



import warnings
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


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):

							'''simple docstring'''





							__lowerCamelCase	:	Dict    ='segformer'





							def __init__(   self		:		str      ,				__lowercase		:		Optional[int]=3      ,				__lowercase		:		int=4      ,				__lowercase		:		str=[2, 2, 2, 2]      ,				__lowercase		:		List[Any]=[8, 4, 2, 1]      ,				__lowercase		:		Optional[int]=[32, 64, 160, 256]      ,				__lowercase		:		Tuple=[7, 3, 3, 3]      ,				__lowercase		:		int=[4, 2, 2, 2]      ,				__lowercase		:		Optional[int]=[1, 2, 5, 8]      ,				__lowercase		:		Optional[int]=[4, 4, 4, 4]      ,				__lowercase		:		Any="gelu"      ,				__lowercase		:		Optional[Any]=0.0      ,				__lowercase		:		int=0.0      ,				__lowercase		:		int=0.1      ,				__lowercase		:		Union[str, Any]=0.02      ,				__lowercase		:		Union[str, Any]=0.1      ,				__lowercase		:		Dict=1E-6      ,				__lowercase		:		int=256      ,				__lowercase		:		List[Any]=255      ,				**__lowercase		:		Dict      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)

											if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
															warnings.warn(
															    """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
															    """ removed, as the behaviour will default to that of reshape_last_stage = True."""      ,				__lowercase      ,				)

											__a       =		num_channels
											__a       =		num_encoder_blocks
											__a       =		depths
											__a       =		sr_ratios
											__a       =		hidden_sizes
											__a       =		patch_sizes
											__a       =		strides
											__a       =		mlp_ratios
											__a       =		num_attention_heads
											__a       =		hidden_act
											__a       =		hidden_dropout_prob
											__a       =		attention_probs_dropout_prob
											__a       =		classifier_dropout_prob
											__a       =		initializer_range
											__a       =		drop_path_rate
											__a       =		layer_norm_eps
											__a       =		decoder_hidden_size
											__a       =		kwargs.get("""reshape_last_stage"""      ,				__lowercase	)
											__a       =		semantic_loss_ignore_index






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):

							'''simple docstring'''





							__lowerCamelCase	:	Optional[int]    =version.parse('1.11'					)





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

											'''simple docstring'''
											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ]	)





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

											'''simple docstring'''
											return 1E-4







							@property
							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											return 12





import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





							@property
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		1
											__a       =		3
											__a       =		(32, 32)

											__a       =		floats_tensor((batch_size, num_channels) + sizes      ,				rng=random.Random(0	)	).to(__lowercase	)
											return image





							@property
							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											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      ,				)
											return model





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

											'''simple docstring'''
											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      ,				)
											return model





							@property
							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											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      ,				)
											return CLIPTextModel(__lowercase	)





							@property
							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											def extract(*__lowercase		:		Tuple      ,				**__lowercase		:		Dict	):






															class    SCREAMING_SNAKE_CASE			:





																						def __init__(   self		:		List[str]	):

																										'''simple docstring'''
																										__a       =		torch.ones([0]	)







																						def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		str	):

																										'''simple docstring'''
																										self.pixel_values.to(__lowercase	)
																										return self



															return Out()

											return extract





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											__a       =		self.dummy_cond_unet
											__a       =		DDIMScheduler(
											    beta_start=0.00085      ,				beta_end=0.012      ,				beta_schedule="""scaled_linear"""      ,				clip_sample=__lowercase      ,				set_alpha_to_one=__lowercase      ,				)

											__a       =		self.dummy_vae
											__a       =		self.dummy_text_encoder
											__a       =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

											# make sure here that pndm scheduler skips prk
											__a       =		StableDiffusionPipeline(
											    unet=__lowercase      ,				scheduler=__lowercase      ,				vae=__lowercase      ,				text_encoder=__lowercase      ,				tokenizer=__lowercase      ,				safety_checker=__lowercase      ,				feature_extractor=self.dummy_extractor      ,				)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		"""A painting of a squirrel eating a burger"""

											__a       =		torch.Generator(device=__lowercase	).manual_seed(0	)
											__a       =		sd_pipe([prompt]      ,				generator=__lowercase      ,				guidance_scale=6.0      ,				num_inference_steps=2      ,				output_type="""np"""	)
											__a       =		output.images

											__a       =		torch.Generator(device=__lowercase	).manual_seed(0	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=6.0      ,				num_inference_steps=2      ,				output_type="""np"""      ,				return_dict=__lowercase      ,				)[0]

											__a       =		image[0, -3:, -3:, -1]
											__a       =		image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											__a       =		np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]	)

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





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											__a       =		self.dummy_cond_unet
											__a       =		PNDMScheduler(skip_prk_steps=__lowercase	)
											__a       =		self.dummy_vae
											__a       =		self.dummy_text_encoder
											__a       =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

											# make sure here that pndm scheduler skips prk
											__a       =		StableDiffusionPipeline(
											    unet=__lowercase      ,				scheduler=__lowercase      ,				vae=__lowercase      ,				text_encoder=__lowercase      ,				tokenizer=__lowercase      ,				safety_checker=__lowercase      ,				feature_extractor=self.dummy_extractor      ,				)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		"""A painting of a squirrel eating a burger"""
											__a       =		torch.Generator(device=__lowercase	).manual_seed(0	)
											__a       =		sd_pipe([prompt]      ,				generator=__lowercase      ,				guidance_scale=6.0      ,				num_inference_steps=2      ,				output_type="""np"""	)

											__a       =		output.images

											__a       =		torch.Generator(device=__lowercase	).manual_seed(0	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=6.0      ,				num_inference_steps=2      ,				output_type="""np"""      ,				return_dict=__lowercase      ,				)[0]

											__a       =		image[0, -3:, -3:, -1]
											__a       =		image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											__a       =		np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]	)

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





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		StableDiffusionPipeline.from_pretrained(
											    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""      ,				safety_checker=__lowercase	)
											assert isinstance(__lowercase      ,				__lowercase	)
											assert isinstance(pipe.scheduler      ,				__lowercase	)
											assert pipe.safety_checker is None

											__a       =		pipe("""example prompt"""      ,				num_inference_steps=2	).images[0]
											assert image is not None

											# check that there's no error when saving a pipeline with one of the models being None
											with tempfile.TemporaryDirectory() as tmpdirname:
															pipe.save_pretrained(__lowercase	)
															__a       =		StableDiffusionPipeline.from_pretrained(__lowercase	)

											# sanity check that the pipeline still works
											assert pipe.safety_checker is None
											__a       =		pipe("""example prompt"""      ,				num_inference_steps=2	).images[0]
											assert image is not None







							@unittest.skipIf(torch_device != """cuda"""      ,				"""This test requires a GPU"""	)
							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		self.dummy_cond_unet
											__a       =		PNDMScheduler(skip_prk_steps=__lowercase	)
											__a       =		self.dummy_vae
											__a       =		self.dummy_text_encoder
											__a       =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

											# put models in fp16
											__a       =		unet.half()
											__a       =		vae.half()
											__a       =		bert.half()

											# make sure here that pndm scheduler skips prk
											__a       =		StableDiffusionPipeline(
											    unet=__lowercase      ,				scheduler=__lowercase      ,				vae=__lowercase      ,				text_encoder=__lowercase      ,				tokenizer=__lowercase      ,				safety_checker=__lowercase      ,				feature_extractor=self.dummy_extractor      ,				)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		"""A painting of a squirrel eating a burger"""
											__a       =		sd_pipe([prompt]      ,				num_inference_steps=2      ,				output_type="""np"""	).images

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






@nightly
@require_torch_gpu
class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""      ,				safety_checker=__lowercase	)
											__a       =		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		(
											    """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
											    """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
											    """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
											    """ children from bahnhof zoo, detailed """
											)
											__a       =		4003660346
											__a       =		7

											# without safety guidance (sld_guidance_scale = 0)
											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=0      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

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

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

											# without safety guidance (strong configuration)
											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=2000      ,				sld_warmup_steps=7      ,				sld_threshold=0.025      ,				sld_momentum_scale=0.5      ,				sld_mom_beta=0.7      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

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

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





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""      ,				safety_checker=__lowercase	)
											__a       =		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		"""padme amidala taking a bath artwork, safe for work, no nudity"""
											__a       =		2734971755
											__a       =		7

											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=0      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

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

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

											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=2000      ,				sld_warmup_steps=7      ,				sld_threshold=0.025      ,				sld_momentum_scale=0.5      ,				sld_mom_beta=0.7      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

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

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







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		(
											    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
											    """ leyendecker"""
											)
											__a       =		1044355234
											__a       =		12

											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=0      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	)

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

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-7

											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=2000      ,				sld_warmup_steps=7      ,				sld_threshold=0.025      ,				sld_momentum_scale=0.5      ,				sld_mom_beta=0.7      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]	)
											assert image.shape == (1, 512, 512, 3)

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