





"""simple docstring"""




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 		SCREAMING_SNAKE_CASE__    (	snake_case_						,				unittest.TestCase  ):
      A_						:    str							=   KandinskyVaaPipeline
      A_						:    str							=   [
          "image_embeds",
          "negative_image_embeds",
      ]
      A_						:    str							=   ["image_embeds", "negative_image_embeds"]
      A_						:    int							=   [
          "generator",
          "height",
          "width",
          "latents",
          "guidance_scale",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      A_						:    Dict							=   False


      @property
      def        a			(self	:				Tuple				):





         """simple docstring"""



         return 32


      @property
      def        a			(self	:				Tuple				):





         """simple docstring"""



         return 32


      @property
      def        a			(self	:				List[str]				):





         """simple docstring"""



         return self.time_input_dim


      @property
      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         return self.time_input_dim * 4


      @property
      def        a			(self	:				List[Any]				):





         """simple docstring"""



         return 100


      @property
      def        a			(self	:				List[str]				):





         """simple docstring"""



         torch.manual_seed(0				)

         __snake_case								=       {
             '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,
         }

         __snake_case								=       UNetaDConditionModel(**_A				)
         return model


      @property
      def        a			(self	:				List[Any]				):





         """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        a			(self	:				str				):





         """simple docstring"""



         torch.manual_seed(0				)
         __snake_case								=       VQModel(**self.dummy_movq_kwargs				)
         return model


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       self.dummy_unet
         __snake_case								=       self.dummy_movq

         __snake_case								=       DDIMScheduler(
             num_train_timesteps=1000    ,      beta_schedule='''linear'''    ,      beta_start=0.0_0_0_8_5    ,      beta_end=0.0_1_2    ,      clip_sample=_A    ,      set_alpha_to_one=_A    ,      steps_offset=1    ,      prediction_type='''epsilon'''    ,      thresholding=_A    ,      )

         __snake_case								=       {
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }
         return components


      def        a			(self	:				List[Any]    ,      a__	:				Tuple    ,      a__	:				List[str]=0				):





         """simple docstring"""



         __snake_case								=       floats_tensor((1, self.text_embedder_hidden_size)    ,      rng=random.Random(_A				)				).to(_A				)
         __snake_case								=       floats_tensor((1, self.text_embedder_hidden_size)    ,      rng=random.Random(seed + 1				)				).to(
             _A				)
         if str(_A				).startswith('''mps'''				):
            __snake_case								=       torch.manual_seed(_A				)
         else:
            __snake_case								=       torch.Generator(device=_A				).manual_seed(_A				)
         __snake_case								=       {
             '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        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case								=       'cpu'

         __snake_case								=       self.get_dummy_components()

         __snake_case								=       self.pipeline_class(**_A				)
         __snake_case								=       pipe.to(_A				)

         pipe.set_progress_bar_config(disable=_A				)

         __snake_case								=       pipe(**self.get_dummy_inputs(_A				)				)
         __snake_case								=       output.images

         __snake_case								=       pipe(
             **self.get_dummy_inputs(_A				)    ,      return_dict=_A    ,      )[0]

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

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

         __snake_case								=       np.array(
             [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]				)

         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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				Tuple				):





         """simple docstring"""



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


      def        a			(self	:				Any				):





         """simple docstring"""



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

         __snake_case								=       KandinskyVaaPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-prior'''    ,      torch_dtype=torch.floataa				)
         pipe_prior.to(_A				)

         __snake_case								=       KandinskyVaaPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-decoder'''    ,      torch_dtype=torch.floataa				)
         __snake_case								=       pipeline.to(_A				)
         pipeline.set_progress_bar_config(disable=_A				)

         __snake_case								=       'red cat, 4k photo'

         __snake_case								=       torch.Generator(device='''cuda'''				).manual_seed(0				)
         __snake_case								=       pipe_prior(
             _A    ,      generator=_A    ,      num_inference_steps=5    ,      negative_prompt=''''''    ,      ).to_tuple()

         __snake_case								=       torch.Generator(device='''cuda'''				).manual_seed(0				)
         __snake_case								=       pipeline(
             image_embeds=_A    ,      negative_image_embeds=_A    ,      generator=_A    ,      num_inference_steps=100    ,      output_type='''np'''    ,      )

         __snake_case								=       output.images[0]

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

         assert_mean_pixel_difference(_A    ,      _A				)








from typing import TYPE_CHECKING

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


snake_case_           =      {}

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

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      ['NllbTokenizerFast']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb import NllbTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb_fast import NllbTokenizerFast

else:
    import sys

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

