





"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					_UpperCAmelCase   (	lowercase_     ,							lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							CycleDiffusionPipeline
					UpperCamelCase      =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
					    '''negative_prompt''',
					    '''height''',
					    '''width''',
					    '''negative_prompt_embeds''',
					}
					UpperCamelCase      =							PipelineTesterMixin.required_optional_params - {'''latents'''}
					UpperCamelCase      =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''} )
					UpperCamelCase      =							IMAGE_TO_IMAGE_IMAGE_PARAMS
					UpperCamelCase      =							IMAGE_TO_IMAGE_IMAGE_PARAMS


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												torch.manual_seed(0		)
												A		=  UNetaDConditionModel(
												    block_out_channels=(32, 64)     ,       layers_per_block=2     ,       sample_size=32     ,       in_channels=4     ,       out_channels=4     ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,       cross_attention_dim=32     ,       )
												A		=  DDIMScheduler(
												    beta_start=0.00_085     ,       beta_end=0.012     ,       beta_schedule="scaled_linear"     ,       num_train_timesteps=10_00     ,       clip_sample=__UpperCamelCase     ,       set_alpha_to_one=__UpperCamelCase     ,       )
												torch.manual_seed(0		)
												A		=  AutoencoderKL(
												    block_out_channels=[32, 64]     ,       in_channels=3     ,       out_channels=3     ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,       latent_channels=4     ,       )
												torch.manual_seed(0		)
												A		=  CLIPTextConfig(
												    bos_token_id=0     ,       eos_token_id=2     ,       hidden_size=32     ,       intermediate_size=37     ,       layer_norm_eps=1e-05     ,       num_attention_heads=4     ,       num_hidden_layers=5     ,       pad_token_id=1     ,       vocab_size=10_00     ,       )
												A		=  CLIPTextModel(__UpperCamelCase		)
												A		=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

												A		=  {
												    "unet": unet,
												    "scheduler": scheduler,
												    "vae": vae,
												    "text_encoder": text_encoder,
												    "tokenizer": tokenizer,
												    "safety_checker": None,
												    "feature_extractor": None,
												}
												return components


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Any=0		):
												A		=  floats_tensor((1, 3, 32, 32)     ,       rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)
												A		=  image / 2 + 0.5
												if str(__UpperCamelCase		).startswith("mps"		):
																			A		=  torch.manual_seed(__UpperCamelCase		)
												else:
																			A		=  torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
												A		=  {
												    "prompt": "An astronaut riding an elephant",
												    "source_prompt": "An astronaut riding a horse",
												    "image": image,
												    "generator": generator,
												    "num_inference_steps": 2,
												    "eta": 0.1,
												    "strength": 0.8,
												    "guidance_scale": 3,
												    "source_guidance_scale": 1,
												    "output_type": "numpy",
												}
												return inputs


					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  "cpu"  # ensure determinism for the device-dependent torch.Generator

												A		=  self.get_dummy_components()
												A		=  CycleDiffusionPipeline(**__UpperCamelCase		)
												A		=  pipe.to(__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs(__UpperCamelCase		)
												A		=  pipe(**__UpperCamelCase		)
												A		=  output.images

												A		=  images[0, -3:, -3:, -1]

												assert images.shape == (1, 32, 32, 3)
												A		=  np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2


					@unittest.skipIf(torch_device != "cuda"     ,       "This test requires a GPU"		)
					def 	lowerCamelCase       (					self			:Any		):
												A		=  self.get_dummy_components()
												for name, module in components.items():
																			if hasattr(__UpperCamelCase     ,       "half"		):
																										A		=  module.half()
												A		=  CycleDiffusionPipeline(**__UpperCamelCase		)
												A		=  pipe.to(__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs(__UpperCamelCase		)
												A		=  pipe(**__UpperCamelCase		)
												A		=  output.images

												A		=  images[0, -3:, -3:, -1]

												assert images.shape == (1, 32, 32, 3)
												A		=  np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2


					@skip_mps
					def 	lowerCamelCase       (					self			:Any		):
												return super().test_save_load_local()


					@unittest.skip("non-deterministic pipeline"		)
					def 	lowerCamelCase       (					self			:Tuple		):
												return super().test_inference_batch_single_identical()


					@skip_mps
					def 	lowerCamelCase       (					self			:List[Any]		):
												return super().test_dict_tuple_outputs_equivalent()


					@skip_mps
					def 	lowerCamelCase       (					self			:Tuple		):
												return super().test_save_load_optional_components()


					@skip_mps
					def 	lowerCamelCase       (					self			:Tuple		):
												return super().test_attention_slicing_forward_pass()







@slow
@require_torch_gpu
class 					_UpperCAmelCase   (	unittest.TestCase ):


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


					def 	lowerCamelCase       (					self			:List[str]		):
												A		=  load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/cycle-diffusion/black_colored_car.png"		)
												A		=  load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"		)
												A		=  init_image.resize((5_12, 5_12)		)

												A		=  "CompVis/stable-diffusion-v1-4"
												A		=  DDIMScheduler.from_pretrained(__UpperCamelCase     ,       subfolder="scheduler"		)
												A		=  CycleDiffusionPipeline.from_pretrained(
												    __UpperCamelCase     ,       scheduler=__UpperCamelCase     ,       safety_checker=__UpperCamelCase     ,       torch_dtype=torch.floataa     ,       revision="fp16"		)

												pipe.to(__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)
												pipe.enable_attention_slicing()

												A		=  "A black colored car"
												A		=  "A blue colored car"

												A		=  torch.manual_seed(0		)
												A		=  pipe(
												    prompt=__UpperCamelCase     ,       source_prompt=__UpperCamelCase     ,       image=__UpperCamelCase     ,       num_inference_steps=1_00     ,       eta=0.1     ,       strength=0.85     ,       guidance_scale=3     ,       source_guidance_scale=1     ,       generator=__UpperCamelCase     ,       output_type="np"     ,       )
												A		=  output.images

												# the values aren't exactly equal, but the images look the same visually
												assert np.abs(image - expected_image		).max() < 5e-1


					def 	lowerCamelCase       (					self			:Any		):
												A		=  load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/cycle-diffusion/black_colored_car.png"		)
												A		=  load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"		)
												A		=  init_image.resize((5_12, 5_12)		)

												A		=  "CompVis/stable-diffusion-v1-4"
												A		=  DDIMScheduler.from_pretrained(__UpperCamelCase     ,       subfolder="scheduler"		)
												A		=  CycleDiffusionPipeline.from_pretrained(__UpperCamelCase     ,       scheduler=__UpperCamelCase     ,       safety_checker=__UpperCamelCase		)

												pipe.to(__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)
												pipe.enable_attention_slicing()

												A		=  "A black colored car"
												A		=  "A blue colored car"

												A		=  torch.manual_seed(0		)
												A		=  pipe(
												    prompt=__UpperCamelCase     ,       source_prompt=__UpperCamelCase     ,       image=__UpperCamelCase     ,       num_inference_steps=1_00     ,       eta=0.1     ,       strength=0.85     ,       guidance_scale=3     ,       source_guidance_scale=1     ,       generator=__UpperCamelCase     ,       output_type="np"     ,       )
												A		=  output.images

												assert np.abs(image - expected_image		).max() < 2e-2









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case     :		Optional[int]       						= logging.get_logger(__name__)

_snake_case     :		Optional[int]       						= {
    'google/vivit-b-16x2-kinetics400': (
        'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''vivit'''


					def __init__(					self			:Optional[Any]     ,       __UpperCamelCase			:Dict=2_24     ,       __UpperCamelCase			:int=32     ,       __UpperCamelCase			:Union[str, Any]=[2, 16, 16]     ,       __UpperCamelCase			:Optional[Any]=3     ,       __UpperCamelCase			:Optional[Any]=7_68     ,       __UpperCamelCase			:Any=12     ,       __UpperCamelCase			:List[str]=12     ,       __UpperCamelCase			:List[str]=30_72     ,       __UpperCamelCase			:Any="gelu_fast"     ,       __UpperCamelCase			:List[Any]=0.0     ,       __UpperCamelCase			:str=0.0     ,       __UpperCamelCase			:Dict=0.02     ,       __UpperCamelCase			:Optional[Any]=1e-06     ,       __UpperCamelCase			:Dict=True     ,       **__UpperCamelCase			:Tuple     ,       ):
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps

												A		=  image_size
												A		=  num_frames
												A		=  tubelet_size
												A		=  num_channels
												A		=  qkv_bias

												super().__init__(**__UpperCamelCase		)


