

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 SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=VideoToVideoSDPipeline
  lowerCamelCase   :      Tuple										=TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}			) - {"image", "width", "height"}
  lowerCamelCase   :      str										=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}			) - {"image"}
  lowerCamelCase   :      List[Any]										=PipelineTesterMixin.required_optional_params - {"latents"}
  lowerCamelCase   :      Optional[int]										=False

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Any:






     """simple docstring"""







     torch.manual_seed(0   )
     __lowerCAmelCase  :      List[Any]    =						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  :      Optional[int]    =						DDIMScheduler(
         beta_start=0.0_0085	,							beta_end=0.012	,							beta_schedule="""scaled_linear"""	,							clip_sample=lowerCAmelCase	,							set_alpha_to_one=lowerCAmelCase	,							)
     torch.manual_seed(0   )
     __lowerCAmelCase  :      Union[str, 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  :      List[Any]    =						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	,							hidden_act="""gelu"""	,							projection_dim=5_12	,							)
     __lowerCAmelCase  :      List[str]    =						CLIPTextModel(lowerCAmelCase   )
     __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 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Optional[int]=0   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						floats_tensor((1, 3, 3, 32, 32)	,							rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )

     if str(lowerCAmelCase   ).startswith("""mps"""   ):
        __lowerCAmelCase  :      Optional[int]    =						torch.manual_seed(lowerCAmelCase   )
     else:
        __lowerCAmelCase  :      str    =						torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						{
         """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 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[int]:






     """simple docstring"""







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

     __lowerCAmelCase  :      Union[str, Any]    =						self.get_dummy_inputs(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						"""np"""
     __lowerCAmelCase  :      Optional[Any]    =						sd_pipe(**lowerCAmelCase   ).frames
     __lowerCAmelCase  :      Any    =						frames[0][-3:, -3:, -1]

     assert frames[0].shape == (32, 32, 3)
     __lowerCAmelCase  :      Dict    =						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 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Tuple:






     """simple docstring"""







     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCAmelCase	,							expected_max_diff=5e-3   )







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






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







  @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     List[Any]:






     """simple docstring"""







     return super().test_progress_bar()






@slow
@skip_mps
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL"""	,							torch_dtype=torch.floataa   )
     pipe.enable_model_cpu_offload()

     # 10 frames
     __lowerCAmelCase  :      Dict    =						torch.Generator(device="""cpu"""   ).manual_seed(0   )
     __lowerCAmelCase  :      Dict    =						torch.randn((1, 10, 3, 10_24, 5_76)	,							generator=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						video.to("""cuda"""   )

     __lowerCAmelCase  :      List[Any]    =						"""Spiderman is surfing"""

     __lowerCAmelCase  :      Any    =						pipe(lowerCAmelCase	,							video=lowerCAmelCase	,							generator=lowerCAmelCase	,							num_inference_steps=3	,							output_type="""pt"""   ).frames

     __lowerCAmelCase  :      Tuple    =						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





from pathlib import Path

import fire





def      snake_case_				(__A					:						str ,       __A					:						str ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   dest_dir.mkdir(exist_ok=__A					)
   for path in src_dir.iterdir():
      __lowerCAmelCase  :      str    =						[x.rstrip() for x in list(path.open().readlines()					)][:n]
      __lowerCAmelCase  :      Dict    =						dest_dir.joinpath(path.name					)
      print(__A					)
      dest_path.open("""w"""					).write("""\n""".join(__A					)					)


if __name__ == "__main__":
   fire.Fire(minify)


