



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      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					VideoToVideoSDPipeline
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'''video'''}  ) - {'''image''', '''width''', '''height'''}
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''video'''}  ) - {'''image'''}
     UpperCamelCase__		     =					PipelineTesterMixin.required_optional_params - {'''latents'''}
     UpperCamelCase__		     =					False

     # No `output_type`.
     UpperCamelCase__		     =					frozenset(
         [
             '''num_inference_steps''',
             '''generator''',
             '''latents''',
             '''return_dict''',
             '''callback''',
             '''callback_steps''',
         ]  )




     def 		snake_case_		(					self):
         torch.manual_seed(0)
         A__					=       UNetaDConditionModel(
             block_out_channels=(3_2, 6_4, 6_4, 6_4)			,    layers_per_block=2			,    sample_size=3_2			,    in_channels=4			,    out_channels=4			,    down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''')			,    up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''')			,    cross_attention_dim=3_2			,    attention_head_dim=4			,    )
         A__					=       DDIMScheduler(
             beta_start=0.0_0_0_8_5			,    beta_end=0.0_1_2			,    beta_schedule='''scaled_linear'''			,    clip_sample=a__			,    set_alpha_to_one=a__			,    )
         torch.manual_seed(0)
         A__					=       AutoencoderKL(
             block_out_channels=[3_2, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,    latent_channels=4			,    sample_size=1_2_8			,    )
         torch.manual_seed(0)
         A__					=       CLIPTextConfig(
             bos_token_id=0			,    eos_token_id=2			,    hidden_size=3_2			,    intermediate_size=3_7			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1_0_0_0			,    hidden_act='''gelu'''			,    projection_dim=5_1_2			,    )
         A__					=       CLIPTextModel(a__)
         A__					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

         A__					=       {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
         }
         return components




     def 		snake_case_		(					self			,    a__			,    a__=0):
         # 3 frames
         A__					=       floats_tensor((1, 3, 3, 3_2, 3_2)			,    rng=random.Random(a__)).to(a__)

         if str(a__).startswith('''mps'''):
             A__					=       torch.manual_seed(a__)
         else:
             A__					=       torch.Generator(device=a__).manual_seed(a__)
         A__					=       {
             '''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 		snake_case_		(					self):
         A__					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         A__					=       self.get_dummy_components()
         A__					=       VideoToVideoSDPipeline(**a__)
         A__					=       sd_pipe.to(a__)
         sd_pipe.set_progress_bar_config(disable=a__)

         A__					=       self.get_dummy_inputs(a__)
         A__					=       '''np'''
         A__					=       sd_pipe(**a__).frames
         A__					=       frames[0][-3:, -3:, -1]

         assert frames[0].shape == (3_2, 3_2, 3)
         A__					=       np.array([1_0_6, 1_1_7, 1_1_3, 1_7_4, 1_3_7, 1_1_2, 1_4_8, 1_5_1, 1_3_1])

         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 		snake_case_		(					self):
         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 		snake_case_		(					self):
         pass




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




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




     def 		snake_case_		(					self):
         return super().test_progress_bar()







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




     def 		snake_case_		(					self):
         A__					=       VideoToVideoSDPipeline.from_pretrained('''cerspense/zeroscope_v2_XL'''			,    torch_dtype=torch.floataa)
         pipe.enable_model_cpu_offload()

         # 10 frames
         A__					=       torch.Generator(device='''cpu''').manual_seed(0)
         A__					=       torch.randn((1, 1_0, 3, 1_0_2_4, 5_7_6)			,    generator=a__)
         A__					=       video.to('''cuda''')

         A__					=       '''Spiderman is surfing'''

         A__					=       pipe(a__			,    video=a__			,    generator=a__			,    num_inference_steps=3			,    output_type='''pt''').frames

         A__					=       np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6])
         assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array).sum() < 1e-2





import re



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	str:

    if len(re.findall('''[ATCG]'''			,							UpperCamelCase_						)						) != len(UpperCamelCase_						):
        raise ValueError('''Invalid Strand'''						)

    return dna.translate(dna.maketrans('''ATCG'''			,							'''TAGC'''						)						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
