

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 				lowercase_		(							self						:		Union[str, Any] )		->							Optional[int]:
       return 32






  @property
  def 				lowercase_		(							self						:		int )		->							List[str]:
       return 32






  @property
  def 				lowercase_		(							self						:		List[str] )		->							Union[str, Any]:
       return self.time_input_dim






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






  @property
  def 				lowercase_		(							self						:		Dict )		->							Optional[int]:
       return 100






  @property
  def 				lowercase_		(							self						:		Optional[Any] )		->							List[str]:
       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(**__lowerCamelCase )
       return model






  @property
  def 				lowercase_		(							self						:		List[str] )		->							str:
       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 				lowercase_		(							self						:		str )		->							Dict:
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__   =   VQModel(**self.dummy_movq_kwargs )
       return model






  def 				lowercase_		(							self						:		int )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   self.dummy_unet
       SCREAMING_SNAKE_CASE__   =   self.dummy_movq

       SCREAMING_SNAKE_CASE__   =   DDIMScheduler(
           num_train_timesteps=1000     ,       beta_schedule='''linear'''     ,       beta_start=0.00085     ,       beta_end=0.012     ,       clip_sample=__lowerCamelCase     ,       set_alpha_to_one=__lowerCamelCase     ,       steps_offset=1     ,       prediction_type='''epsilon'''     ,       thresholding=__lowerCamelCase     ,       )

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






  def 				lowercase_		(							self						:		Any     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		Union[str, Any]=0 )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   floats_tensor((1, self.text_embedder_hidden_size)     ,       rng=random.Random(seed + 1 ) ).to(
           __lowerCamelCase )
       if str(__lowerCamelCase ).startswith('''mps''' ):
            SCREAMING_SNAKE_CASE__   =   torch.manual_seed(__lowerCamelCase )
       else:
            SCREAMING_SNAKE_CASE__   =   torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
       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 				lowercase_		(							self						:		int )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   '''cpu'''

       SCREAMING_SNAKE_CASE__   =   self.get_dummy_components()

       SCREAMING_SNAKE_CASE__   =   self.pipeline_class(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   pipe.to(__lowerCamelCase )

       pipe.set_progress_bar_config(disable=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
       SCREAMING_SNAKE_CASE__   =   output.images

       SCREAMING_SNAKE_CASE__   =   pipe(
           **self.get_dummy_inputs(__lowerCamelCase )     ,       return_dict=__lowerCamelCase     ,       )[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(
           [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046] )

       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 				lowercase_		(							self						:		Optional[int] )		->							Tuple:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def 				lowercase_		(							self						:		Optional[Any] )		->							Union[str, Any]:
       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(__lowerCamelCase )

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

       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(
           __lowerCamelCase     ,       generator=__lowerCamelCase     ,       num_inference_steps=5     ,       negative_prompt=''''''     ,       ).to_tuple()

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

       SCREAMING_SNAKE_CASE__   =   output.images[0]

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

       assert_mean_pixel_difference(__lowerCamelCase     ,       __lowerCamelCase )





import warnings

from .generation import TFGenerationMixin






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  warnings.warn(
      "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
      "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."						,					A__						,					)


