





'''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










import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__a:     List[Any]   					=					(720, 1280)  # Height, Width
__a:     str   					=					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__a:     Optional[Any]   					=					1 / 100
__a:     Optional[Any]   					=					''''''
__a:     Tuple   					=					''''''
__a:     List[Any]   					=					''''''
__a:     Union[str, Any]   					=					250




def       _SCREAMING_SNAKE_CASE (							)				->						None:
       _UpperCAmelCase	,			_UpperCAmelCase							=    get_dataset(__snake_case   ,      __snake_case )
       for index in range(__snake_case ):
              _UpperCAmelCase							=    random.sample(range(len(__snake_case ) )   ,      4 )
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    update_image_and_anno(
                  __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      filter_scale=__snake_case   ,      )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              _UpperCAmelCase							=    random_chars(3_2 )
              _UpperCAmelCase							=    path.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              _UpperCAmelCase							=    f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""   ,      __snake_case   ,      [cva.IMWRITE_JPEG_QUALITY, 8_5] )
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
              _UpperCAmelCase							=    []
              for anno in new_annos:
                     _UpperCAmelCase							=    anno[3] - anno[1]
                     _UpperCAmelCase							=    anno[4] - anno[2]
                     _UpperCAmelCase							=    anno[1] + width / 2
                     _UpperCAmelCase							=    anno[2] + height / 2
                     _UpperCAmelCase							=    f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(__snake_case )
              with open(f"""{file_root}.txt"""   ,      """w""" ) as outfile:
                     outfile.write("""\n""".join(line for line in annos_list ) )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						tuple[list, list]:
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for label_file in glob.glob(os.path.join(__snake_case   ,      """*.txt""" ) ):
              _UpperCAmelCase							=    label_file.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              with open(__snake_case ) as in_file:
                     _UpperCAmelCase							=    in_file.readlines()
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      f"""{label_name}.jpg""" )

              _UpperCAmelCase							=    []
              for obj_list in obj_lists:
                     _UpperCAmelCase							=    obj_list.rstrip("""\n""" ).split(""" """ )
                     _UpperCAmelCase							=    float(obj[1] ) - float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) - float(obj[4] ) / 2
                     _UpperCAmelCase							=    float(obj[1] ) + float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) + float(obj[4] ) / 2

                     boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
              if not boxes:
                     continue
              img_paths.append(__snake_case )
              labels.append(__snake_case )
       return img_paths, labels




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case = 0.0   ,      )				->						tuple[list, list, str]:
       _UpperCAmelCase							=    np.zeros([output_size[0], output_size[1], 3]   ,      dtype=np.uinta )
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    int(scale_x * output_size[1] )
       _UpperCAmelCase							=    int(scale_y * output_size[0] )

       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for i, index in enumerate(__snake_case ):
              _UpperCAmelCase							=    all_img_list[index]
              path_list.append(__snake_case )
              _UpperCAmelCase							=    all_annos[index]
              _UpperCAmelCase							=    cva.imread(__snake_case )
              if i == 0:  # top-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 1:  # top-right
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (output_size[1] - divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 2:  # bottom-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              else:  # bottom-right
                     _UpperCAmelCase							=    cva.resize(
                         __snake_case   ,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )

    # Remove bounding box small than scale of filter
       if filter_scale > 0:
              _UpperCAmelCase							=    [
                  anno
                  for anno in new_anno
                  if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
              ]

       return output_img, new_anno, path_list[0]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						str:
       assert number_char > 1, "The number of character should greater than 1"
       _UpperCAmelCase							=    ascii_lowercase + digits
       return "".join(random.choice(__snake_case ) for _ in range(__snake_case ) )


if __name__ == "__main__":
  main()
  print('''DONE ✅''')