

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			unittest.TestCase							):
  _a									=  KandinskyVaaImgaImgPipeline
  _a									=  ["""image_embeds""", """negative_image_embeds""", """image"""]
  _a									=  [
      """image_embeds""",
      """negative_image_embeds""",
      """image""",
  ]
  _a									=  [
      """generator""",
      """height""",
      """width""",
      """strength""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  _a									=  False




  @property
  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        return 32




  @property
  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        return 32




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






        return self.time_input_dim




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






        return self.time_input_dim * 4




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






        return 100




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






        torch.manual_seed(0					)

        _lowercase	       ={
            '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,
        }

        _lowercase	       =UNetaDConditionModel(**lowerCAmelCase					)
        return model




  @property
  def   A__     (				self					)  -> str:
        '''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					)  -> List[Any]:
        '''simple docstring'''






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




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






        _lowercase	       =self.dummy_unet
        _lowercase	       =self.dummy_movq

        _lowercase	       ={
            'num_train_timesteps': 1_000,
            'beta_schedule': 'linear',
            'beta_start': 0.00085,
            'beta_end': 0.012,
            'clip_sample': False,
            'set_alpha_to_one': False,
            'steps_offset': 0,
            'prediction_type': 'epsilon',
            'thresholding': False,
        }

        _lowercase	       =DDIMScheduler(**lowerCAmelCase					)

        _lowercase	       ={
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=0					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =floats_tensor((1, self.text_embedder_hidden_size)							,   rng=random.Random(lowerCAmelCase					)					).to(lowerCAmelCase					)
        _lowercase	       =floats_tensor((1, self.text_embedder_hidden_size)							,   rng=random.Random(seed + 1					)					).to(
            lowerCAmelCase					)
        # create init_image
        _lowercase	       =floats_tensor((1, 3, 64, 64)							,   rng=random.Random(lowerCAmelCase					)					).to(lowerCAmelCase					)
        _lowercase	       =image.cpu().permute(0							,   2							,   3							,   1					)[0]
        _lowercase	       =Image.fromarray(np.uinta(lowerCAmelCase					)					).convert('RGB'					).resize((256, 256)					)

        if str(lowerCAmelCase					).startswith('mps'					):
              _lowercase	       =torch.manual_seed(lowerCAmelCase					)
        else:
              _lowercase	       =torch.Generator(device=lowerCAmelCase					).manual_seed(lowerCAmelCase					)
        _lowercase	       ={
            'image': init_image,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 10,
            'guidance_scale': 7.0,
            'strength': 0.2,
            'output_type': 'np',
        }
        return inputs




  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        _lowercase	       ='cpu'

        _lowercase	       =self.get_dummy_components()

        _lowercase	       =self.pipeline_class(**lowerCAmelCase					)
        _lowercase	       =pipe.to(lowerCAmelCase					)

        pipe.set_progress_bar_config(disable=lowerCAmelCase					)

        _lowercase	       =pipe(**self.get_dummy_inputs(lowerCAmelCase					)					)
        _lowercase	       =output.images

        _lowercase	       =pipe(
            **self.get_dummy_inputs(lowerCAmelCase					)							,   return_dict=lowerCAmelCase							,   )[0]

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

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

        _lowercase	       =np.array(
            [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]					)
        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  __lowerCAmelCase     (					unittest.TestCase							):




  def   A__     (				self					)  -> str:
        '''simple docstring'''






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




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/kandinskyv22_img2img_frog.npy'					)

        _lowercase	       =load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
        _lowercase	       ='A red cartoon frog, 4k'

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

        _lowercase	       =KandinskyVaaImgaImgPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-decoder'							,   torch_dtype=torch.floataa					)
        _lowercase	       =pipeline.to(lowerCAmelCase					)

        pipeline.set_progress_bar_config(disable=lowerCAmelCase					)

        _lowercase	       =torch.Generator(device='cpu'					).manual_seed(0					)
        _lowercase       ,       _lowercase	       =pipe_prior(
            lowerCAmelCase							,   generator=lowerCAmelCase							,   num_inference_steps=5							,   negative_prompt=''							,   ).to_tuple()

        _lowercase	       =pipeline(
            image=lowerCAmelCase							,   image_embeds=lowerCAmelCase							,   negative_image_embeds=lowerCAmelCase							,   generator=lowerCAmelCase							,   num_inference_steps=100							,   height=768							,   width=768							,   strength=0.2							,   output_type='np'							,   )

        _lowercase	       =output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(lowerCAmelCase							,   lowerCAmelCase					)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_									=   {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['BloomTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BloomForCausalLM',
      'BloomModel',
      'BloomPreTrainedModel',
      'BloomForSequenceClassification',
      'BloomForTokenClassification',
      'BloomForQuestionAnswering',
  ]

if TYPE_CHECKING:
  from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bloom_fast import BloomTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bloom import (
        BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
        BloomForCausalLM,
        BloomForQuestionAnswering,
        BloomForSequenceClassification,
        BloomForTokenClassification,
        BloomModel,
        BloomPreTrainedModel,
    )

else:
  import sys

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


