





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       A__     ( __snake_case ,     unittest.TestCase    ):

      '''simple docstring'''





      _UpperCAmelCase			:Union[str, Any]							=     LongformerTokenizer
      _UpperCAmelCase			:Optional[Any]							=     True
      _UpperCAmelCase			:Any							=     LongformerTokenizerFast
      _UpperCAmelCase			:List[str]							=     True





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       UpperCamelCase			:      List[Any] 			=     [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       UpperCamelCase			:      List[Any] 			=     dict(zip(A_ ,							range(len(A_     )     )     )     )
       UpperCamelCase			:      Any 			=     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       UpperCamelCase			:      Optional[Any] 			=     {"unk_token": "<unk>"}

       UpperCamelCase			:      Dict 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["vocab_file"]     )
       UpperCamelCase			:      Any 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["merges_file"]     )
       with open(self.vocab_file ,							"w" ,							encoding="utf-8"     ) as fp:
        fp.write(json.dumps(A_     ) + "\n"     )
       with open(self.merges_file ,							"w" ,							encoding="utf-8"     ) as fp:
        fp.write("\n".join(A_     )     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map     )
       return self.tokenizer_class.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map     )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     "lower newer"
       UpperCamelCase			:      str 			=     "lower newer"
       return input_text, output_text





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     self.tokenizer_class(self.vocab_file ,							self.merges_file ,							**self.special_tokens_map     )
       UpperCamelCase			:      str 			=     "lower newer"
       UpperCamelCase			:      Tuple 			=     ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       UpperCamelCase			:      int 			=     tokenizer.tokenize(A_     )  # , add_prefix_space=True)
       self.assertListEqual(A_ ,							A_     )

       UpperCamelCase			:      Optional[int] 			=     tokens + [tokenizer.unk_token]
       UpperCamelCase			:      List[Any] 			=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     ) ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.get_tokenizer()

       self.assertListEqual(tokenizer.encode("Hello world!" ,							add_special_tokens=A_     ) ,							[0, 3_1414, 232, 328, 2]     )
       self.assertListEqual(
           tokenizer.encode("Hello world! cécé herlolip 418" ,							add_special_tokens=A_     ) ,							[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2] ,							)





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"     )

       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.encode("sequence builders" ,							add_special_tokens=A_     )
       UpperCamelCase			:      Dict 			=     tokenizer.encode("multi-sequence build" ,							add_special_tokens=A_     )

       UpperCamelCase			:      Optional[Any] 			=     tokenizer.encode(
           "sequence builders" ,							add_special_tokens=A_ ,							add_prefix_space=A_     )
       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.encode(
           "sequence builders" ,							"multi-sequence build" ,							add_special_tokens=A_ ,							add_prefix_space=A_     )

       UpperCamelCase			:      List[str] 			=     tokenizer.build_inputs_with_special_tokens(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.build_inputs_with_special_tokens(A_ ,							A_     )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.get_tokenizer()

       UpperCamelCase			:      Dict 			=     "Encode this sequence."
       UpperCamelCase			:      Optional[int] 			=     tokenizer.byte_encoder[" ".encode("utf-8"     )[0]]

       # Testing encoder arguments
       UpperCamelCase			:      Optional[int] 			=     tokenizer.encode(A_ ,							add_special_tokens=A_ ,							add_prefix_space=A_     )
       UpperCamelCase			:      str 			=     tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
       self.assertNotEqual(A_ ,							A_     )

       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.encode(A_ ,							add_special_tokens=A_ ,							add_prefix_space=A_     )
       UpperCamelCase			:      Tuple 			=     tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
       self.assertEqual(A_ ,							A_     )

       tokenizer.add_special_tokens({"bos_token": "<s>"}     )
       UpperCamelCase			:      Dict 			=     tokenizer.encode(A_ ,							add_special_tokens=A_     )
       UpperCamelCase			:      Optional[Any] 			=     tokenizer.convert_ids_to_tokens(encoded[1]     )[0]
       self.assertNotEqual(A_ ,							A_     )

       # Testing spaces after special tokens
       UpperCamelCase			:      Union[str, Any] 			=     "<mask>"
       tokenizer.add_special_tokens(
           {"mask_token": AddedToken(A_ ,							lstrip=A_ ,							rstrip=A_     )}     )  # mask token has a left space
       UpperCamelCase			:      List[Any] 			=     tokenizer.convert_tokens_to_ids(A_     )

       UpperCamelCase			:      Any 			=     "Encode <mask> sequence"
       UpperCamelCase			:      Optional[int] 			=     "Encode <mask>sequence"

       UpperCamelCase			:      List[Any] 			=     tokenizer.encode(A_     )
       UpperCamelCase			:      Optional[int] 			=     encoded.index(A_     )
       UpperCamelCase			:      Optional[Any] 			=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
       self.assertEqual(A_ ,							A_     )

       UpperCamelCase			:      Dict 			=     tokenizer.encode(A_     )
       UpperCamelCase			:      List[str] 			=     encoded.index(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
       self.assertNotEqual(A_ ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
         UpperCamelCase			:      Tuple 			=     self.rust_tokenizer_class.from_pretrained(A_ ,							**A_     )
         UpperCamelCase			:      Union[str, Any] 			=     self.tokenizer_class.from_pretrained(A_ ,							**A_     )
         UpperCamelCase			:      Any 			=     "A, <mask> AllenNLP sentence."
         UpperCamelCase			:      int 			=     tokenizer_r.encode_plus(A_ ,							add_special_tokens=A_ ,							return_token_type_ids=A_     )
         UpperCamelCase			:      Any 			=     tokenizer_p.encode_plus(A_ ,							add_special_tokens=A_ ,							return_token_type_ids=A_     )

         # token_type_ids should put 0 everywhere
         self.assertEqual(sum(tokens_r["token_type_ids"]     ) ,							sum(tokens_p["token_type_ids"]     )     )

         # attention_mask should put 1 everywhere, so sum over length should be 1
         self.assertEqual(
             sum(tokens_r["attention_mask"]     ) / len(tokens_r["attention_mask"]     ) ,							sum(tokens_p["attention_mask"]     ) / len(tokens_p["attention_mask"]     ) ,							)

         UpperCamelCase			:      Optional[Any] 			=     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]     )
         UpperCamelCase			:      int 			=     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]     )

         # Rust correctly handles the space before the mask while python doesnt
         self.assertSequenceEqual(tokens_p["input_ids"] ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]     )
         self.assertSequenceEqual(tokens_r["input_ids"] ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]     )

         self.assertSequenceEqual(
             A_ ,							["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]     )
         self.assertSequenceEqual(
             A_ ,							["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for trim_offsets, add_prefix_space in itertools.product([True, False] ,							repeat=2     ):
        UpperCamelCase			:      str 			=     self.rust_tokenizer_class.from_pretrained(
            self.tmpdirname ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )

        UpperCamelCase			:      Optional[Any] 			=     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
        UpperCamelCase			:      Union[str, Any] 			=     json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()     )

        self.assertEqual(pre_tokenizer_state["add_prefix_space"] ,							A_     )

        self.assertEqual(post_processor_state["add_prefix_space"] ,							A_     )
        self.assertEqual(post_processor_state["trim_offsets"] ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
         UpperCamelCase			:      Union[str, Any] 			=     "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
         UpperCamelCase			:      Tuple 			=     F"""{text_of_1_token} {text_of_1_token}"""

         UpperCamelCase			:      List[Any] 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Any 			=     tokenizer_r(A_ ,							return_offsets_mapping=A_ ,							add_special_tokens=A_     )
         self.assertEqual(encoding.offset_mapping[0] ,							(0, len(A_     ))     )
         self.assertEqual(
             encoding.offset_mapping[1] ,							(len(A_     ) + 1, len(A_     ) + 1 + len(A_     )) ,							)

         UpperCamelCase			:      Any 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Optional[int] 			=     tokenizer_r(A_ ,							return_offsets_mapping=A_ ,							add_special_tokens=A_     )
         self.assertEqual(encoding.offset_mapping[0] ,							(0, len(A_     ))     )
         self.assertEqual(
             encoding.offset_mapping[1] ,							(len(A_     ) + 1, len(A_     ) + 1 + len(A_     )) ,							)

         UpperCamelCase			:      int 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Union[str, Any] 			=     tokenizer_r(A_ ,							return_offsets_mapping=A_ ,							add_special_tokens=A_     )
         self.assertEqual(encoding.offset_mapping[0] ,							(0, len(A_     ))     )
         self.assertEqual(
             encoding.offset_mapping[1] ,							(len(A_     ), len(A_     ) + 1 + len(A_     )) ,							)

         UpperCamelCase			:      Any 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Optional[int] 			=     tokenizer_r(A_ ,							return_offsets_mapping=A_ ,							add_special_tokens=A_     )
         self.assertEqual(encoding.offset_mapping[0] ,							(0, len(A_     ))     )
         self.assertEqual(
             encoding.offset_mapping[1] ,							(len(A_     ), len(A_     ) + 1 + len(A_     )) ,							)

         UpperCamelCase			:      List[Any] 			=     F""" {text}"""

         # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
         #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
         # )
         # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
         # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
         # self.assertEqual(
         #     encoding.offset_mapping[1],
         #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
         # )

         UpperCamelCase			:      str 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Dict 			=     tokenizer_r(A_ ,							return_offsets_mapping=A_ ,							add_special_tokens=A_     )
         self.assertEqual(encoding.offset_mapping[0] ,							(1, 1 + len(A_     ))     )
         self.assertEqual(
             encoding.offset_mapping[1] ,							(1 + len(A_     ) + 1, 1 + len(A_     ) + 1 + len(A_     )) ,							)

         UpperCamelCase			:      Dict 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Union[str, Any] 			=     tokenizer_r(A_ ,							return_offsets_mapping=A_ ,							add_special_tokens=A_     )
         self.assertEqual(encoding.offset_mapping[0] ,							(0, 1 + len(A_     ))     )
         self.assertEqual(
             encoding.offset_mapping[1] ,							(1 + len(A_     ), 1 + len(A_     ) + 1 + len(A_     )) ,							)

         UpperCamelCase			:      Dict 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Optional[Any] 			=     tokenizer_r(A_ ,							return_offsets_mapping=A_ ,							add_special_tokens=A_     )
         self.assertEqual(encoding.offset_mapping[0] ,							(0, 1 + len(A_     ))     )
         self.assertEqual(
             encoding.offset_mapping[1] ,							(1 + len(A_     ), 1 + len(A_     ) + 1 + len(A_     )) ,							)







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       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:str							=     KandinskyVaaPipeline
      _UpperCAmelCase			:str							=     [
          'image_embeds',
          'negative_image_embeds',
      ]
      _UpperCAmelCase			:str							=     ['image_embeds', 'negative_image_embeds']
      _UpperCAmelCase			:List[str]							=     [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase			:List[str]							=     False





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim * 4





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 100





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )

       UpperCamelCase			:      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,
       }

       UpperCamelCase			:      Dict 			=     UNetaDConditionModel(**A_     )
       return model





      @property
      def  __UpperCamelCase(				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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )
       UpperCamelCase			:      Optional[Any] 			=     VQModel(**self.dummy_movq_kwargs     )
       return model





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.dummy_unet
       UpperCamelCase			:      Optional[Any] 			=     self.dummy_movq

       UpperCamelCase			:      Dict 			=     DDIMScheduler(
           num_train_timesteps=1000 ,							beta_schedule="linear" ,							beta_start=0.0_00_85 ,							beta_end=0.0_12 ,							clip_sample=A_ ,							set_alpha_to_one=A_ ,							steps_offset=1 ,							prediction_type="epsilon" ,							thresholding=A_ ,							)

       UpperCamelCase			:      Tuple 			=     {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }
       return components





      def  __UpperCamelCase(				self ,							A_ ,							A_=0     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(A_     )     ).to(A_     )
       UpperCamelCase			:      Optional[Any] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(seed + 1     )     ).to(
           A_     )
       if str(A_     ).startswith("mps"     ):
        UpperCamelCase			:      Optional[Any] 			=     torch.manual_seed(A_     )
       else:
        UpperCamelCase			:      List[Any] 			=     torch.Generator(device=A_     ).manual_seed(A_     )
       UpperCamelCase			:      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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     "cpu"

       UpperCamelCase			:      List[str] 			=     self.get_dummy_components()

       UpperCamelCase			:      Tuple 			=     self.pipeline_class(**A_     )
       UpperCamelCase			:      List[str] 			=     pipe.to(A_     )

       pipe.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      Dict 			=     pipe(**self.get_dummy_inputs(A_     )     )
       UpperCamelCase			:      Optional[int] 			=     output.images

       UpperCamelCase			:      int 			=     pipe(
           **self.get_dummy_inputs(A_     ) ,							return_dict=A_ ,							)[0]

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

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

       UpperCamelCase			:      int 			=     np.array(
           [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]     )

       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       A__     ( unittest.TestCase    ):





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"     )

       UpperCamelCase			:      Dict 			=     KandinskyVaaPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior" ,							torch_dtype=torch.floataa     )
       pipe_prior.to(A_     )

       UpperCamelCase			:      Dict 			=     KandinskyVaaPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-decoder" ,							torch_dtype=torch.floataa     )
       UpperCamelCase			:      Tuple 			=     pipeline.to(A_     )
       pipeline.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      str 			=     "red cat, 4k photo"

       UpperCamelCase			:      str 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			,					UpperCamelCase			:      Tuple 			=     pipe_prior(
           A_ ,							generator=A_ ,							num_inference_steps=5 ,							negative_prompt="" ,							).to_tuple()

       UpperCamelCase			:      int 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			:      Tuple 			=     pipeline(
           image_embeds=A_ ,							negative_image_embeds=A_ ,							generator=A_ ,							num_inference_steps=100 ,							output_type="np" ,							)

       UpperCamelCase			:      Union[str, Any] 			=     output.images[0]

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

       assert_mean_pixel_difference(A_ ,							A_     )
