

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 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

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





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		str        =		1
      snake_case							:		str        =		3
      snake_case							:		Optional[Any]        =		(32, 32)

      snake_case							:		int        =		floats_tensor((batch_size, num_channels) + sizes   ,rng=random.Random(0    )    ).to(SCREAMING_SNAKE_CASE_    )
      return image





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      torch.manual_seed(0    )
      snake_case							:		str        =		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    snake_case_       (  self    ):

      '''simple docstring'''
      torch.manual_seed(0    )
      snake_case							:		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    snake_case_       (  self    ):

      '''simple docstring'''
      torch.manual_seed(0    )
      snake_case							:		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(SCREAMING_SNAKE_CASE_    )





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      def extract(*SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ):



          class 						_A     :





            '''simple docstring'''



            def __init__(  self    ):

                '''simple docstring'''
                snake_case							:		Optional[Any]        =		torch.ones([0]    )





            def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

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







          return Out()

      return extract





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Dict        =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      snake_case							:		Optional[Any]        =		self.dummy_cond_unet
      snake_case							:		Optional[int]        =		DDIMScheduler(
          beta_start=0.0_00_85   ,beta_end=0.0_12   ,beta_schedule="""scaled_linear"""   ,clip_sample=SCREAMING_SNAKE_CASE_   ,set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,)

      snake_case							:		Any        =		self.dummy_vae
      snake_case							:		List[str]        =		self.dummy_text_encoder
      snake_case							:		int        =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

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

      snake_case							:		str        =		"""A painting of a squirrel eating a burger"""

      snake_case							:		List[Any]        =		torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(0    )
      snake_case							:		Tuple        =		sd_pipe([prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type="""np"""    )
      snake_case							:		Any        =		output.images

      snake_case							:		Union[str, Any]        =		torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(0    )
      snake_case							:		Dict        =		sd_pipe(
          [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type="""np"""   ,return_dict=SCREAMING_SNAKE_CASE_   ,)[0]

      snake_case							:		Dict        =		image[0, -3:, -3:, -1]
      snake_case							:		Tuple        =		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      snake_case							:		Any        =		np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]    )

      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    snake_case_       (  self    ):

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

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

      snake_case							:		Tuple        =		"""A painting of a squirrel eating a burger"""
      snake_case							:		Dict        =		torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(0    )
      snake_case							:		Optional[int]        =		sd_pipe([prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type="""np"""    )

      snake_case							:		str        =		output.images

      snake_case							:		Dict        =		torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(0    )
      snake_case							:		str        =		sd_pipe(
          [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=6.0   ,num_inference_steps=2   ,output_type="""np"""   ,return_dict=SCREAMING_SNAKE_CASE_   ,)[0]

      snake_case							:		int        =		image[0, -3:, -3:, -1]
      snake_case							:		Any        =		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      snake_case							:		int        =		np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]    )

      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    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		StableDiffusionPipeline.from_pretrained(
          """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""   ,safety_checker=SCREAMING_SNAKE_CASE_    )
      assert isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      assert isinstance(pipe.scheduler   ,SCREAMING_SNAKE_CASE_    )
      assert pipe.safety_checker is None

      snake_case							:		str        =		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(SCREAMING_SNAKE_CASE_    )
          snake_case							:		Any        =		StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_    )

      # sanity check that the pipeline still works
      assert pipe.safety_checker is None
      snake_case							:		int        =		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    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.dummy_cond_unet
      snake_case							:		Tuple        =		PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		self.dummy_vae
      snake_case							:		int        =		self.dummy_text_encoder
      snake_case							:		Any        =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

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

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

      snake_case							:		Tuple        =		"""A painting of a squirrel eating a burger"""
      snake_case							:		int        =		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				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   ,safety_checker=SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config    )
      snake_case							:		Dict        =		sd_pipe.to(SCREAMING_SNAKE_CASE_    )
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		List[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 """
      )
      snake_case							:		List[Any]        =		4003660346
      snake_case							:		Optional[Any]        =		7

      # without safety guidance (sld_guidance_scale = 0)
      snake_case							:		List[str]        =		torch.manual_seed(SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		sd_pipe(
          [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,sld_guidance_scale=0   ,)

      snake_case							:		Optional[Any]        =		output.images
      snake_case							:		Union[str, Any]        =		image[0, -3:, -3:, -1]
      snake_case							:		Optional[int]        =		[0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

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

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

      # without safety guidance (strong configuration)
      snake_case							:		int        =		torch.manual_seed(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		sd_pipe(
          [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,sld_guidance_scale=2000   ,sld_warmup_steps=7   ,sld_threshold=0.0_25   ,sld_momentum_scale=0.5   ,sld_mom_beta=0.7   ,)

      snake_case							:		Dict        =		output.images
      snake_case							:		Optional[int]        =		image[0, -3:, -3:, -1]
      snake_case							:		str        =		[0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

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

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   ,safety_checker=SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config    )
      snake_case							:		List[Any]        =		sd_pipe.to(SCREAMING_SNAKE_CASE_    )
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[Any]        =		"""padme amidala taking a bath artwork, safe for work, no nudity"""
      snake_case							:		Optional[Any]        =		2734971755
      snake_case							:		Dict        =		7

      snake_case							:		Tuple        =		torch.manual_seed(SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		sd_pipe(
          [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,sld_guidance_scale=0   ,)

      snake_case							:		Union[str, Any]        =		output.images
      snake_case							:		Union[str, Any]        =		image[0, -3:, -3:, -1]
      snake_case							:		Any        =		[0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

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

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

      snake_case							:		str        =		torch.manual_seed(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Union[str, Any]        =		sd_pipe(
          [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,sld_guidance_scale=2000   ,sld_warmup_steps=7   ,sld_threshold=0.0_25   ,sld_momentum_scale=0.5   ,sld_mom_beta=0.7   ,)

      snake_case							:		str        =		output.images
      snake_case							:		Optional[Any]        =		image[0, -3:, -3:, -1]
      snake_case							:		Any        =		[0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

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

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





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""    )
      snake_case							:		str        =		sd_pipe.to(SCREAMING_SNAKE_CASE_    )
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[int]        =		(
          """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
          """ leyendecker"""
      )
      snake_case							:		List[Any]        =		1044355234
      snake_case							:		Optional[Any]        =		12

      snake_case							:		Union[str, Any]        =		torch.manual_seed(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Dict        =		sd_pipe(
          [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,sld_guidance_scale=0   ,)

      snake_case							:		str        =		output.images
      snake_case							:		Optional[int]        =		image[0, -3:, -3:, -1]
      snake_case							:		Tuple        =		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

      snake_case							:		str        =		torch.manual_seed(SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		sd_pipe(
          [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=50   ,output_type="""np"""   ,width=512   ,height=512   ,sld_guidance_scale=2000   ,sld_warmup_steps=7   ,sld_threshold=0.0_25   ,sld_momentum_scale=0.5   ,sld_mom_beta=0.7   ,)

      snake_case							:		Dict        =		output.images
      snake_case							:		Optional[Any]        =		image[0, -3:, -3:, -1]
      snake_case							:		Optional[Any]        =		np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]    )
      assert image.shape == (1, 512, 512, 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_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''SpeechT5Tokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SpeechT5ForSpeechToText''',
       '''SpeechT5ForSpeechToSpeech''',
       '''SpeechT5ForTextToSpeech''',
       '''SpeechT5Model''',
       '''SpeechT5PreTrainedModel''',
       '''SpeechT5HifiGan''',
   ]

if TYPE_CHECKING:
   from .configuration_speechta import (
       SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
       SpeechTaConfig,
       SpeechTaHifiGanConfig,
   )
   from .feature_extraction_speechta import SpeechTaFeatureExtractor
   from .processing_speechta import SpeechTaProcessor

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_speechta import SpeechTaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speechta import (
          SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechTaForSpeechToSpeech,
          SpeechTaForSpeechToText,
          SpeechTaForTextToSpeech,
          SpeechTaHifiGan,
          SpeechTaModel,
          SpeechTaPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



