





import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

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


enable_full_determinism()

@skip_mps
class 		__a				(						__SCREAMING_SNAKE_CASE      ,unittest.TestCase   ):
       __snake_case    : Any   =		VideoToVideoSDPipeline
       __snake_case    : List[Any]   =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}   ) - {"""image""", """width""", """height"""}
       __snake_case    : List[str]   =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}   ) - {"""image"""}
       __snake_case    : List[str]   =		PipelineTesterMixin.required_optional_params - {"""latents"""}
       __snake_case    : int   =		False

       # No `output_type`.
       __snake_case    : List[Any]   =		frozenset(
           [
               """num_inference_steps""",
               """generator""",
               """latents""",
               """return_dict""",
               """callback""",
               """callback_steps""",
           ]   )



       def 			A				(  self						: List[Any]      ):
           torch.manual_seed(0      )
           lowerCAmelCase_       :						Optional[int]							= UNetaDConditionModel(
               block_out_channels=(32, 64, 64, 64)       ,      layers_per_block=2       ,      sample_size=32       ,      in_channels=4       ,      out_channels=4       ,      down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""")       ,      up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""")       ,      cross_attention_dim=32       ,      attention_head_dim=4       ,      )
           lowerCAmelCase_       :						Dict							= DDIMScheduler(
               beta_start=0.0_0085       ,      beta_end=0.012       ,      beta_schedule="""scaled_linear"""       ,      clip_sample=A_       ,      set_alpha_to_one=A_       ,      )
           torch.manual_seed(0      )
           lowerCAmelCase_       :						Optional[Any]							= 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       ,      sample_size=1_28       ,      )
           torch.manual_seed(0      )
           lowerCAmelCase_       :						Any							= CLIPTextConfig(
               bos_token_id=0       ,      eos_token_id=2       ,      hidden_size=32       ,      intermediate_size=37       ,      layer_norm_eps=1e-0_5       ,      num_attention_heads=4       ,      num_hidden_layers=5       ,      pad_token_id=1       ,      vocab_size=10_00       ,      hidden_act="""gelu"""       ,      projection_dim=5_12       ,      )
           lowerCAmelCase_       :						Optional[int]							= CLIPTextModel(A_      )
           lowerCAmelCase_       :						str							= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

           lowerCAmelCase_       :						Dict							= {
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
           }
           return components



       def 			A				(  self						: Any       ,      UpperCAmelCase						: Optional[int]       ,      UpperCAmelCase						: Any=0      ):
           lowerCAmelCase_       :						Optional[int]							= floats_tensor((1, 3, 3, 32, 32)       ,      rng=random.Random(A_      )      ).to(A_      )

           if str(A_      ).startswith("""mps"""      ):
               lowerCAmelCase_       :						List[Any]							= torch.manual_seed(A_      )
           else:
               lowerCAmelCase_       :						Union[str, Any]							= torch.Generator(device=A_      ).manual_seed(A_      )
           lowerCAmelCase_       :						str							= {
               """prompt""": """A painting of a squirrel eating a burger""",
               """video""": video,
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 6.0,
               """output_type""": """pt""",
           }
           return inputs



       def 			A				(  self						: Tuple      ):
           lowerCAmelCase_       :						str							= """cpu"""  # ensure determinism for the device-dependent torch.Generator
           lowerCAmelCase_       :						Union[str, Any]							= self.get_dummy_components()
           lowerCAmelCase_       :						Optional[Any]							= VideoToVideoSDPipeline(**A_      )
           lowerCAmelCase_       :						List[Any]							= sd_pipe.to(A_      )
           sd_pipe.set_progress_bar_config(disable=A_      )

           lowerCAmelCase_       :						List[str]							= self.get_dummy_inputs(A_      )
           lowerCAmelCase_       :						Optional[Any]							= """np"""
           lowerCAmelCase_       :						List[Any]							= sd_pipe(**A_      ).frames
           lowerCAmelCase_       :						Any							= frames[0][-3:, -3:, -1]

           assert frames[0].shape == (32, 32, 3)
           lowerCAmelCase_       :						Optional[Any]							= np.array([1_06, 1_17, 1_13, 1_74, 1_37, 1_12, 1_48, 1_51, 1_31]      )

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



       @unittest.skipIf(
           torch_device != """cuda""" or not is_xformers_available()       ,      reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,      )
       def 			A				(  self						: Dict      ):
           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_       ,      expected_max_diff=5e-3      )



       @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""      )
       def 			A				(  self						: List[Any]      ):
           pass



       @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""      )
       def 			A				(  self						: Any      ):
           pass



       @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""      )
       def 			A				(  self						: int      ):
           pass



       def 			A				(  self						: Union[str, Any]      ):
           return super().test_progress_bar()






@slow
@skip_mps
class 		__a				(						unittest.TestCase   ):



       def 			A				(  self						: Optional[int]      ):
           lowerCAmelCase_       :						Tuple							= VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL"""       ,      torch_dtype=torch.floataa      )
           pipe.enable_model_cpu_offload()

           # 10 frames
           lowerCAmelCase_       :						str							= torch.Generator(device="""cpu"""      ).manual_seed(0      )
           lowerCAmelCase_       :						Optional[Any]							= torch.randn((1, 10, 3, 10_24, 5_76)       ,      generator=A_      )
           lowerCAmelCase_       :						Tuple							= video.to("""cuda"""      )

           lowerCAmelCase_       :						List[str]							= """Spiderman is surfing"""

           lowerCAmelCase_       :						str							= pipe(A_       ,      video=A_       ,      generator=A_       ,      num_inference_steps=3       ,      output_type="""pt"""      ).frames

           lowerCAmelCase_       :						int							= np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656]      )
           assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array      ).sum() < 1e-2









class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			name
        SCREAMING_SNAKE_CASE__											=			value
        SCREAMING_SNAKE_CASE__											=			weight



    def __repr__(       self     ):





        '''simple docstring'''

        return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.name



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.weight




    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value / self.weight





def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[]
    for i in range(len(lowerCAmelCase_	)	):
        menu.append(Things(name[i] ,							value[i] ,							weight[i]	)	)
    return menu


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			sorted(lowerCAmelCase_ ,							key=lowerCAmelCase_ ,							reverse=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			0.0, 0.0
    for i in range(len(lowerCAmelCase_	)	):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]	)
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)


def 							__snake_case							(    )				->						str:
    pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
