






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    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        a__      (	lowerCAmelCase__	,       lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Optional[int] 					= StableDiffusionParadigmsPipeline
		UpperCAmelCase_							:       Tuple 					= TEXT_TO_IMAGE_PARAMS
		UpperCAmelCase_							:       Dict 					= TEXT_TO_IMAGE_BATCH_PARAMS
		UpperCAmelCase_							:       List[Any] 					= TEXT_TO_IMAGE_IMAGE_PARAMS
		UpperCAmelCase_							:       Tuple 					= TEXT_TO_IMAGE_IMAGE_PARAMS






		def 					_lowerCamelCase					(      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							, attention_head_dim=(2, 4)							, use_linear_projection=lowercase__							, )
				__A    =							DDIMParallelScheduler(
				    beta_start=0.0_0085							, beta_end=0.012							, beta_schedule="scaled_linear"							, clip_sample=lowercase__							, set_alpha_to_one=lowercase__							, )
				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							, sample_size=128							, )
				torch.manual_seed(0						)
				__A    =							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=1000							, hidden_act="gelu"							, projection_dim=512							, )
				__A    =							CLIPTextModel(lowercase__						)
				__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 					_lowerCamelCase					(      self							, lowercase__							, lowercase__=0						)				->				Union[str, Any]:
				if str(lowercase__						).startswith("mps"						):
						__A    =							torch.manual_seed(lowercase__						)
				else:
						__A    =							torch.Generator(device=lowercase__						).manual_seed(lowercase__						)
				__A    =							{
				    "prompt": "a photograph of an astronaut riding a horse",
				    "generator": generator,
				    "num_inference_steps": 10,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				    "parallel": 3,
				    "debug": True,
				}
				return inputs






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				__A    =							self.get_dummy_components()
				__A    =							StableDiffusionParadigmsPipeline(**lowercase__						)
				__A    =							sd_pipe.to(lowercase__						)
				sd_pipe.set_progress_bar_config(disable=lowercase__						)

				__A    =							self.get_dummy_inputs(lowercase__						)
				__A    =							sd_pipe(**lowercase__						).images
				__A    =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				__A    =							np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				__A    =							self.get_dummy_components()
				torch.manual_seed(0						)
				__A    =							DDPMParallelScheduler()
				torch.manual_seed(0						)
				__A    =							StableDiffusionParadigmsPipeline(**lowercase__						)
				__A    =							sd_pipe.to(lowercase__						)
				sd_pipe.set_progress_bar_config(disable=lowercase__						)

				__A    =							self.get_dummy_inputs(lowercase__						)
				__A    =							sd_pipe(**lowercase__						).images
				__A    =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				__A    =							np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				super().test_inference_batch_consistent(batch_sizes=[1, 2]						)






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				super().test_inference_batch_single_identical(batch_size=2							, expected_max_diff=3e-3						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				__A    =							self.get_dummy_components()
				__A    =							StableDiffusionParadigmsPipeline(**lowercase__						)
				__A    =							sd_pipe.to(lowercase__						)
				sd_pipe.set_progress_bar_config(disable=lowercase__						)

				__A    =							self.get_dummy_inputs(lowercase__						)
				__A    =							"french fries"
				__A    =							sd_pipe(**lowercase__							, negative_prompt=lowercase__						)
				__A    =							output.images
				__A    =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				__A    =							np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2





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






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






		def 					_lowerCamelCase					(      self							, lowercase__=0						)				->				Optional[Any]:
				__A    =							torch.Generator(device=lowercase__						).manual_seed(lowercase__						)
				__A    =							{
				    "prompt": "a photograph of an astronaut riding a horse",
				    "generator": generator,
				    "num_inference_steps": 10,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				    "parallel": 3,
				    "debug": True,
				}
				return inputs






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							"stabilityai/stable-diffusion-2-base"
				__A    =							DDIMParallelScheduler.from_pretrained(lowercase__							, subfolder="scheduler"						)
				__A    =							StableDiffusionParadigmsPipeline.from_pretrained(lowercase__							, scheduler=lowercase__							, safety_checker=lowercase__						)
				pipe.to(lowercase__						)
				pipe.set_progress_bar_config(disable=lowercase__						)
				pipe.enable_attention_slicing()

				__A    =							self.get_inputs()
				__A    =							pipe(**lowercase__						).images
				__A    =							image[0, -3:, -3:, -1].flatten()

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

				__A    =							np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]						)

				assert np.abs(expected_slice - image_slice						).max() < 1e-2









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_   :	Optional[Any]							      =logging.get_logger(__name__)

snake_case_   :	Any							      ={
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Union[str, Any] 					= 'swiftformer'






		def __init__(      self							, lowercase__=3							, lowercase__=[3, 3, 6, 4]							, lowercase__=[48, 56, 112, 220]							, lowercase__=4							, lowercase__=[True, True, True, True]							, lowercase__="gelu"							, lowercase__=3							, lowercase__=2							, lowercase__=1							, lowercase__=0.0							, lowercase__=True							, lowercase__=1e-5							, lowercase__=1e-5							, **lowercase__							, )				->				Optional[Any]:
				super().__init__(**lowercase__						)
				__A    =							num_channels
				__A    =							depths
				__A    =							embed_dims
				__A    =							mlp_ratio
				__A    =							downsamples
				__A    =							hidden_act
				__A    =							down_patch_size
				__A    =							down_stride
				__A    =							down_pad
				__A    =							drop_path_rate
				__A    =							use_layer_scale
				__A    =							layer_scale_init_value
				__A    =							batch_norm_eps





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Union[str, Any] 					= version.parse('1.11'		)






		@property
		def 					_lowerCamelCase					(      self						)				->				Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]						)






		@property
		def 					_lowerCamelCase					(      self						)				->				float:
				return 1e-4

