


'''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 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      str	):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						@property
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		1
													A_			    =		3
													A_			    =		(32, 32)

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






						@property
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													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							:      str	):
													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							:      Optional[int]	):
													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=10_00 ,  )
													return CLIPTextModel(lowerCAmelCase	)






						@property
						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													def extract(*lowerCAmelCase							:      Dict ,  **lowerCAmelCase							:      int	):






																				class 					__lowerCAmelCase   :

																										"""simple docstring"""






																										def __init__(  self							:      Optional[Any]	):
																																	A_			    =		torch.ones([0]	)






																										def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      List[Any]	):
																																	self.pixel_values.to(lowerCAmelCase	)
																																	return self



																				return Out()

													return extract






						def 				_UpperCAmelCase       (  self							:      Any	):
													A_			    =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													A_			    =		self.dummy_cond_unet
													A_			    =		DDIMScheduler(
													    beta_start=0.0_0_0_8_5 ,  beta_end=0.0_1_2 ,  beta_schedule="scaled_linear" ,  clip_sample=lowerCAmelCase ,  set_alpha_to_one=lowerCAmelCase ,  )

													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=lowerCAmelCase ,  scheduler=lowerCAmelCase ,  vae=lowerCAmelCase ,  text_encoder=lowerCAmelCase ,  tokenizer=lowerCAmelCase ,  safety_checker=lowerCAmelCase ,  feature_extractor=self.dummy_extractor ,  )
													A_			    =		sd_pipe.to(lowerCAmelCase	)
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

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

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

													A_			    =		torch.Generator(device=lowerCAmelCase	).manual_seed(0	)
													A_			    =		sd_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=6.0 ,  num_inference_steps=2 ,  output_type="np" ,  return_dict=lowerCAmelCase ,  )[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([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_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	):
													A_			    =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													A_			    =		self.dummy_cond_unet
													A_			    =		PNDMScheduler(skip_prk_steps=lowerCAmelCase	)
													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=lowerCAmelCase ,  scheduler=lowerCAmelCase ,  vae=lowerCAmelCase ,  text_encoder=lowerCAmelCase ,  tokenizer=lowerCAmelCase ,  safety_checker=lowerCAmelCase ,  feature_extractor=self.dummy_extractor ,  )
													A_			    =		sd_pipe.to(lowerCAmelCase	)
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

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

													A_			    =		output.images

													A_			    =		torch.Generator(device=lowerCAmelCase	).manual_seed(0	)
													A_			    =		sd_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=6.0 ,  num_inference_steps=2 ,  output_type="np" ,  return_dict=lowerCAmelCase ,  )[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([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]	)

													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							:      Union[str, Any]	):
													A_			    =		StableDiffusionPipeline.from_pretrained(
													    "hf-internal-testing/tiny-stable-diffusion-lms-pipe" ,  safety_checker=lowerCAmelCase	)
													assert isinstance(lowerCAmelCase ,  lowerCAmelCase	)
													assert isinstance(pipe.scheduler ,  lowerCAmelCase	)
													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(lowerCAmelCase	)
																				A_			    =		StableDiffusionPipeline.from_pretrained(lowerCAmelCase	)

													# 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	):
													A_			    =		self.dummy_cond_unet
													A_			    =		PNDMScheduler(skip_prk_steps=lowerCAmelCase	)
													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=lowerCAmelCase ,  scheduler=lowerCAmelCase ,  vae=lowerCAmelCase ,  text_encoder=lowerCAmelCase ,  tokenizer=lowerCAmelCase ,  safety_checker=lowerCAmelCase ,  feature_extractor=self.dummy_extractor ,  )
													A_			    =		sd_pipe.to(lowerCAmelCase	)
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

													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 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" ,  safety_checker=lowerCAmelCase	)
													A_			    =		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
													A_			    =		sd_pipe.to(lowerCAmelCase	)
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

													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_			    =		40_03_66_03_46
													A_			    =		7

													# without safety guidance (sld_guidance_scale = 0)
													A_			    =		torch.manual_seed(lowerCAmelCase	)
													A_			    =		sd_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=lowerCAmelCase ,  num_inference_steps=50 ,  output_type="np" ,  width=5_12 ,  height=5_12 ,  sld_guidance_scale=0 ,  )

													A_			    =		output.images
													A_			    =		image[0, -3:, -3:, -1]
													A_			    =		[0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

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

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

													# without safety guidance (strong configuration)
													A_			    =		torch.manual_seed(lowerCAmelCase	)
													A_			    =		sd_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=lowerCAmelCase ,  num_inference_steps=50 ,  output_type="np" ,  width=5_12 ,  height=5_12 ,  sld_guidance_scale=20_00 ,  sld_warmup_steps=7 ,  sld_threshold=0.0_2_5 ,  sld_momentum_scale=0.5 ,  sld_mom_beta=0.7 ,  )

													A_			    =		output.images
													A_			    =		image[0, -3:, -3:, -1]
													A_			    =		[0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

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

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






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" ,  safety_checker=lowerCAmelCase	)
													A_			    =		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
													A_			    =		sd_pipe.to(lowerCAmelCase	)
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

													A_			    =		"padme amidala taking a bath artwork, safe for work, no nudity"
													A_			    =		27_34_97_17_55
													A_			    =		7

													A_			    =		torch.manual_seed(lowerCAmelCase	)
													A_			    =		sd_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=lowerCAmelCase ,  num_inference_steps=50 ,  output_type="np" ,  width=5_12 ,  height=5_12 ,  sld_guidance_scale=0 ,  )

													A_			    =		output.images
													A_			    =		image[0, -3:, -3:, -1]
													A_			    =		[0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

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

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

													A_			    =		torch.manual_seed(lowerCAmelCase	)
													A_			    =		sd_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=lowerCAmelCase ,  num_inference_steps=50 ,  output_type="np" ,  width=5_12 ,  height=5_12 ,  sld_guidance_scale=20_00 ,  sld_warmup_steps=7 ,  sld_threshold=0.0_2_5 ,  sld_momentum_scale=0.5 ,  sld_mom_beta=0.7 ,  )

													A_			    =		output.images
													A_			    =		image[0, -3:, -3:, -1]
													A_			    =		[0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

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

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






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	)
													A_			    =		sd_pipe.to(lowerCAmelCase	)
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

													A_			    =		(
													    "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
													    " leyendecker"
													)
													A_			    =		10_44_35_52_34
													A_			    =		12

													A_			    =		torch.manual_seed(lowerCAmelCase	)
													A_			    =		sd_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=lowerCAmelCase ,  num_inference_steps=50 ,  output_type="np" ,  width=5_12 ,  height=5_12 ,  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, 5_12, 5_12, 3)

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

													A_			    =		torch.manual_seed(lowerCAmelCase	)
													A_			    =		sd_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=lowerCAmelCase ,  num_inference_steps=50 ,  output_type="np" ,  width=5_12 ,  height=5_12 ,  sld_guidance_scale=20_00 ,  sld_warmup_steps=7 ,  sld_threshold=0.0_2_5 ,  sld_momentum_scale=0.5 ,  sld_mom_beta=0.7 ,  )

													A_			    =		output.images
													A_			    =		image[0, -3:, -3:, -1]
													A_			    =		np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]	)
													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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
							from ..ta.tokenization_ta import TaTokenizer
else:
							from ...utils.dummy_sentencepiece_objects import TaTokenizer

__SCREAMING_SNAKE_CASE					:				str									=		TaTokenizer

if is_tokenizers_available():
							from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
							from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Any									=		TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Tuple									=		{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		[
							    '''MT5EncoderModel''',
							    '''MT5ForConditionalGeneration''',
							    '''MT5ForQuestionAnswering''',
							    '''MT5Model''',
							    '''MT5PreTrainedModel''',
							    '''MT5Stack''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				str									=		['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[str]									=		['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
							from .configuration_mta import MTaConfig, MTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mta import (
														    MTaEncoderModel,
														    MTaForConditionalGeneration,
														    MTaForQuestionAnswering,
														    MTaModel,
														    MTaPreTrainedModel,
														    MTaStack,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		_LazyModule(
							    __name__,
							    globals()['''__file__'''],
							    _import_structure,
							    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
							    module_spec=__spec__,
							)





