'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					KandinskyVaaControlnetImgaImgPipeline
  __lowerCAmelCase				      =					["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
  __lowerCAmelCase				      =					["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
  __lowerCAmelCase				      =					[
      """generator""",
      """height""",
      """width""",
      """strength""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  __lowerCAmelCase				      =					False







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return 32







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return 32







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.time_input_dim







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.time_input_dim * 4







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return 100







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    torch.manual_seed(0					)

    __UpperCAmelCase:					Tuple		= {
        """in_channels""": 8,
        # Out channels is double in channels because predicts mean and variance
        """out_channels""": 8,
        """addition_embed_type""": """image_hint""",
        """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:					List[str]		= UNetaDConditionModel(**snake_case_					)
    return model







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return {
        "block_out_channels": [32, 32, 64, 64],
        "down_block_types": [
            "DownEncoderBlock2D",
            "DownEncoderBlock2D",
            "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
        "vq_embed_dim": 4,
    }







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    torch.manual_seed(0					)
    __UpperCAmelCase:					str		= VQModel(**self.dummy_movq_kwargs					)
    return model







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.dummy_unet
    __UpperCAmelCase:					Optional[Any]		= self.dummy_movq

    __UpperCAmelCase:					Dict		= {
        """num_train_timesteps""": 1000,
        """beta_schedule""": """linear""",
        """beta_start""": 0.0_0_0_8_5,
        """beta_end""": 0.0_1_2,
        """clip_sample""": False,
        """set_alpha_to_one""": False,
        """steps_offset""": 0,
        """prediction_type""": """epsilon""",
        """thresholding""": False,
    }

    __UpperCAmelCase:					Any		= DDIMScheduler(**snake_case_					)

    __UpperCAmelCase:					str		= {
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }

    return components







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_=0					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(snake_case_					)					).to(snake_case_					)
    __UpperCAmelCase:					int		= floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(seed + 1					)					).to(
        snake_case_					)
    # create init_image
    __UpperCAmelCase:					List[Any]		= floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(snake_case_					)					).to(snake_case_					)
    __UpperCAmelCase:					List[str]		= image.cpu().permute(0   ,    2   ,    3   ,    1					)[0]
    __UpperCAmelCase:					int		= Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					).resize((256, 256)					)
    # create hint
    __UpperCAmelCase:					Dict		= floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(snake_case_					)					).to(snake_case_					)

    if str(snake_case_					).startswith("""mps"""					):
      __UpperCAmelCase:					Union[str, Any]		= torch.manual_seed(snake_case_					)
    else:
      __UpperCAmelCase:					Optional[Any]		= torch.Generator(device=snake_case_					).manual_seed(snake_case_					)
    __UpperCAmelCase:					Any		= {
        """image""": init_image,
        """image_embeds""": image_embeds,
        """negative_image_embeds""": negative_image_embeds,
        """hint""": hint,
        """generator""": generator,
        """height""": 64,
        """width""": 64,
        """num_inference_steps""": 10,
        """guidance_scale""": 7.0,
        """strength""": 0.2,
        """output_type""": """np""",
    }
    return inputs







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= """cpu"""

    __UpperCAmelCase:					List[Any]		= self.get_dummy_components()

    __UpperCAmelCase:					int		= self.pipeline_class(**snake_case_					)
    __UpperCAmelCase:					str		= pipe.to(snake_case_					)

    pipe.set_progress_bar_config(disable=snake_case_					)

    __UpperCAmelCase:					List[Any]		= pipe(**self.get_dummy_inputs(snake_case_					)					)
    __UpperCAmelCase:					Any		= output.images

    __UpperCAmelCase:					List[Any]		= pipe(
        **self.get_dummy_inputs(snake_case_					)   ,    return_dict=snake_case_   ,    )[0]

    __UpperCAmelCase:					Optional[Any]		= image[0, -3:, -3:, -1]
    __UpperCAmelCase:					Union[str, Any]		= image_from_tuple[0, -3:, -3:, -1]

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

    __UpperCAmelCase:					str		= np.array(
        [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_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    a (       unittest.TestCase				):



  """simple docstring"""







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""					)

    __UpperCAmelCase:					Any		= load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
    __UpperCAmelCase:					Dict		= init_image.resize((512, 512)					)

    __UpperCAmelCase:					int		= load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinskyv22/hint_image_cat.png"""					)
    __UpperCAmelCase:					Any		= torch.from_numpy(np.array(snake_case_					)					).float() / 2_5_5.0
    __UpperCAmelCase:					List[Any]		= hint.permute(2   ,    0   ,    1					).unsqueeze(0					)

    __UpperCAmelCase:					str		= """A robot, 4k photo"""

    __UpperCAmelCase:					Tuple		= KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-prior"""   ,    torch_dtype=torch.floataa					)
    pipe_prior.to(snake_case_					)

    __UpperCAmelCase:					Tuple		= KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-controlnet-depth"""   ,    torch_dtype=torch.floataa					)
    __UpperCAmelCase:					Tuple		= pipeline.to(snake_case_					)

    pipeline.set_progress_bar_config(disable=snake_case_					)

    __UpperCAmelCase:					List[str]		= torch.Generator(device="""cpu"""					).manual_seed(0					)

    __UpperCAmelCase, __UpperCAmelCase:					Optional[int]		= pipe_prior(
        snake_case_   ,    image=snake_case_   ,    strength=0.8_5   ,    generator=snake_case_   ,    negative_prompt=""""""   ,    ).to_tuple()

    __UpperCAmelCase:					Tuple		= pipeline(
        image=snake_case_   ,    image_embeds=snake_case_   ,    negative_image_embeds=snake_case_   ,    hint=snake_case_   ,    generator=snake_case_   ,    num_inference_steps=100   ,    height=512   ,    width=512   ,    strength=0.5   ,    output_type="""np"""   ,    )

    __UpperCAmelCase:					Optional[int]		= output.images[0]

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

    assert_mean_pixel_difference(snake_case_   ,    snake_case_					)
'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			{
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					"""timesformer"""







  def __init__(							self   ,    snake_case_=224   ,    snake_case_=16   ,    snake_case_=3   ,    snake_case_=8   ,    snake_case_=768   ,    snake_case_=12   ,    snake_case_=12   ,    snake_case_=3072   ,    snake_case_="gelu"   ,    snake_case_=0.0   ,    snake_case_=0.0   ,    snake_case_=0.0_2   ,    snake_case_=1e-6   ,    snake_case_=True   ,    snake_case_="divided_space_time"   ,    snake_case_=0   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(**snake_case_					)

    __UpperCAmelCase:					Tuple		= image_size
    __UpperCAmelCase:					List[Any]		= patch_size
    __UpperCAmelCase:					Optional[Any]		= num_channels
    __UpperCAmelCase:					int		= num_frames

    __UpperCAmelCase:					List[str]		= hidden_size
    __UpperCAmelCase:					List[str]		= num_hidden_layers
    __UpperCAmelCase:					Dict		= num_attention_heads
    __UpperCAmelCase:					List[str]		= intermediate_size
    __UpperCAmelCase:					Union[str, Any]		= hidden_act
    __UpperCAmelCase:					Dict		= hidden_dropout_prob
    __UpperCAmelCase:					Optional[Any]		= attention_probs_dropout_prob
    __UpperCAmelCase:					Union[str, Any]		= initializer_range
    __UpperCAmelCase:					List[Any]		= layer_norm_eps
    __UpperCAmelCase:					int		= qkv_bias

    __UpperCAmelCase:					str		= attention_type
    __UpperCAmelCase:					Tuple		= drop_path_rate