




'''simple docstring'''





import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=256	):
    _a							:       Tuple       					=			min(img.size	)

    if s < target_image_size:
        raise ValueError(F'''min dim for image {s} < {target_image_size}'''	)

    _a							:       Tuple       					=			target_image_size / s
    _a							:       Optional[int]       					=			(round(r * img.size[1]	), round(r * img.size[0]	))
    _a							:       Any       					=			TF.resize(UpperCAmelCase_      ,  UpperCAmelCase_      ,  interpolation=PIL.Image.LANCZOS	)
    _a							:       Tuple       					=			TF.center_crop(UpperCAmelCase_      ,  output_size=2 * [target_image_size]	)
    _a							:       Union[str, Any]       					=			torch.unsqueeze(T.ToTensor()(UpperCAmelCase_	)      ,  0	)
    return img





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       int       					=			2.0 * x - 1.0
    return x





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=True      ,  UpperCAmelCase_="RGB"	):
    _a							:       Dict       					=			x.detach().cpu()
    if process:
        _a							:       List[str]       					=			post_process_tensor(UpperCAmelCase_	)
    _a							:       Union[str, Any]       					=			x.numpy()
    if process:
        _a							:       List[str]       					=			(255 * x).astype(np.uinta	)
    _a							:       List[str]       					=			Image.fromarray(UpperCAmelCase_	)
    if not x.mode == mode:
        _a							:       Union[str, Any]       					=			x.convert(UpperCAmelCase_	)
    return x





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       List[Any]       					=			torch.clamp(UpperCAmelCase_      ,  -1.0      ,  1.0	)
    _a							:       Optional[int]       					=			(x + 1.0) / 2.0
    _a							:       Optional[Any]       					=			x.permute(1      ,  2      ,  0	)
    return x





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       str       					=			post_process_tensor(x.squeeze()	)
    return x.permute(2      ,  0      ,  1	).unsqueeze(0	)








'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




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



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			"""runwayml/stable-diffusion-v1-5"""
      _a							:       Tuple       					=			TextToVideoZeroPipeline.from_pretrained(__lowerCAmelCase		,    torch_dtype=torch.floataa   ).to("""cuda"""   )
      _a							:       Dict       					=			DDIMScheduler.from_config(pipe.scheduler.config   )
      _a							:       int       					=			torch.Generator(device="""cuda"""   ).manual_seed(0   )

      _a							:       Union[str, Any]       					=			"""A bear is playing a guitar on Times Square"""
      _a							:       List[Any]       					=			pipe(prompt=__lowerCAmelCase		,    generator=__lowerCAmelCase   ).images

      _a							:       Any       					=			load_pt(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"""   )

      assert_mean_pixel_difference(__lowerCAmelCase		,    __lowerCAmelCase   )


