



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	List[Any]    =['image_processor', 'tokenizer']
							__lowerCamelCase	:	Dict    ='AutoImageProcessor'
							__lowerCamelCase	:	Union[str, Any]    ='AutoTokenizer'





							def __init__(   self		:		Optional[int]      ,				__lowercase		:		Dict=None      ,				__lowercase		:		Dict=None      ,				**__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		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      ,				)
															__a       =		kwargs.pop("""feature_extractor"""	)

											__a       =		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	)
											__a       =		self.image_processor
											__a       =		False





							def __call__(   self		:		int      ,				*__lowercase		:		List[Any]      ,				**__lowercase		:		List[str]	):

											'''simple docstring'''
											# For backward compatibility
											if self._in_target_context_manager:
															return self.current_processor(*__lowercase      ,				**__lowercase	)

											__a       =		kwargs.pop("""images"""      ,				__lowercase	)
											__a       =		kwargs.pop("""text"""      ,				__lowercase	)
											if len(__lowercase	) > 0:
															__a       =		args[0]
															__a       =		args[1:]

											if images is None and text is None:
															raise ValueError("""You need to specify either an `images` or `text` input to process."""	)

											if images is not None:
															__a       =		self.image_processor(__lowercase      ,				*__lowercase      ,				**__lowercase	)
											if text is not None:
															__a       =		self.tokenizer(__lowercase      ,				**__lowercase	)

											if text is None:
															return inputs
											elif images is None:
															return encodings
											else:
															__a       =		encodings["""input_ids"""]
															return inputs





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

											'''simple docstring'''
											return self.tokenizer.batch_decode(*__lowercase      ,				**__lowercase	)





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

											'''simple docstring'''
											return self.tokenizer.decode(*__lowercase      ,				**__lowercase	)





							@contextmanager
							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											warnings.warn(
											    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
											    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
											    """your images inputs, or in a separate call."""	)
											__a       =		True
											__a       =		self.tokenizer
											yield
											__a       =		self.image_processor
											__a       =		False





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		List[Any]      ,				__lowercase		:		List[str]=False      ,				__lowercase		:		int=None	):

											'''simple docstring'''
											if added_vocab is None:
															__a       =		self.tokenizer.get_added_vocab()

											__a       =		{}

											while tokens:
															__a       =		re.search(r"""<s_(.*?)>"""      ,				__lowercase      ,				re.IGNORECASE	)
															if start_token is None:
																			break
															__a       =		start_token.group(1	)
															__a       =		re.search(rF"</s_{key}>"      ,				__lowercase      ,				re.IGNORECASE	)
															__a       =		start_token.group()
															if end_token is None:
																			__a       =		tokens.replace(__lowercase      ,				""""""	)
															else:
																			__a       =		end_token.group()
																			__a       =		re.escape(__lowercase	)
																			__a       =		re.escape(__lowercase	)
																			__a       =		re.search(F"{start_token_escaped}(.*?){end_token_escaped}"      ,				__lowercase      ,				re.IGNORECASE	)
																			if content is not None:
																							__a       =		content.group(1	).strip()
																							if r"<s_" in content and r"</s_" in content:  # non-leaf node
																											__a       =		self.tokenajson(__lowercase      ,				is_inner_value=__lowercase      ,				added_vocab=__lowercase	)
																											if value:
																															if len(__lowercase	) == 1:
																																			__a       =		value[0]
																															__a       =		value
																							else:  # leaf nodes
																											__a       =		[]
																											for leaf in content.split(r"""<sep/>"""	):
																															__a       =		leaf.strip()
																															if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																			__a       =		leaf[1:-2]  # for categorical special tokens
																															output[key].append(__lowercase	)
																											if len(output[key]	) == 1:
																															__a       =		output[key][0]

																			__a       =		tokens[tokens.find(__lowercase	) + len(__lowercase	) :].strip()
																			if tokens[:6] == r"<sep/>":  # non-leaf nodes
																							return [output] + self.tokenajson(tokens[6:]      ,				is_inner_value=__lowercase      ,				added_vocab=__lowercase	)

											if len(__lowercase	):
															return [output] if is_inner_value else output
											else:
															return [] if is_inner_value else {"text_sequence": tokens}





							@property
							def 						UpperCamelCase_					(   self		:		str	):

											'''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







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

											'''simple docstring'''
											warnings.warn(
											    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,				__lowercase      ,				)
											return self.image_processor





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
