
'''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    _a				(_lowerCamelCase  ,					unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  KandinskyVaaPipeline
       SCREAMING_SNAKE_CASE      =  [
           'image_embeds',
           'negative_image_embeds',
       ]
       SCREAMING_SNAKE_CASE      =  ['image_embeds', 'negative_image_embeds']
       SCREAMING_SNAKE_CASE      =  [
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       SCREAMING_SNAKE_CASE      =  False






       @property
       def        UpperCamelCase				(							self      )    ->			Dict:
          return 32






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          return 32






       @property
       def        UpperCamelCase				(							self      )    ->			List[str]:
          return self.time_input_dim






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          return self.time_input_dim * 4






       @property
       def        UpperCamelCase				(							self      )    ->			Any:
          return 1_00






       @property
       def        UpperCamelCase				(							self      )    ->			Any:
          torch.manual_seed(0      )

          _SCREAMING_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,
          }

          _SCREAMING_SNAKE_CASE      =				UNetaDConditionModel(**A__      )
          return model






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          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      )    ->			List[Any]:
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				VQModel(**self.dummy_movq_kwargs      )
          return model






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				self.dummy_unet
          _SCREAMING_SNAKE_CASE      =				self.dummy_movq

          _SCREAMING_SNAKE_CASE      =				DDIMScheduler(
              num_train_timesteps=10_00       ,							beta_schedule="""linear"""       ,							beta_start=0.0_0085       ,							beta_end=0.012       ,							clip_sample=A__       ,							set_alpha_to_one=A__       ,							steps_offset=1       ,							prediction_type="""epsilon"""       ,							thresholding=A__       ,							)

          _SCREAMING_SNAKE_CASE      =				{
              """unet""": unet,
              """scheduler""": scheduler,
              """movq""": movq,
          }
          return components






       def        UpperCamelCase				(							self       ,							A__       ,							A__=0      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				floats_tensor((1, self.text_embedder_hidden_size)       ,							rng=random.Random(A__      )      ).to(A__      )
          _SCREAMING_SNAKE_CASE      =				floats_tensor((1, self.text_embedder_hidden_size)       ,							rng=random.Random(seed + 1      )      ).to(
              A__      )
          if str(A__      ).startswith("""mps"""      ):
             _SCREAMING_SNAKE_CASE      =				torch.manual_seed(A__      )
          else:
             _SCREAMING_SNAKE_CASE      =				torch.Generator(device=A__      ).manual_seed(A__      )
          _SCREAMING_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        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				"""cpu"""

          _SCREAMING_SNAKE_CASE      =				self.get_dummy_components()

          _SCREAMING_SNAKE_CASE      =				self.pipeline_class(**A__      )
          _SCREAMING_SNAKE_CASE      =				pipe.to(A__      )

          pipe.set_progress_bar_config(disable=A__      )

          _SCREAMING_SNAKE_CASE      =				pipe(**self.get_dummy_inputs(A__      )      )
          _SCREAMING_SNAKE_CASE      =				output.images

          _SCREAMING_SNAKE_CASE      =				pipe(
              **self.get_dummy_inputs(A__      )       ,							return_dict=A__       ,							)[0]

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

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

          _SCREAMING_SNAKE_CASE      =				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    _a				(unittest.TestCase):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			List[str]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""      )

          _SCREAMING_SNAKE_CASE      =				KandinskyVaaPriorPipeline.from_pretrained(
              """kandinsky-community/kandinsky-2-2-prior"""       ,							torch_dtype=torch.floataa      )
          pipe_prior.to(A__      )

          _SCREAMING_SNAKE_CASE      =				KandinskyVaaPipeline.from_pretrained(
              """kandinsky-community/kandinsky-2-2-decoder"""       ,							torch_dtype=torch.floataa      )
          _SCREAMING_SNAKE_CASE      =				pipeline.to(A__      )
          pipeline.set_progress_bar_config(disable=A__      )

          _SCREAMING_SNAKE_CASE      =				"""red cat, 4k photo"""

          _SCREAMING_SNAKE_CASE      =				torch.Generator(device="""cuda"""      ).manual_seed(0      )
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				pipe_prior(
              A__       ,							generator=A__       ,							num_inference_steps=5       ,							negative_prompt=""""""       ,							).to_tuple()

          _SCREAMING_SNAKE_CASE      =				torch.Generator(device="""cuda"""      ).manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				pipeline(
              image_embeds=A__       ,							negative_image_embeds=A__       ,							generator=A__       ,							num_inference_steps=1_00       ,							output_type="""np"""       ,							)

          _SCREAMING_SNAKE_CASE      =				output.images[0]

          assert image.shape == (5_12, 5_12, 3)

          assert_mean_pixel_difference(A__       ,							A__      )








'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCamelCase__      :		str       							=  {
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		List[Any]       							=  [
         "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CanineForMultipleChoice",
         "CanineForQuestionAnswering",
         "CanineForSequenceClassification",
         "CanineForTokenClassification",
         "CanineLayer",
         "CanineModel",
         "CaninePreTrainedModel",
         "load_tf_weights_in_canine",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

     UpperCamelCase__      :		str       							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






