





'''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.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class    __a (unittest.TestCase       ):

							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    List[str]:







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

							@property
							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    List[str]:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[int]         =					1
													UpperCAmelCase_     : List[str]         =					3
													UpperCAmelCase_     : Tuple         =					(32, 32)

													UpperCAmelCase_     : Optional[Any]         =					floats_tensor((batch_size, num_channels) + sizes    ,       rng=random.Random(0				)				).to(__magic_name__				)
													return image

							@property
							def       UpperCAmelCase__		(							self							:       Tuple				)   ->    List[Any]:







													"""simple docstring"""
													torch.manual_seed(0				)
													UpperCAmelCase_     : Optional[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       UpperCAmelCase__		(							self							:       Any				)   ->    List[Any]:







													"""simple docstring"""
													torch.manual_seed(0				)
													UpperCAmelCase_     : List[str]         =					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							:       List[Any]				)   ->    Tuple:







													"""simple docstring"""
													torch.manual_seed(0				)
													UpperCAmelCase_     : str         =					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=10_00    ,       )
													return CLIPTextModel(__magic_name__				)

							@property
							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    Optional[int]:







													"""simple docstring"""
													def extract(*__magic_name__							:       Optional[int]    ,       **__magic_name__							:       Optional[Any]				):


																			class    __a :

																										def __init__(							self							:       Dict				)   ->    Optional[int]:







																																"""simple docstring"""
																																UpperCAmelCase_     : Tuple         =					torch.ones([0]				)



																										def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       Tuple				)   ->    Dict:







																																"""simple docstring"""
																																self.pixel_values.to(__magic_name__				)
																																return self





																			return Out()

													return extract

							def       UpperCAmelCase__		(							self							:       Union[str, Any]				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_     : int         =					self.dummy_cond_unet
													UpperCAmelCase_     : Union[str, Any]         =					DDIMScheduler(
													    beta_start=0.0_0_0_8_5    ,       beta_end=0.0_1_2    ,       beta_schedule='''scaled_linear'''    ,       clip_sample=__magic_name__    ,       set_alpha_to_one=__magic_name__    ,       )

													UpperCAmelCase_     : int         =					self.dummy_vae
													UpperCAmelCase_     : str         =					self.dummy_text_encoder
													UpperCAmelCase_     : Tuple         =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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

													UpperCAmelCase_     : int         =					'''A painting of a squirrel eating a burger'''

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

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

													UpperCAmelCase_     : str         =					image[0, -3:, -3:, -1]
													UpperCAmelCase_     : List[str]         =					image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													UpperCAmelCase_     : Dict         =					np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]				)

													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				)   ->    str:







													"""simple docstring"""
													UpperCAmelCase_     : Tuple         =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_     : Union[str, Any]         =					self.dummy_cond_unet
													UpperCAmelCase_     : Optional[Any]         =					PNDMScheduler(skip_prk_steps=__magic_name__				)
													UpperCAmelCase_     : str         =					self.dummy_vae
													UpperCAmelCase_     : Any         =					self.dummy_text_encoder
													UpperCAmelCase_     : List[Any]         =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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

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

													UpperCAmelCase_     : Tuple         =					output.images

													UpperCAmelCase_     : Tuple         =					torch.Generator(device=__magic_name__				).manual_seed(0				)
													UpperCAmelCase_     : Dict         =					sd_pipe(
													    [prompt]    ,       generator=__magic_name__    ,       guidance_scale=6.0    ,       num_inference_steps=2    ,       output_type='''np'''    ,       return_dict=__magic_name__    ,       )[0]

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

													assert image.shape == (1, 64, 64, 3)
													UpperCAmelCase_     : Union[str, Any]         =					np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]				)

													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				)   ->    Dict:







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

													UpperCAmelCase_     : 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(__magic_name__				)
																			UpperCAmelCase_     : List[Any]         =					StableDiffusionPipeline.from_pretrained(__magic_name__				)

													# sanity check that the pipeline still works
													assert pipe.safety_checker is None
													UpperCAmelCase_     : Dict         =					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							:       Union[str, Any]				)   ->    str:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					self.dummy_cond_unet
													UpperCAmelCase_     : Dict         =					PNDMScheduler(skip_prk_steps=__magic_name__				)
													UpperCAmelCase_     : Optional[Any]         =					self.dummy_vae
													UpperCAmelCase_     : int         =					self.dummy_text_encoder
													UpperCAmelCase_     : Tuple         =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

													# put models in fp16
													UpperCAmelCase_     : Tuple         =					unet.half()
													UpperCAmelCase_     : Union[str, Any]         =					vae.half()
													UpperCAmelCase_     : List[str]         =					bert.half()

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

													UpperCAmelCase_     : Optional[int]         =					'''A painting of a squirrel eating a burger'''
													UpperCAmelCase_     : List[str]         =					sd_pipe([prompt]    ,       num_inference_steps=2    ,       output_type='''np'''				).images

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





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

							def       UpperCAmelCase__		(							self							:       str				)   ->    Union[str, Any]:







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

							def       UpperCAmelCase__		(							self							:       str				)   ->    List[str]:







													"""simple docstring"""
													UpperCAmelCase_     : Tuple         =					'''cuda'''
													UpperCAmelCase_     : str         =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				)
													UpperCAmelCase_     : Union[str, Any]         =					pipe.to(__magic_name__				)
													pipe.set_progress_bar_config(disable=__magic_name__				)

													UpperCAmelCase_     : Dict         =					'''a photo of a cat'''
													UpperCAmelCase_     : Any         =					{
													    '''editing_prompt''': ['''sunglasses'''],
													    '''reverse_editing_direction''': [False],
													    '''edit_warmup_steps''': 10,
													    '''edit_guidance_scale''': 6,
													    '''edit_threshold''': 0.9_5,
													    '''edit_momentum_scale''': 0.5,
													    '''edit_mom_beta''': 0.6,
													}

													UpperCAmelCase_     : List[Any]         =					3
													UpperCAmelCase_     : List[Any]         =					7

													# no sega enabled
													UpperCAmelCase_     : str         =					torch.Generator(__magic_name__				)
													generator.manual_seed(__magic_name__				)
													UpperCAmelCase_     : List[str]         =					pipe(
													    [prompt]    ,       generator=__magic_name__    ,       guidance_scale=__magic_name__    ,       num_inference_steps=50    ,       output_type='''np'''    ,       width=5_12    ,       height=5_12    ,       )

													UpperCAmelCase_     : int         =					output.images
													UpperCAmelCase_     : Optional[int]         =					image[0, -3:, -3:, -1]
													UpperCAmelCase_     : Union[str, Any]         =					[
													    0.3_4_6_7_3_1_1_3,
													    0.3_8_4_9_2_7_3_3,
													    0.3_7_5_9_7_3_5_2,
													    0.3_4_0_8_6_3_3_5,
													    0.3_5_6_5_0_7_4_8,
													    0.3_5_5_7_9_2_0_5,
													    0.3_3_8_4_7_6_3,
													    0.3_4_3_4_0_2_3_6,
													    0.3_5_7_3_2_7_1,
													]

													assert image.shape == (1, 5_12, 5_12, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(__magic_name__				)
													UpperCAmelCase_     : List[str]         =					pipe(
													    [prompt]    ,       generator=__magic_name__    ,       guidance_scale=__magic_name__    ,       num_inference_steps=50    ,       output_type='''np'''    ,       width=5_12    ,       height=5_12    ,       **__magic_name__    ,       )

													UpperCAmelCase_     : List[str]         =					output.images
													UpperCAmelCase_     : List[Any]         =					image[0, -3:, -3:, -1]
													UpperCAmelCase_     : Optional[Any]         =					[
													    0.4_1_8_8_7_8_2_6,
													    0.3_7_7_2_8_7_6_6,
													    0.3_0_1_3_8_2_7_2,
													    0.4_1_4_1_6_3_3_5,
													    0.4_1_6_6_4_9_8_5,
													    0.3_6_2_8_3_3_9_2,
													    0.3_6_1_9_1_2_4_6,
													    0.4_3_3_6_4_4_6_5,
													    0.4_3_0_0_1_7_3_2,
													]

													assert image.shape == (1, 5_12, 5_12, 3)

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

							def       UpperCAmelCase__		(							self							:       int				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[Any]         =					'''cuda'''
													UpperCAmelCase_     : Optional[int]         =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				)
													UpperCAmelCase_     : Optional[int]         =					pipe.to(__magic_name__				)
													pipe.set_progress_bar_config(disable=__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					'''an image of a crowded boulevard, realistic, 4k'''
													UpperCAmelCase_     : Any         =					{
													    '''editing_prompt''': '''crowd, crowded, people''',
													    '''reverse_editing_direction''': True,
													    '''edit_warmup_steps''': 10,
													    '''edit_guidance_scale''': 8.3,
													    '''edit_threshold''': 0.9,
													    '''edit_momentum_scale''': 0.5,
													    '''edit_mom_beta''': 0.6,
													}

													UpperCAmelCase_     : int         =					9
													UpperCAmelCase_     : Optional[Any]         =					7

													# no sega enabled
													UpperCAmelCase_     : int         =					torch.Generator(__magic_name__				)
													generator.manual_seed(__magic_name__				)
													UpperCAmelCase_     : Optional[int]         =					pipe(
													    [prompt]    ,       generator=__magic_name__    ,       guidance_scale=__magic_name__    ,       num_inference_steps=50    ,       output_type='''np'''    ,       width=5_12    ,       height=5_12    ,       )

													UpperCAmelCase_     : Union[str, Any]         =					output.images
													UpperCAmelCase_     : Any         =					image[0, -3:, -3:, -1]
													UpperCAmelCase_     : Any         =					[
													    0.4_3_4_9_7_9_9_8,
													    0.9_1_8_1_4_0_6_5,
													    0.7_5_4_0_7_3_9,
													    0.5_5_5_8_0_2_0_5,
													    0.8_4_6_7_2_6_5,
													    0.5_3_8_9_6_9_1,
													    0.6_2_5_7_4_5_0_6,
													    0.5_8_8_9_7_7_6_3,
													    0.5_0_9_2_6_7_5_7,
													]

													assert image.shape == (1, 5_12, 5_12, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(__magic_name__				)
													UpperCAmelCase_     : Optional[Any]         =					pipe(
													    [prompt]    ,       generator=__magic_name__    ,       guidance_scale=__magic_name__    ,       num_inference_steps=50    ,       output_type='''np'''    ,       width=5_12    ,       height=5_12    ,       **__magic_name__    ,       )

													UpperCAmelCase_     : Optional[int]         =					output.images
													UpperCAmelCase_     : List[str]         =					image[0, -3:, -3:, -1]
													UpperCAmelCase_     : Any         =					[
													    0.3_0_8_9_7_1_9,
													    0.3_0_5_0_0_1_4_4,
													    0.2_9_0_1_6_0_4_2,
													    0.3_0_6_3_0_9_6_4,
													    0.3_2_5_6_8_7,
													    0.2_9_4_1_9_2_2_5,
													    0.2_9_0_8_0_9_1,
													    0.2_8_7_2_3_5_9_8,
													    0.2_7_6_9_6_2_9_4,
													]

													assert image.shape == (1, 5_12, 5_12, 3)

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

							def       UpperCAmelCase__		(							self							:       Tuple				)   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					'''cuda'''
													UpperCAmelCase_     : Dict         =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				)
													UpperCAmelCase_     : Tuple         =					pipe.to(__magic_name__				)
													pipe.set_progress_bar_config(disable=__magic_name__				)

													UpperCAmelCase_     : Union[str, Any]         =					'''a castle next to a river'''
													UpperCAmelCase_     : Any         =					{
													    '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
													    '''reverse_editing_direction''': False,
													    '''edit_warmup_steps''': [15, 18],
													    '''edit_guidance_scale''': 6,
													    '''edit_threshold''': [0.9, 0.8],
													    '''edit_momentum_scale''': 0.5,
													    '''edit_mom_beta''': 0.6,
													}

													UpperCAmelCase_     : Optional[int]         =					48
													UpperCAmelCase_     : Dict         =					7

													# no sega enabled
													UpperCAmelCase_     : int         =					torch.Generator(__magic_name__				)
													generator.manual_seed(__magic_name__				)
													UpperCAmelCase_     : Any         =					pipe(
													    [prompt]    ,       generator=__magic_name__    ,       guidance_scale=__magic_name__    ,       num_inference_steps=50    ,       output_type='''np'''    ,       width=5_12    ,       height=5_12    ,       )

													UpperCAmelCase_     : Dict         =					output.images
													UpperCAmelCase_     : Tuple         =					image[0, -3:, -3:, -1]
													UpperCAmelCase_     : Dict         =					[
													    0.7_5_1_6_3_5_5_5,
													    0.7_6_0_3_7_1_4_5,
													    0.6_1_7_8_5,
													    0.9_1_8_9_6_7_3,
													    0.8_6_2_7_7_0_1,
													    0.8_5_1_8_9_6_9_4,
													    0.8_5_1_2_8_1_3,
													    0.8_7_0_1_2_0_7_6,
													    0.8_3_1_2_8_5_7,
													]

													assert image.shape == (1, 5_12, 5_12, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(__magic_name__				)
													UpperCAmelCase_     : Any         =					pipe(
													    [prompt]    ,       generator=__magic_name__    ,       guidance_scale=__magic_name__    ,       num_inference_steps=50    ,       output_type='''np'''    ,       width=5_12    ,       height=5_12    ,       **__magic_name__    ,       )

													UpperCAmelCase_     : Optional[Any]         =					output.images
													UpperCAmelCase_     : Optional[Any]         =					image[0, -3:, -3:, -1]
													UpperCAmelCase_     : List[str]         =					[
													    0.7_3_5_5_3_3_6_5,
													    0.7_5_3_7_2_7_1,
													    0.7_4_3_4_1_9_0_5,
													    0.6_6_4_8_0_3_5_6,
													    0.6_4_7_2_9_2_5,
													    0.6_3_0_3_9_4_1_6,
													    0.6_4_8_1_2_9_0_5,
													    0.6_7_4_9_7_1_7,
													    0.6_5_1_7_1_0_2,
													]

													assert image.shape == (1, 5_12, 5_12, 3)

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



							def       UpperCAmelCase__		(							self							:       Tuple				)   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					'''cuda'''
													UpperCAmelCase_     : List[str]         =					StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    ,       torch_dtype=torch.floataa				)
													UpperCAmelCase_     : Dict         =					pipe.to(__magic_name__				)
													pipe.set_progress_bar_config(disable=__magic_name__				)

													UpperCAmelCase_     : Optional[int]         =					'''a photo of a cat'''
													UpperCAmelCase_     : int         =					{
													    '''editing_prompt''': ['''sunglasses'''],
													    '''reverse_editing_direction''': [False],
													    '''edit_warmup_steps''': 10,
													    '''edit_guidance_scale''': 6,
													    '''edit_threshold''': 0.9_5,
													    '''edit_momentum_scale''': 0.5,
													    '''edit_mom_beta''': 0.6,
													}

													UpperCAmelCase_     : str         =					3
													UpperCAmelCase_     : List[str]         =					7

													# no sega enabled
													UpperCAmelCase_     : List[Any]         =					torch.Generator(__magic_name__				)
													generator.manual_seed(__magic_name__				)
													UpperCAmelCase_     : Tuple         =					pipe(
													    [prompt]    ,       generator=__magic_name__    ,       guidance_scale=__magic_name__    ,       num_inference_steps=50    ,       output_type='''np'''    ,       width=5_12    ,       height=5_12    ,       )

													UpperCAmelCase_     : Tuple         =					output.images
													UpperCAmelCase_     : Any         =					image[0, -3:, -3:, -1]
													UpperCAmelCase_     : List[str]         =					[
													    0.3_4_8_8_7_6_9_5,
													    0.3_8_7_6_9_5_3,
													    0.3_7_5,
													    0.3_4_4_2_3_8_2_8,
													    0.3_5_8_1_5_4_3,
													    0.3_5_7_1_7_7_7_3,
													    0.3_3_8_3_7_8_9,
													    0.3_4_5_7_0_3_1_2,
													    0.3_5_9_3_7_5,
													]

													assert image.shape == (1, 5_12, 5_12, 3)

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

													# with sega enabled
													# generator = torch.manual_seed(seed)
													generator.manual_seed(__magic_name__				)
													UpperCAmelCase_     : Any         =					pipe(
													    [prompt]    ,       generator=__magic_name__    ,       guidance_scale=__magic_name__    ,       num_inference_steps=50    ,       output_type='''np'''    ,       width=5_12    ,       height=5_12    ,       **__magic_name__    ,       )

													UpperCAmelCase_     : Union[str, Any]         =					output.images
													UpperCAmelCase_     : Tuple         =					image[0, -3:, -3:, -1]
													UpperCAmelCase_     : Optional[Any]         =					[
													    0.4_2_2_8_5_1_5_6,
													    0.3_6_9_1_4_0_6_2,
													    0.2_9_0_7_7_1_4_8,
													    0.4_2_0_4_1_0_1_6,
													    0.4_1_9_1_8_9_4_5,
													    0.3_5_4_9_8_0_4_7,
													    0.3_6_1_8_1_6_4,
													    0.4_4_2_3_8_2_8,
													    0.4_3_1_1_5_2_3_4,
													]

													assert image.shape == (1, 5_12, 5_12, 3)

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












'''simple docstring'''







from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


snake_case_       :    int          =					logging.get_logger(__name__)


class    __a (nn.Module       ):
							__a   :				HybridCLIPConfig
							__a   :				jnp.dtype       =    jnp.floataa

							def       UpperCAmelCase__		(							self							:       Optional[Any]				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.config.text_config
													UpperCAmelCase_     : Dict         =					self.config.vision_config

													UpperCAmelCase_     : Tuple         =					self.config.projection_dim
													UpperCAmelCase_     : str         =					text_config.hidden_size
													UpperCAmelCase_     : List[Any]         =					vision_config.hidden_size

													UpperCAmelCase_     : int         =					FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
													UpperCAmelCase_     : int         =					FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__    ,       __magic_name__				).module_class

													UpperCAmelCase_     : str         =					text_module(__magic_name__    ,       dtype=self.dtype				)
													UpperCAmelCase_     : List[Any]         =					vision_module(__magic_name__    ,       dtype=self.dtype				)

													UpperCAmelCase_     : int         =					nn.Dense(
													    self.projection_dim    ,       dtype=self.dtype    ,       kernel_init=jax.nn.initializers.normal(0.0_2				)    ,       use_bias=__magic_name__    ,       )
													UpperCAmelCase_     : Union[str, Any]         =					nn.Dense(
													    self.projection_dim    ,       dtype=self.dtype    ,       kernel_init=jax.nn.initializers.normal(0.0_2				)    ,       use_bias=__magic_name__    ,       )
													UpperCAmelCase_     : Tuple         =					self.param('''logit_scale'''    ,       jax.nn.initializers.ones    ,       []				)



							def __call__(							self							:       Tuple    ,       __magic_name__							:       Optional[Any]=None    ,       __magic_name__							:       List[Any]=None    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       str=None    ,       __magic_name__							:       List[Any]=None    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       List[str]=None    ,       __magic_name__							:       Optional[int]=None    ,       __magic_name__							:       Any=None    ,       )   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : int         =					return_dict if return_dict is not None else self.config.return_dict

													UpperCAmelCase_     : Union[str, Any]         =					self.vision_model(
													    pixel_values=__magic_name__    ,       deterministic=__magic_name__    ,       output_attentions=__magic_name__    ,       output_hidden_states=__magic_name__    ,       return_dict=__magic_name__    ,       )

													UpperCAmelCase_     : str         =					self.text_model(
													    input_ids=__magic_name__    ,       attention_mask=__magic_name__    ,       token_type_ids=__magic_name__    ,       position_ids=__magic_name__    ,       deterministic=__magic_name__    ,       output_attentions=__magic_name__    ,       output_hidden_states=__magic_name__    ,       return_dict=__magic_name__    ,       )

													UpperCAmelCase_     : List[str]         =					vision_outputs[1]
													UpperCAmelCase_     : Union[str, Any]         =					self.visual_projection(__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					text_outputs[1]
													UpperCAmelCase_     : Optional[Any]         =					self.text_projection(__magic_name__				)

													# normalized features
													UpperCAmelCase_     : int         =					image_embeds / jnp.linalg.norm(__magic_name__    ,       axis=-1    ,       keepdims=__magic_name__				)
													UpperCAmelCase_     : List[str]         =					text_embeds / jnp.linalg.norm(__magic_name__    ,       axis=-1    ,       keepdims=__magic_name__				)

													# cosine similarity as logits
													UpperCAmelCase_     : str         =					jnp.exp(self.logit_scale				)
													UpperCAmelCase_     : Optional[int]         =					jnp.matmul(__magic_name__    ,       image_embeds.T				) * logit_scale
													UpperCAmelCase_     : int         =					logits_per_text.T

													if not return_dict:
																			return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

													return FlaxCLIPOutput(
													    logits_per_image=__magic_name__    ,       logits_per_text=__magic_name__    ,       text_embeds=__magic_name__    ,       image_embeds=__magic_name__    ,       text_model_output=__magic_name__    ,       vision_model_output=__magic_name__    ,       )





class    __a (lowerCamelCase       ):
							__a   :				int       =    HybridCLIPConfig
							__a   :				Optional[Any]       =    FlaxHybridCLIPModule

							def __init__(							self							:       Dict    ,       __magic_name__							:       HybridCLIPConfig    ,       __magic_name__							:       Optional[Tuple] = None    ,       __magic_name__							:       int = 0    ,       __magic_name__							:       jnp.dtype = jnp.floataa    ,       **__magic_name__							:       Optional[Any]    ,       )   ->    str:







													"""simple docstring"""
													if input_shape is None:
																			UpperCAmelCase_     : Optional[Any]         =					((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

													UpperCAmelCase_     : Any         =					self.module_class(config=__magic_name__    ,       dtype=__magic_name__    ,       **__magic_name__				)
													super().__init__(__magic_name__    ,       __magic_name__    ,       input_shape=__magic_name__    ,       seed=__magic_name__    ,       dtype=__magic_name__				)

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       jax.random.PRNGKey    ,       __magic_name__							:       Tuple    ,       __magic_name__							:       FrozenDict = None				)   ->    FrozenDict:







													"""simple docstring"""
													# init input tensor
													UpperCAmelCase_     : List[Any]         =					jnp.zeros(input_shape[0]    ,       dtype='''i4'''				)
													UpperCAmelCase_     : List[Any]         =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__magic_name__				).shape[-1]				)    ,       input_shape[0]				)
													UpperCAmelCase_     : Optional[int]         =					jnp.ones_like(__magic_name__				)
													UpperCAmelCase_     : str         =					jnp.ones_like(__magic_name__				)

													UpperCAmelCase_     : Optional[int]         =					jax.random.normal(__magic_name__    ,       input_shape[1]				)

													UpperCAmelCase_  ,    UpperCAmelCase_     : Optional[int]         =					jax.random.split(__magic_name__				)
													UpperCAmelCase_     : List[Any]         =					{'''params''': params_rng, '''dropout''': dropout_rng}

													return self.module.init(__magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__				)["params"]

							def __call__(							self							:       Optional[Any]    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       int    ,       __magic_name__							:       List[str]=None    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       List[str]=None    ,       __magic_name__							:       dict = None    ,       __magic_name__							:       jax.random.PRNGKey = None    ,       __magic_name__							:       bool = False    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[bool] = None    ,       )   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					output_attentions if output_attentions is not None else self.config.output_attentions
													UpperCAmelCase_     : List[str]         =					(
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													UpperCAmelCase_     : List[Any]         =					return_dict if return_dict is not None else self.config.return_dict

													if position_ids is None:
																			UpperCAmelCase_     : List[Any]         =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__magic_name__				).shape[-1]				)    ,       input_ids.shape				)

													if token_type_ids is None:
																			UpperCAmelCase_     : Optional[Any]         =					jnp.zeros_like(__magic_name__				)

													if attention_mask is None:
																			UpperCAmelCase_     : Tuple         =					jnp.ones_like(__magic_name__				)

													# Handle any PRNG if needed
													UpperCAmelCase_     : Dict         =					{}
													if dropout_rng is not None:
																			UpperCAmelCase_     : Any         =					dropout_rng

													return self.module.apply(
													    {'''params''': params or self.params}    ,       jnp.array(__magic_name__    ,       dtype='''i4'''				)    ,       jnp.array(__magic_name__    ,       dtype=jnp.floataa				)    ,       jnp.array(__magic_name__    ,       dtype='''i4'''				)    ,       jnp.array(__magic_name__    ,       dtype='''i4'''				)    ,       jnp.array(__magic_name__    ,       dtype='''i4'''				)    ,       not train    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__    ,       rngs=__magic_name__    ,       )

							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       Union[str, Any]    ,       __magic_name__							:       int=None    ,       __magic_name__							:       List[str]=None    ,       __magic_name__							:       int=None    ,       __magic_name__							:       dict = None    ,       __magic_name__							:       jax.random.PRNGKey = None    ,       __magic_name__							:       Tuple=False    ,       )   ->    Tuple:







													"""simple docstring"""
													if position_ids is None:
																			UpperCAmelCase_     : Optional[int]         =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__magic_name__				).shape[-1]				)    ,       input_ids.shape				)

													if token_type_ids is None:
																			UpperCAmelCase_     : Tuple         =					jnp.zeros_like(__magic_name__				)

													if attention_mask is None:
																			UpperCAmelCase_     : Tuple         =					jnp.ones_like(__magic_name__				)

													# Handle any PRNG if needed
													UpperCAmelCase_     : List[str]         =					{}
													if dropout_rng is not None:
																			UpperCAmelCase_     : str         =					dropout_rng

													def _get_features(__magic_name__							:       Any    ,       __magic_name__							:       str    ,       __magic_name__							:       List[Any]    ,       __magic_name__							:       str    ,       __magic_name__							:       Union[str, Any]    ,       __magic_name__							:       Dict				):
																			UpperCAmelCase_     : Optional[Any]         =					module.text_model(
																			    input_ids=__magic_name__    ,       attention_mask=__magic_name__    ,       position_ids=__magic_name__    ,       token_type_ids=__magic_name__    ,       deterministic=__magic_name__    ,       )
																			UpperCAmelCase_     : Tuple         =					text_outputs[1]
																			UpperCAmelCase_     : Optional[int]         =					module.text_projection(__magic_name__				)
																			return text_features

													return self.module.apply(
													    {'''params''': params or self.params}    ,       jnp.array(__magic_name__    ,       dtype='''i4'''				)    ,       jnp.array(__magic_name__    ,       dtype='''i4'''				)    ,       jnp.array(__magic_name__    ,       dtype='''i4'''				)    ,       jnp.array(__magic_name__    ,       dtype='''i4'''				)    ,       not train    ,       method=_get_features    ,       rngs=__magic_name__    ,       )

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       str    ,       __magic_name__							:       dict = None    ,       __magic_name__							:       jax.random.PRNGKey = None    ,       __magic_name__							:       Dict=False				)   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					{}
													if dropout_rng is not None:
																			UpperCAmelCase_     : Union[str, Any]         =					dropout_rng

													def _get_features(__magic_name__							:       List[str]    ,       __magic_name__							:       List[Any]    ,       __magic_name__							:       List[Any]				):
																			UpperCAmelCase_     : Optional[int]         =					module.vision_model(pixel_values=__magic_name__    ,       deterministic=__magic_name__				)
																			UpperCAmelCase_     : Tuple         =					vision_outputs[1]  # pooled_output
																			UpperCAmelCase_     : List[Any]         =					module.visual_projection(__magic_name__				)
																			return image_features

													return self.module.apply(
													    {'''params''': params or self.params}    ,       jnp.array(__magic_name__    ,       dtype=jnp.floataa				)    ,       not train    ,       method=_get_features    ,       rngs=__magic_name__    ,       )



							@classmethod
							def       UpperCAmelCase__		(							cls							:       Tuple    ,       __magic_name__							:       str = None    ,       __magic_name__							:       str = None    ,       *__magic_name__							:       Union[str, Any]    ,       **__magic_name__							:       Union[str, Any]    ,       )   ->    FlaxPreTrainedModel:







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					{
													    argument[len('''text_'''				) :]: value for argument, value in kwargs.items() if argument.startswith('''text_'''				)
													}

													UpperCAmelCase_     : List[Any]         =					{
													    argument[len('''vision_'''				) :]: value for argument, value in kwargs.items() if argument.startswith('''vision_'''				)
													}

													# remove text, vision kwargs from kwargs
													for key in kwargs_text.keys():
																			del kwargs["text_" + key]
													for key in kwargs_vision.keys():
																			del kwargs["vision_" + key]

													# Load and initialize the text and vision model
													UpperCAmelCase_     : List[str]         =					kwargs_text.pop('''model'''    ,       __magic_name__				)
													if text_model is None:
																			assert (
																			    text_model_name_or_path is not None
																			), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
																			from transformers import FlaxAutoModel

																			if "config" not in kwargs_text:
																									from transformers import AutoConfig

																									UpperCAmelCase_     : Optional[int]         =					AutoConfig.from_pretrained(__magic_name__				)
																									UpperCAmelCase_     : Optional[int]         =					text_config

																			UpperCAmelCase_     : Dict         =					FlaxAutoModel.from_pretrained(__magic_name__    ,       *__magic_name__    ,       **__magic_name__				)

													UpperCAmelCase_     : Union[str, Any]         =					kwargs_vision.pop('''model'''    ,       __magic_name__				)
													if vision_model is None:
																			assert (
																			    vision_model_name_or_path is not None
																			), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
																			from transformers import FlaxAutoModel

																			if "config" not in kwargs_vision:
																									from transformers import AutoConfig

																									UpperCAmelCase_     : Tuple         =					AutoConfig.from_pretrained(__magic_name__				)
																									UpperCAmelCase_     : Union[str, Any]         =					vision_config

																			UpperCAmelCase_     : Union[str, Any]         =					FlaxAutoModel.from_pretrained(__magic_name__    ,       *__magic_name__    ,       **__magic_name__				)

													# instantiate config with corresponding kwargs
													UpperCAmelCase_     : Union[str, Any]         =					kwargs.pop('''dtype'''    ,       jnp.floataa				)
													UpperCAmelCase_     : Tuple         =					HybridCLIPConfig.from_text_vision_configs(text_model.config    ,       vision_model.config    ,       **__magic_name__				)

													# init model
													UpperCAmelCase_     : Union[str, Any]         =					cls(__magic_name__    ,       *__magic_name__    ,       dtype=__magic_name__    ,       **__magic_name__				)

													if vision_config.model_type == "clip":
																			UpperCAmelCase_     : Union[str, Any]         =					vision_model.params['''vision_model''']
																			UpperCAmelCase_     : Tuple         =					vision_model.params['''visual_projection''']['''kernel''']
													else:
																			UpperCAmelCase_     : Union[str, Any]         =					vision_model.params

													UpperCAmelCase_     : Optional[Any]         =					text_model.params

													return model





