





'''simple docstring'''


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     lowerCAmelCase__ (				UpperCAmelCase_      , unittest.TestCase    ):

       '''simple docstring'''
       _lowerCamelCase								=VideoToVideoSDPipeline
       _lowerCamelCase								=TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}    ) - {"image", "width", "height"}
       _lowerCamelCase								=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}    ) - {"image"}
       _lowerCamelCase								=PipelineTesterMixin.required_optional_params - {"latents"}
       _lowerCamelCase								=False

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

       def 			__snake_case      ( self					:				List[str]						):
              torch.manual_seed(0						)
              UpperCAmelCase  =						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       ,     )
              UpperCAmelCase  =						DDIMScheduler(
                  beta_start=0.00_085       ,     beta_end=0.012       ,     beta_schedule='''scaled_linear'''       ,     clip_sample=a__       ,     set_alpha_to_one=a__       ,     )
              torch.manual_seed(0						)
              UpperCAmelCase  =						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=128       ,     )
              torch.manual_seed(0						)
              UpperCAmelCase  =						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=1000       ,     hidden_act='''gelu'''       ,     projection_dim=512       ,     )
              UpperCAmelCase  =						CLIPTextModel(a__						)
              UpperCAmelCase  =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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

       def 			__snake_case      ( self					:				Any       ,     a__					:				int       ,     a__					:				int=0						):
              # 3 frames
              UpperCAmelCase  =						floats_tensor((1, 3, 3, 32, 32)       ,     rng=random.Random(a__						)						).to(a__						)

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

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

              assert frames[0].shape == (32, 32, 3)
              UpperCAmelCase  =						np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]						)

              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					:				Tuple						):
              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					:				List[str]						):
              pass

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

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



       def 			__snake_case      ( self					:				List[str]						):
              return super().test_progress_bar()






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

       '''simple docstring'''

       def 			__snake_case      ( self					:				List[str]						):
              UpperCAmelCase  =						VideoToVideoSDPipeline.from_pretrained('''cerspense/zeroscope_v2_XL'''       ,     torch_dtype=torch.floataa						)
              pipe.enable_model_cpu_offload()

              # 10 frames
              UpperCAmelCase  =						torch.Generator(device='''cpu'''						).manual_seed(0						)
              UpperCAmelCase  =						torch.randn((1, 10, 3, 1024, 576)       ,     generator=a__						)
              UpperCAmelCase  =						video.to('''cuda'''						)

              UpperCAmelCase  =						'''Spiderman is surfing'''

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

              UpperCAmelCase  =						np.array([-1.0_458_984, -1.1_279_297, -0.9_663_086, -0.91_503_906, -0.75_097_656]						)
              assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array						).sum() < 1e-2








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a							     =					{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
         'EncodecModel',
         'EncodecPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_encodec import (
         ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EncodecConfig,
     )
     from .feature_extraction_encodec import EncodecFeatureExtractor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_encodec import (
              ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
              EncodecModel,
              EncodecPreTrainedModel,
          )

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)