


import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


__lowercase  :   Tuple						      =logging.get_logger(__name__)

__lowercase  :   Optional[int]						      ={
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

__lowercase  :   int						      ={
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

__lowercase  :   List[str]						      ={
    'jukebox': 512,
}

class      A       (	__lowercase						):
      _snake_case							     =VOCAB_FILES_NAMES
      _snake_case							     =PRETRAINED_VOCAB_FILES_MAP
      _snake_case							     =PRETRAINED_LYRIC_TOKENS_SIZES
      _snake_case							     =['''input_ids''', '''attention_mask''']



      def __init__(	self:						int		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Optional[Any]=["v3", "v2", "v2"]		,  _lowerCAmelCase:						Any=512		,  _lowerCAmelCase:						str=5		,  _lowerCAmelCase:						Tuple="<|endoftext|>"		,  **_lowerCAmelCase:						Optional[int]		,  )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=AddedToken(_lowerCAmelCase		,  lstrip=_lowerCAmelCase		,  rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ) else unk_token
         super().__init__(
             unk_token=_lowerCAmelCase		,  n_genres=_lowerCAmelCase		,  version=_lowerCAmelCase		,  max_n_lyric_tokens=_lowerCAmelCase		,  **_lowerCAmelCase		,  )
         UpperCAmelCase_											=version
         UpperCAmelCase_											=max_n_lyric_tokens
         UpperCAmelCase_											=n_genres

         with open(_lowerCAmelCase		,  encoding="utf-8"       ) as vocab_handle:
            UpperCAmelCase_											=json.load(_lowerCAmelCase       )

         with open(_lowerCAmelCase		,  encoding="utf-8"       ) as vocab_handle:
            UpperCAmelCase_											=json.load(_lowerCAmelCase       )

         with open(_lowerCAmelCase		,  encoding="utf-8"       ) as vocab_handle:
            UpperCAmelCase_											=json.load(_lowerCAmelCase       )

         UpperCAmelCase_											=r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
         # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
         if len(self.lyrics_encoder       ) == 79:
            UpperCAmelCase_											=oov.replace(r"\-'"		,  r"\-+'"       )

         UpperCAmelCase_											=regex.compile(_lowerCAmelCase       )
         UpperCAmelCase_											={v: k for k, v in self.artists_encoder.items()}
         UpperCAmelCase_											={v: k for k, v in self.genres_encoder.items()}
         UpperCAmelCase_											={v: k for k, v in self.lyrics_encoder.items()}



      @property
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         return len(self.artists_encoder       ) + len(self.genres_encoder       ) + len(self.lyrics_encoder       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    str:
         '''simple docstring'''

         return dict(self.artists_encoder		,  self.genres_encoder		,  self.lyrics_encoder       )



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Tuple       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=[self.artists_encoder.get(_lowerCAmelCase		,  0       ) for artist in list_artists]
         for genres in range(len(_lowerCAmelCase       )       ):
            UpperCAmelCase_											=[self.genres_encoder.get(_lowerCAmelCase		,  0       ) for genre in list_genres[genres]]
            UpperCAmelCase_											=list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]       ))

         UpperCAmelCase_											=[[self.lyrics_encoder.get(_lowerCAmelCase		,  0       ) for character in list_lyrics[0]], [], []]
         return artists_id, list_genres, lyric_ids



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any]       )						->    Tuple:
         '''simple docstring'''

         return list(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Any		,  _lowerCAmelCase:						Dict		,  **_lowerCAmelCase:						List[str]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=self.prepare_for_tokenization(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=self._tokenize(_lowerCAmelCase       )
         return artist, genre, lyrics



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						bool = False       )						->    Tuple[str, str, str, Dict[str, Any]]:
         '''simple docstring'''

         for idx in range(len(self.version       )       ):
            if self.version[idx] == "v3":
               UpperCAmelCase_											=artists[idx].lower()
               UpperCAmelCase_											=[genres[idx].lower()]
            else:
               UpperCAmelCase_											=self._normalize(artists[idx]       ) + ".v2"
               UpperCAmelCase_											=[
                   self._normalize(_lowerCAmelCase       ) + ".v2" for genre in genres[idx].split("_"       )
               ]  # split is for the full dictionary with combined genres

         if self.version[0] == "v2":
            UpperCAmelCase_											=regex.compile(r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"       )
            UpperCAmelCase_											="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
            UpperCAmelCase_											={vocab[index]: index + 1 for index in range(len(_lowerCAmelCase       )       )}
            UpperCAmelCase_											=0
            UpperCAmelCase_											=len(_lowerCAmelCase       ) + 1
            UpperCAmelCase_											=self.vocab
            UpperCAmelCase_											={v: k for k, v in self.vocab.items()}
            UpperCAmelCase_											=""
         else:
            UpperCAmelCase_											=regex.compile(r"[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+"       )

         UpperCAmelCase_											=self._run_strip_accents(_lowerCAmelCase       )
         UpperCAmelCase_											=lyrics.replace("\\"		,  "\n"       )
         UpperCAmelCase_											=self.out_of_vocab.sub(""		,  _lowerCAmelCase       ), [], []
         return artists, genres, lyrics



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						List[str]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=unicodedata.normalize("NFD"		,  _lowerCAmelCase       )
         UpperCAmelCase_											=[]
         for char in text:
            UpperCAmelCase_											=unicodedata.category(_lowerCAmelCase       )
            if cat == "Mn":
               continue
            output.append(_lowerCAmelCase       )
         return "".join(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						str       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=(
             [chr(_lowerCAmelCase       ) for i in range(ord("a"       )		,  ord("z"       ) + 1       )]
             + [chr(_lowerCAmelCase       ) for i in range(ord("A"       )		,  ord("Z"       ) + 1       )]
             + [chr(_lowerCAmelCase       ) for i in range(ord("0"       )		,  ord("9"       ) + 1       )]
             + ["."]
         )
         UpperCAmelCase_											=frozenset(_lowerCAmelCase       )
         UpperCAmelCase_											=re.compile(r"_+"       )
         UpperCAmelCase_											="".join([c if c in accepted else "_" for c in text.lower()]       )
         UpperCAmelCase_											=pattern.sub("_"		,  _lowerCAmelCase       ).strip("_"       )
         return text



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						List[str]       )						->    str:
         '''simple docstring'''

         return " ".join(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Optional[Union[str, TensorType]] = None		,  _lowerCAmelCase:						bool = False       )						->    Dict:
         '''simple docstring'''

         if not isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=TensorType(_lowerCAmelCase       )

         # Get a function reference for the correct framework
         if tensor_type == TensorType.TENSORFLOW:
            if not is_tf_available():
               raise ImportError(
                   "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."       )
            import tensorflow as tf

            UpperCAmelCase_											=tf.constant
            UpperCAmelCase_											=tf.is_tensor
         elif tensor_type == TensorType.PYTORCH:
            if not is_torch_available():
               raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed."       )
            import torch

            UpperCAmelCase_											=torch.tensor
            UpperCAmelCase_											=torch.is_tensor
         elif tensor_type == TensorType.JAX:
            if not is_flax_available():
               raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed."       )
            import jax.numpy as jnp  # noqa: F811

            UpperCAmelCase_											=jnp.array
            UpperCAmelCase_											=_is_jax
         else:
            UpperCAmelCase_											=np.asarray
            UpperCAmelCase_											=_is_numpy

         # Do the tensor conversion in batch

         try:
            if prepend_batch_axis:
               UpperCAmelCase_											=[inputs]

            if not is_tensor(_lowerCAmelCase       ):
               UpperCAmelCase_											=as_tensor(_lowerCAmelCase       )
         except:  # noqa E722
            raise ValueError(
                "Unable to create tensor, you should probably activate truncation and/or padding "
                "with 'padding=True' 'truncation=True' to have batched tensors with the same length."       )

         return inputs



      def __call__(	self:						str		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						str=""		,  _lowerCAmelCase:						Optional[Any]="pt"       )						->    BatchEncoding:
         '''simple docstring'''

         UpperCAmelCase_											=[0, 0, 0]
         UpperCAmelCase_											=[artist] * len(self.version       )
         UpperCAmelCase_											=[genres] * len(self.version       )

         UpperCAmelCase_											=self.tokenize(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=self._convert_token_to_id(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )

         UpperCAmelCase_											=[-INFINITY] * len(full_tokens[-1]       )
         UpperCAmelCase_											=[
             self.convert_to_tensors(
                 [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]		,  tensor_type=_lowerCAmelCase       )
             for i in range(len(self.version       )       )
         ]
         return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks}       )



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[str] = None       )						->    Tuple[str]:
         '''simple docstring'''

         if not os.path.isdir(_lowerCAmelCase       ):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'       )
            return

         UpperCAmelCase_											=os.path.join(
             _lowerCAmelCase		,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["artists_file"]       )
         with open(_lowerCAmelCase		,  "w"		,  encoding="utf-8"       ) as f:
            f.write(json.dumps(self.artists_encoder		,  ensure_ascii=_lowerCAmelCase       )       )

         UpperCAmelCase_											=os.path.join(
             _lowerCAmelCase		,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["genres_file"]       )
         with open(_lowerCAmelCase		,  "w"		,  encoding="utf-8"       ) as f:
            f.write(json.dumps(self.genres_encoder		,  ensure_ascii=_lowerCAmelCase       )       )

         UpperCAmelCase_											=os.path.join(
             _lowerCAmelCase		,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"]       )
         with open(_lowerCAmelCase		,  "w"		,  encoding="utf-8"       ) as f:
            f.write(json.dumps(self.lyrics_encoder		,  ensure_ascii=_lowerCAmelCase       )       )

         return (artists_file, genres_file, lyrics_file)






      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Optional[int]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.artists_decoder.get(_lowerCAmelCase       )
         UpperCAmelCase_											=[self.genres_decoder.get(_lowerCAmelCase       ) for genre in genres_index]
         UpperCAmelCase_											=[self.lyrics_decoder.get(_lowerCAmelCase       ) for character in lyric_index]
         return artist, genres, lyrics











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						):



      def 	lowerCAmelCase__    (	self:						str       )						->    Optional[Any]:
         '''simple docstring'''

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



      @property
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=1
         UpperCAmelCase_											=3
         UpperCAmelCase_											=(32, 32)

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



      @property
      def 	lowerCAmelCase__    (	self:						Any       )						->    Union[str, Any]:
         '''simple docstring'''

         torch.manual_seed(0       )
         UpperCAmelCase_											=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:						List[Any]       )						->    int:
         '''simple docstring'''

         torch.manual_seed(0       )
         UpperCAmelCase_											=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:						Dict       )						->    str:
         '''simple docstring'''

         torch.manual_seed(0       )
         UpperCAmelCase_											=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(_lowerCAmelCase       )



      @property
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         def extract(*_lowerCAmelCase:						List[str]		,  **_lowerCAmelCase:						List[str]       ):

            class      A       :



                  def __init__(	self:						List[str]       )						->    Any:
                     '''simple docstring'''

                     UpperCAmelCase_											=torch.ones([0]       )






                  def 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						Dict       )						->    List[Any]:
                     '''simple docstring'''

                     self.pixel_values.to(_lowerCAmelCase       )
                     return self

            return Out()

         return extract



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_											=self.dummy_cond_unet
         UpperCAmelCase_											=DDIMScheduler(
             beta_start=0.0_00_85		,  beta_end=0.0_12		,  beta_schedule="scaled_linear"		,  clip_sample=_lowerCAmelCase		,  set_alpha_to_one=_lowerCAmelCase		,  )

         UpperCAmelCase_											=self.dummy_vae
         UpperCAmelCase_											=self.dummy_text_encoder
         UpperCAmelCase_											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

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

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

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

         UpperCAmelCase_											=torch.Generator(device=_lowerCAmelCase       ).manual_seed(0       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=6.0		,  num_inference_steps=2		,  output_type="np"		,  return_dict=_lowerCAmelCase		,  )[0]

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

         assert image.shape == (1, 64, 64, 3)
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						Optional[int]       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_											=self.dummy_cond_unet
         UpperCAmelCase_											=PNDMScheduler(skip_prk_steps=_lowerCAmelCase       )
         UpperCAmelCase_											=self.dummy_vae
         UpperCAmelCase_											=self.dummy_text_encoder
         UpperCAmelCase_											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

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

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

         UpperCAmelCase_											=output.images

         UpperCAmelCase_											=torch.Generator(device=_lowerCAmelCase       ).manual_seed(0       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=6.0		,  num_inference_steps=2		,  output_type="np"		,  return_dict=_lowerCAmelCase		,  )[0]

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

         assert image.shape == (1, 64, 64, 3)
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						List[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=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

         UpperCAmelCase_											=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       )
            UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained(_lowerCAmelCase       )

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         UpperCAmelCase_											=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:						int       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.dummy_cond_unet
         UpperCAmelCase_											=PNDMScheduler(skip_prk_steps=_lowerCAmelCase       )
         UpperCAmelCase_											=self.dummy_vae
         UpperCAmelCase_											=self.dummy_text_encoder
         UpperCAmelCase_											=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

         # put models in fp16
         UpperCAmelCase_											=unet.half()
         UpperCAmelCase_											=vae.half()
         UpperCAmelCase_											=bert.half()

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

         UpperCAmelCase_											="A painting of a squirrel eating a burger"
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						List[Any]       )						->    Tuple:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,  safety_checker=_lowerCAmelCase       )
         UpperCAmelCase_											=LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
         UpperCAmelCase_											=sd_pipe.to(_lowerCAmelCase       )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

         UpperCAmelCase_											=(
             "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_											=40_0366_0346
         UpperCAmelCase_											=7

         # without safety guidance (sld_guidance_scale = 0)
         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  num_inference_steps=50		,  output_type="np"		,  width=512		,  height=512		,  sld_guidance_scale=0		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=[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)
         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  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		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=[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 	lowerCAmelCase__    (	self:						Any       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,  safety_checker=_lowerCAmelCase       )
         UpperCAmelCase_											=LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
         UpperCAmelCase_											=sd_pipe.to(_lowerCAmelCase       )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

         UpperCAmelCase_											="padme amidala taking a bath artwork, safe for work, no nudity"
         UpperCAmelCase_											=27_3497_1755
         UpperCAmelCase_											=7

         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  num_inference_steps=50		,  output_type="np"		,  width=512		,  height=512		,  sld_guidance_scale=0		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=[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

         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  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		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=[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 	lowerCAmelCase__    (	self:						str       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
         UpperCAmelCase_											=sd_pipe.to(_lowerCAmelCase       )
         sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

         UpperCAmelCase_											=(
             "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
             " leyendecker"
         )
         UpperCAmelCase_											=10_4435_5234
         UpperCAmelCase_											=12

         UpperCAmelCase_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  num_inference_steps=50		,  output_type="np"		,  width=512		,  height=512		,  sld_guidance_scale=0		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=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_											=torch.manual_seed(_lowerCAmelCase       )
         UpperCAmelCase_											=sd_pipe(
             [prompt]		,  generator=_lowerCAmelCase		,  guidance_scale=_lowerCAmelCase		,  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		,  )

         UpperCAmelCase_											=output.images
         UpperCAmelCase_											=image[0, -3:, -3:, -1]
         UpperCAmelCase_											=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




