


'''simple docstring'''
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      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				1
							UpperCamelCase__     :Any							      =				3
							UpperCamelCase__     :List[str]							      =				(32, 32)

							UpperCamelCase__     :List[str]							      =				floats_tensor((batch_size, num_channels) + sizes					,     rng=random.Random(0							)							).to(UpperCamelCase_							)
							return image




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__     :int							      =				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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__     :Dict							      =				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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__     :Optional[Any]							      =				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(UpperCamelCase_							)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							def extract(*UpperCamelCase_					,     **UpperCamelCase_							):
												class      lowercase			:

														"""simple docstring"""

														def __init__(	self							):

																			'''simple docstring'''


																			UpperCamelCase__     :List[str]							      =				torch.ones([0]							)




														def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

																			'''simple docstring'''


																			self.pixel_values.to(UpperCamelCase_							)
																			return self



												return Out()

							return extract




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :int							      =				self.dummy_cond_unet
							UpperCamelCase__     :Union[str, Any]							      =				DDIMScheduler(
							    beta_start=0.00085					,     beta_end=0.012					,     beta_schedule='''scaled_linear'''					,     clip_sample=UpperCamelCase_					,     set_alpha_to_one=UpperCamelCase_					,     )

							UpperCamelCase__     :Union[str, Any]							      =				self.dummy_vae
							UpperCamelCase__     :List[str]							      =				self.dummy_text_encoder
							UpperCamelCase__     :str							      =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__     :str							      =				StableDiffusionPipeline(
							    unet=UpperCamelCase_					,     scheduler=UpperCamelCase_					,     vae=UpperCamelCase_					,     text_encoder=UpperCamelCase_					,     tokenizer=UpperCamelCase_					,     safety_checker=UpperCamelCase_					,     feature_extractor=self.dummy_extractor					,     )
							UpperCamelCase__     :List[str]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Dict							      =				'''A painting of a squirrel eating a burger'''

							UpperCamelCase__     :Any							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__     :List[Any]							      =				sd_pipe([prompt]					,     generator=UpperCamelCase_					,     guidance_scale=6.0					,     num_inference_steps=2					,     output_type='''np'''							)
							UpperCamelCase__     :Union[str, Any]							      =				output.images

							UpperCamelCase__     :Union[str, Any]							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__     :str							      =				sd_pipe(
							    [prompt]					,     generator=UpperCamelCase_					,     guidance_scale=6.0					,     num_inference_steps=2					,     output_type='''np'''					,     return_dict=UpperCamelCase_					,     )[0]

							UpperCamelCase__     :Union[str, Any]							      =				image[0, -3:, -3:, -1]
							UpperCamelCase__     :Optional[int]							      =				image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__     :List[str]							      =				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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :str							      =				self.dummy_cond_unet
							UpperCamelCase__     :List[Any]							      =				PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				self.dummy_vae
							UpperCamelCase__     :Optional[Any]							      =				self.dummy_text_encoder
							UpperCamelCase__     :List[str]							      =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__     :Tuple							      =				StableDiffusionPipeline(
							    unet=UpperCamelCase_					,     scheduler=UpperCamelCase_					,     vae=UpperCamelCase_					,     text_encoder=UpperCamelCase_					,     tokenizer=UpperCamelCase_					,     safety_checker=UpperCamelCase_					,     feature_extractor=self.dummy_extractor					,     )
							UpperCamelCase__     :Any							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Tuple							      =				'''A painting of a squirrel eating a burger'''
							UpperCamelCase__     :Tuple							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__     :Any							      =				sd_pipe([prompt]					,     generator=UpperCamelCase_					,     guidance_scale=6.0					,     num_inference_steps=2					,     output_type='''np'''							)

							UpperCamelCase__     :Optional[Any]							      =				output.images

							UpperCamelCase__     :Any							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__     :int							      =				sd_pipe(
							    [prompt]					,     generator=UpperCamelCase_					,     guidance_scale=6.0					,     num_inference_steps=2					,     output_type='''np'''					,     return_dict=UpperCamelCase_					,     )[0]

							UpperCamelCase__     :Any							      =				image[0, -3:, -3:, -1]
							UpperCamelCase__     :List[Any]							      =				image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__     :Optional[int]							      =				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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				StableDiffusionPipeline.from_pretrained(
							    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''					,     safety_checker=UpperCamelCase_							)
							assert isinstance(UpperCamelCase_					,     UpperCamelCase_							)
							assert isinstance(pipe.scheduler					,     UpperCamelCase_							)
							assert pipe.safety_checker is None

							UpperCamelCase__     :Optional[int]							      =				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(UpperCamelCase_							)
												UpperCamelCase__     :Optional[int]							      =				StableDiffusionPipeline.from_pretrained(UpperCamelCase_							)

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							UpperCamelCase__     :Union[str, Any]							      =				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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.dummy_cond_unet
							UpperCamelCase__     :Tuple							      =				PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							UpperCamelCase__     :int							      =				self.dummy_vae
							UpperCamelCase__     :Union[str, Any]							      =				self.dummy_text_encoder
							UpperCamelCase__     :int							      =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							# put models in fp16
							UpperCamelCase__     :Optional[int]							      =				unet.half()
							UpperCamelCase__     :Union[str, Any]							      =				vae.half()
							UpperCamelCase__     :Dict							      =				bert.half()

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__     :int							      =				StableDiffusionPipeline(
							    unet=UpperCamelCase_					,     scheduler=UpperCamelCase_					,     vae=UpperCamelCase_					,     text_encoder=UpperCamelCase_					,     tokenizer=UpperCamelCase_					,     safety_checker=UpperCamelCase_					,     feature_extractor=self.dummy_extractor					,     )
							UpperCamelCase__     :Optional[Any]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Any							      =				'''A painting of a squirrel eating a burger'''
							UpperCamelCase__     :Dict							      =				sd_pipe([prompt]					,     num_inference_steps=2					,     output_type='''np'''							).images

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



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

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,     safety_checker=UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
							UpperCamelCase__     :Any							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				(
							    '''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 '''
							)
							UpperCamelCase__     :List[str]							      =				4003660346
							UpperCamelCase__     :List[Any]							      =				7

							# without safety guidance (sld_guidance_scale = 0)
							UpperCamelCase__     :int							      =				torch.manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				sd_pipe(
							    [prompt]					,     generator=UpperCamelCase_					,     guidance_scale=UpperCamelCase_					,     num_inference_steps=50					,     output_type='''np'''					,     width=512					,     height=512					,     sld_guidance_scale=0					,     )

							UpperCamelCase__     :Tuple							      =				output.images
							UpperCamelCase__     :Optional[int]							      =				image[0, -3:, -3:, -1]
							UpperCamelCase__     :Optional[int]							      =				[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)
							UpperCamelCase__     :int							      =				torch.manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				sd_pipe(
							    [prompt]					,     generator=UpperCamelCase_					,     guidance_scale=UpperCamelCase_					,     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					,     )

							UpperCamelCase__     :Union[str, Any]							      =				output.images
							UpperCamelCase__     :List[Any]							      =				image[0, -3:, -3:, -1]
							UpperCamelCase__     :Dict							      =				[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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,     safety_checker=UpperCamelCase_							)
							UpperCamelCase__     :int							      =				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
							UpperCamelCase__     :Dict							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				'''padme amidala taking a bath artwork, safe for work, no nudity'''
							UpperCamelCase__     :List[Any]							      =				2734971755
							UpperCamelCase__     :str							      =				7

							UpperCamelCase__     :List[Any]							      =				torch.manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				sd_pipe(
							    [prompt]					,     generator=UpperCamelCase_					,     guidance_scale=UpperCamelCase_					,     num_inference_steps=50					,     output_type='''np'''					,     width=512					,     height=512					,     sld_guidance_scale=0					,     )

							UpperCamelCase__     :List[str]							      =				output.images
							UpperCamelCase__     :int							      =				image[0, -3:, -3:, -1]
							UpperCamelCase__     :Optional[Any]							      =				[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

							UpperCamelCase__     :str							      =				torch.manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				sd_pipe(
							    [prompt]					,     generator=UpperCamelCase_					,     guidance_scale=UpperCamelCase_					,     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					,     )

							UpperCamelCase__     :Any							      =				output.images
							UpperCamelCase__     :Optional[int]							      =				image[0, -3:, -3:, -1]
							UpperCamelCase__     :str							      =				[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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
							UpperCamelCase__     :List[str]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Dict							      =				(
							    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
							    ''' leyendecker'''
							)
							UpperCamelCase__     :List[str]							      =				1044355234
							UpperCamelCase__     :Dict							      =				12

							UpperCamelCase__     :Tuple							      =				torch.manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				sd_pipe(
							    [prompt]					,     generator=UpperCamelCase_					,     guidance_scale=UpperCamelCase_					,     num_inference_steps=50					,     output_type='''np'''					,     width=512					,     height=512					,     sld_guidance_scale=0					,     )

							UpperCamelCase__     :List[str]							      =				output.images
							UpperCamelCase__     :List[str]							      =				image[0, -3:, -3:, -1]
							UpperCamelCase__     :List[Any]							      =				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

							UpperCamelCase__     :List[Any]							      =				torch.manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				sd_pipe(
							    [prompt]					,     generator=UpperCamelCase_					,     guidance_scale=UpperCamelCase_					,     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					,     )

							UpperCamelCase__     :Dict							      =				output.images
							UpperCamelCase__     :List[str]							      =				image[0, -3:, -3:, -1]
							UpperCamelCase__     :Union[str, Any]							      =				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



'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'Speech2TextFeatureExtractor'
		_a									=       'Speech2TextTokenizer'




		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase_					,     UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				self.feature_extractor
							UpperCamelCase__     :Optional[Any]							      =				False




		def __call__(	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							if self._in_target_context_manager:
												return self.current_processor(*UpperCamelCase_					,     **UpperCamelCase_							)

							if "raw_speech" in kwargs:
												warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
												UpperCamelCase__     :Optional[Any]							      =				kwargs.pop('''raw_speech'''							)
							else:
												UpperCamelCase__     :List[Any]							      =				kwargs.pop('''audio'''					,     UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				kwargs.pop('''sampling_rate'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				kwargs.pop('''text'''					,     UpperCamelCase_							)
							if len(UpperCamelCase_							) > 0:
												UpperCamelCase__     :Tuple							      =				args[0]
												UpperCamelCase__     :Optional[int]							      =				args[1:]

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

							if audio is not None:
												UpperCamelCase__     :Any							      =				self.feature_extractor(UpperCamelCase_					,     *UpperCamelCase_					,     sampling_rate=UpperCamelCase_					,     **UpperCamelCase_							)
							if text is not None:
												UpperCamelCase__     :Dict							      =				self.tokenizer(UpperCamelCase_					,     **UpperCamelCase_							)

							if text is None:
												return inputs
							elif audio is None:
												return encodings
							else:
												UpperCamelCase__     :Dict							      =				encodings['''input_ids''']
												return inputs




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		@contextmanager
		def 					lowerCAmelCase__       (	self							):

							'''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 audio inputs, or in a separate call.'''							)
							UpperCamelCase__     :Optional[Any]							      =				True
							UpperCamelCase__     :Any							      =				self.tokenizer
							yield
							UpperCamelCase__     :Any							      =				self.feature_extractor
							UpperCamelCase__     :Any							      =				False