






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a    :   Dict        =			logging.get_logger(__name__)


_a    :   Any        =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

_a    :   Dict        =			{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

_a    :   Optional[int]        =			{
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 				snake_case__							(							):
    lowerCAmelCase__   :List[Any]      							=				(
        list(range(ord("!"       )    ,			ord("~"       ) + 1       )       ) + list(range(ord("¡"       )    ,			ord("¬"       ) + 1       )       ) + list(range(ord("®"       )    ,			ord("ÿ"       ) + 1       )       )
    )
    lowerCAmelCase__   :Optional[Any]      							=				bs[:]
    lowerCAmelCase__   :List[str]      							=				0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(UpperCAmelCase       )
            cs.append(2**8 + n       )
            n += 1
    lowerCAmelCase__   :int      							=				[chr(UpperCAmelCase       ) for n in cs]
    return dict(zip(UpperCAmelCase    ,			UpperCAmelCase       )       )


def 				snake_case__							(							UpperCAmelCase							:  Tuple       ):
    lowerCAmelCase__   :Union[str, Any]      							=				set()
    lowerCAmelCase__   :Optional[Any]      							=				word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        lowerCAmelCase__   :str      							=				char
    return pairs







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    VOCAB_FILES_NAMES
      A          =    PRETRAINED_VOCAB_FILES_MAP
      A          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A          =    ['''input_ids''', '''attention_mask''']
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase="replace"    ,	_lowerCAmelCase="<s>"    ,	_lowerCAmelCase="</s>"    ,	_lowerCAmelCase="</s>"    ,	_lowerCAmelCase="<s>"    ,	_lowerCAmelCase="<unk>"    ,	_lowerCAmelCase="<pad>"    ,	_lowerCAmelCase="<mask>"    ,	_lowerCAmelCase=False    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				AddedToken(_lowerCAmelCase    ,	lstrip=_lowerCAmelCase    ,	rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) else bos_token
          lowerCAmelCase__   :Any      							=				AddedToken(_lowerCAmelCase    ,	lstrip=_lowerCAmelCase    ,	rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) else eos_token
          lowerCAmelCase__   :Union[str, Any]      							=				AddedToken(_lowerCAmelCase    ,	lstrip=_lowerCAmelCase    ,	rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) else sep_token
          lowerCAmelCase__   :Optional[Any]      							=				AddedToken(_lowerCAmelCase    ,	lstrip=_lowerCAmelCase    ,	rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) else cls_token
          lowerCAmelCase__   :Tuple      							=				AddedToken(_lowerCAmelCase    ,	lstrip=_lowerCAmelCase    ,	rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) else unk_token
          lowerCAmelCase__   :List[str]      							=				AddedToken(_lowerCAmelCase    ,	lstrip=_lowerCAmelCase    ,	rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          lowerCAmelCase__   :List[str]      							=				AddedToken(_lowerCAmelCase    ,	lstrip=_lowerCAmelCase    ,	rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) else mask_token

          super().__init__(
              errors=_lowerCAmelCase    ,	bos_token=_lowerCAmelCase    ,	eos_token=_lowerCAmelCase    ,	unk_token=_lowerCAmelCase    ,	sep_token=_lowerCAmelCase    ,	cls_token=_lowerCAmelCase    ,	pad_token=_lowerCAmelCase    ,	mask_token=_lowerCAmelCase    ,	add_prefix_space=_lowerCAmelCase    ,	**_lowerCAmelCase    ,	)

          with open(_lowerCAmelCase    ,	encoding="utf-8"		) as vocab_handle:
              lowerCAmelCase__   :Optional[Any]      							=				json.load(_lowerCAmelCase		)
          lowerCAmelCase__   :Union[str, Any]      							=				{v: k for k, v in self.encoder.items()}
          lowerCAmelCase__   :int      							=				errors  # how to handle errors in decoding
          lowerCAmelCase__   :List[str]      							=				bytes_to_unicode()
          lowerCAmelCase__   :Dict      							=				{v: k for k, v in self.byte_encoder.items()}
          with open(_lowerCAmelCase    ,	encoding="utf-8"		) as merges_handle:
              lowerCAmelCase__   :Optional[Any]      							=				merges_handle.read().split("\n"		)[1:-1]
          lowerCAmelCase__   :List[Any]      							=				[tuple(merge.split()		) for merge in bpe_merges]
          lowerCAmelCase__   :int      							=				dict(zip(_lowerCAmelCase    ,	range(len(_lowerCAmelCase		)		)		)		)
          lowerCAmelCase__   :Tuple      							=				{}
          lowerCAmelCase__   :Optional[int]      							=				add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          lowerCAmelCase__   :Union[str, Any]      							=				re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return len(self.encoder		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return dict(self.encoder    ,	**self.added_tokens_encoder		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          if token in self.cache:
              return self.cache[token]
          lowerCAmelCase__   :Any      							=				tuple(_lowerCAmelCase		)
          lowerCAmelCase__   :Tuple      							=				get_pairs(_lowerCAmelCase		)

          if not pairs:
              return token

          while True:
              lowerCAmelCase__   :Union[str, Any]      							=				min(_lowerCAmelCase    ,	key=lambda _lowerCAmelCase		: self.bpe_ranks.get(_lowerCAmelCase    ,	float("inf"		)		)		)
              if bigram not in self.bpe_ranks:
                  break
              lowerCAmelCase__							,lowerCAmelCase__   :List[str]      							=				bigram
              lowerCAmelCase__   :str      							=				[]
              lowerCAmelCase__   :Optional[int]      							=				0
              while i < len(_lowerCAmelCase		):
                  try:
                      lowerCAmelCase__   :List[str]      							=				word.index(_lowerCAmelCase    ,	_lowerCAmelCase		)
                  except ValueError:
                      new_word.extend(word[i:]		)
                      break
                  else:
                      new_word.extend(word[i:j]		)
                      lowerCAmelCase__   :Optional[int]      							=				j

                  if word[i] == first and i < len(_lowerCAmelCase		) - 1 and word[i + 1] == second:
                      new_word.append(first + second		)
                      i += 2
                  else:
                      new_word.append(word[i]		)
                      i += 1
              lowerCAmelCase__   :int      							=				tuple(_lowerCAmelCase		)
              lowerCAmelCase__   :str      							=				new_word
              if len(_lowerCAmelCase		) == 1:
                  break
              else:
                  lowerCAmelCase__   :Optional[int]      							=				get_pairs(_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				" ".join(_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				word
          return word
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				[]
          for token in re.findall(self.pat    ,	_lowerCAmelCase		):
              lowerCAmelCase__   :List[Any]      							=				"".join(
                  self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase		).split(" "		)		)
          return bpe_tokens
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return self.encoder.get(_lowerCAmelCase    ,	self.encoder.get(self.unk_token		)		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          return self.decoder.get(_lowerCAmelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				"".join(_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"    ,	errors=self.errors		)
          return text
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''simple docstring'''
          if not os.path.isdir(_lowerCAmelCase		):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
          lowerCAmelCase__   :Dict      							=				os.path.join(
              _lowerCAmelCase    ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
          lowerCAmelCase__   :List[Any]      							=				os.path.join(
              _lowerCAmelCase    ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

          with open(_lowerCAmelCase    ,	"w"    ,	encoding="utf-8"		) as f:
              f.write(json.dumps(self.encoder    ,	indent=2    ,	sort_keys=_lowerCAmelCase    ,	ensure_ascii=_lowerCAmelCase		) + "\n"		)

          lowerCAmelCase__   :Union[str, Any]      							=				0
          with open(_lowerCAmelCase    ,	"w"    ,	encoding="utf-8"		) as writer:
              writer.write("#version: 0.2\n"		)
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,	key=lambda _lowerCAmelCase		: kv[1]		):
                  if index != token_index:
                      logger.warning(
                          f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                          " Please check that the tokenizer is not corrupted!"		)
                      lowerCAmelCase__   :List[Any]      							=				token_index
                  writer.write(" ".join(_lowerCAmelCase		) + "\n"		)
                  index += 1

          return vocab_file, merge_file
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''simple docstring'''
          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          lowerCAmelCase__   :int      							=				[self.cls_token_id]
          lowerCAmelCase__   :Union[str, Any]      							=				[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = False		):



          '''simple docstring'''
          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_lowerCAmelCase    ,	token_ids_a=_lowerCAmelCase    ,	already_has_special_tokens=_lowerCAmelCase		)

          if token_ids_a is None:
              return [1] + ([0] * len(_lowerCAmelCase		)) + [1]
          return [1] + ([0] * len(_lowerCAmelCase		)) + [1, 1] + ([0] * len(_lowerCAmelCase		)) + [1]
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				[self.sep_token_id]
          lowerCAmelCase__   :Optional[int]      							=				[self.cls_token_id]

          if token_ids_a is None:
              return len(cls + token_ids_a + sep		) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=False    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				kwargs.pop("add_prefix_space"    ,	self.add_prefix_space		)
          if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase		) > 0 and not text[0].isspace()):
              lowerCAmelCase__   :Any      							=				" " + text
          return (text, kwargs)











import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    KandinskyVaaPipeline
      A          =    [
          '''image_embeds''',
          '''negative_image_embeds''',
      ]
      A          =    ['''image_embeds''', '''negative_image_embeds''']
      A          =    [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      A          =    False
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 32
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 32
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.time_input_dim
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.time_input_dim * 4
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 100
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)

          lowerCAmelCase__   :List[str]      							=				{
              "in_channels": 4,
              # Out channels is double in channels because predicts mean and variance
              "out_channels": 8,
              "addition_embed_type": "image",
              "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
              "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
              "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "layers_per_block": 1,
              "encoder_hid_dim": self.text_embedder_hidden_size,
              "encoder_hid_dim_type": "image_proj",
              "cross_attention_dim": self.cross_attention_dim,
              "attention_head_dim": 4,
              "resnet_time_scale_shift": "scale_shift",
              "class_embed_type": None,
          }

          lowerCAmelCase__   :List[str]      							=				UNetaDConditionModel(**_lowerCAmelCase		)
          return model
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)
          lowerCAmelCase__   :Optional[Any]      							=				VQModel(**self.dummy_movq_kwargs		)
          return model
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.dummy_unet
          lowerCAmelCase__   :List[str]      							=				self.dummy_movq

          lowerCAmelCase__   :List[Any]      							=				DDIMScheduler(
              num_train_timesteps=1_000    ,	beta_schedule="linear"    ,	beta_start=0.0_0085    ,	beta_end=0.012    ,	clip_sample=_lowerCAmelCase    ,	set_alpha_to_one=_lowerCAmelCase    ,	steps_offset=1    ,	prediction_type="epsilon"    ,	thresholding=_lowerCAmelCase    ,	)

          lowerCAmelCase__   :Optional[Any]      							=				{
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }
          return components
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=0		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				floats_tensor((1, self.text_embedder_hidden_size)    ,	rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				floats_tensor((1, self.text_embedder_hidden_size)    ,	rng=random.Random(seed + 1		)		).to(
              _lowerCAmelCase		)
          if str(_lowerCAmelCase		).startswith("mps"		):
              lowerCAmelCase__   :List[Any]      							=				torch.manual_seed(_lowerCAmelCase		)
          else:
              lowerCAmelCase__   :Optional[int]      							=				torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				{
              "image_embeds": image_embeds,
              "negative_image_embeds": negative_image_embeds,
              "generator": generator,
              "height": 64,
              "width": 64,
              "guidance_scale": 4.0,
              "num_inference_steps": 2,
              "output_type": "np",
          }
          return inputs



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				"cpu"

          lowerCAmelCase__   :Dict      							=				self.get_dummy_components()

          lowerCAmelCase__   :Tuple      							=				self.pipeline_class(**_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				pipe.to(_lowerCAmelCase		)

          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :str      							=				pipe(**self.get_dummy_inputs(_lowerCAmelCase		)		)
          lowerCAmelCase__   :Dict      							=				output.images

          lowerCAmelCase__   :str      							=				pipe(
              **self.get_dummy_inputs(_lowerCAmelCase		)    ,	return_dict=_lowerCAmelCase    ,	)[0]

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

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

          lowerCAmelCase__   :Tuple      							=				np.array(
              [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]		)

          assert (
              np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
          ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
          ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







@slow
@require_torch_gpu
class 	_UpperCAmelCase  (  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'''
          lowerCAmelCase__   :int      							=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"		)

          lowerCAmelCase__   :str      							=				KandinskyVaaPriorPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-2-prior"    ,	torch_dtype=torch.floataa		)
          pipe_prior.to(_lowerCAmelCase		)

          lowerCAmelCase__   :str      							=				KandinskyVaaPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-2-decoder"    ,	torch_dtype=torch.floataa		)
          lowerCAmelCase__   :Union[str, Any]      							=				pipeline.to(_lowerCAmelCase		)
          pipeline.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[int]      							=				"red cat, 4k photo"

          lowerCAmelCase__   :Tuple      							=				torch.Generator(device="cuda"		).manual_seed(0		)
          lowerCAmelCase__							,lowerCAmelCase__   :Dict      							=				pipe_prior(
              _lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	num_inference_steps=5    ,	negative_prompt=""    ,	).to_tuple()

          lowerCAmelCase__   :List[Any]      							=				torch.Generator(device="cuda"		).manual_seed(0		)
          lowerCAmelCase__   :List[str]      							=				pipeline(
              image_embeds=_lowerCAmelCase    ,	negative_image_embeds=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	num_inference_steps=100    ,	output_type="np"    ,	)

          lowerCAmelCase__   :int      							=				output.images[0]

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

          assert_mean_pixel_difference(_lowerCAmelCase    ,	_lowerCAmelCase		)



