

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 						__UpperCamelCase    (					lowercase     ,     lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     StableDiffusionSAGPipeline
	SCREAMING_SNAKE_CASE__    				=     TEXT_TO_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE__    				=     TEXT_TO_IMAGE_BATCH_PARAMS
	SCREAMING_SNAKE_CASE__    				=     TEXT_TO_IMAGE_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE__    				=     TEXT_TO_IMAGE_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE__    				=     False




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		torch.manual_seed(0							)
		UpperCAmelCase_       				=			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		,	)
		UpperCAmelCase_       				=			DDIMScheduler(
		    beta_start=0.00_085		,	beta_end=0.012		,	beta_schedule="scaled_linear"		,	clip_sample=lowerCAmelCase		,	set_alpha_to_one=lowerCAmelCase		,	)
		torch.manual_seed(0							)
		UpperCAmelCase_       				=			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							)
		UpperCAmelCase_       				=			CLIPTextConfig(
		    bos_token_id=0		,	eos_token_id=2		,	hidden_size=32		,	intermediate_size=37		,	layer_norm_eps=1e-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=1_000		,	)
		UpperCAmelCase_       				=			CLIPTextModel(lowerCAmelCase							)
		UpperCAmelCase_       				=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

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




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						List[Any]=0							):


		'''simple docstring'''







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




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		super().test_inference_batch_single_identical(expected_max_diff=3e-3							)


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




	def       __A					(		self	:						int							):


		'''simple docstring'''







		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"							)
		UpperCAmelCase_       				=			sag_pipe.to(lowerCAmelCase							)
		sag_pipe.set_progress_bar_config(disable=lowerCAmelCase							)

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

		UpperCAmelCase_       				=			output.images

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

		assert image.shape == (1, 512, 512, 3)
		UpperCAmelCase_       				=			np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]							)

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




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
		UpperCAmelCase_       				=			sag_pipe.to(lowerCAmelCase							)
		sag_pipe.set_progress_bar_config(disable=lowerCAmelCase							)

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

		UpperCAmelCase_       				=			output.images

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

		assert image.shape == (1, 512, 512, 3)
		UpperCAmelCase_       				=			np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]							)

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




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
		UpperCAmelCase_       				=			sag_pipe.to(lowerCAmelCase							)
		sag_pipe.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			"."
		UpperCAmelCase_       				=			torch.manual_seed(0							)
		UpperCAmelCase_       				=			sag_pipe(
		    [prompt]		,	width=768		,	height=512		,	generator=lowerCAmelCase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type="np"		,	)

		UpperCAmelCase_       				=			output.images

		assert image.shape == (1, 512, 768, 3)


from .imports import is_tqdm_available


if is_tqdm_available():
				from tqdm.auto import tqdm as _tqdm

from ..state import PartialState



def       __lowerCAmelCase      (						A = True       ,     *A       ,     **A    ):
	if not is_tqdm_available():
		raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."    )
	UpperCAmelCase_       				=			False
	if main_process_only:
		UpperCAmelCase_       				=			PartialState().local_process_index == 0
	return _tqdm(*A       ,     **A       ,     disable=A    )