Any]   				=					"""ChineseCLIPImageProcessor"""
       __a				:     List[Any]   				=					("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(	self						:	Dict   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Dict=None   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''   ,				lowercase   ,				)
               UpperCAmelCase			       =		kwargs.pop('''feature_extractor'''				)

           UpperCAmelCase			       =		image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.'''				)
           if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.'''				)

           super().__init__(lowercase   ,				lowercase				)
           UpperCAmelCase			       =		self.image_processor
       def __call__(	self						:	Tuple   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	int=None   ,				**lowercase						:	Dict				):



           '''simple docstring'''

           if text is None and images is None:
               raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

           if text is not None:
               UpperCAmelCase			       =		self.tokenizer(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if images is not None:
               UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if text is not None and images is not None:
               UpperCAmelCase			       =		image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowercase				)   ,				tensor_type=lowercase				)
       def     A      (	self						:	int   ,				*lowercase						:	Tuple   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[Any]   ,				*lowercase						:	int   ,				**lowercase						:	Optional[int]				):



           '''simple docstring'''

           return self.tokenizer.decode(*lowercase   ,				**lowercase				)
       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.tokenizer.model_input_names
           UpperCAmelCase			       =		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







       @property
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           warnings.warn(
               '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,				lowercase   ,				)
           return self.image_processor_class




