

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			(  SCREAMING_SNAKE_CASE						,      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 UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      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				,     )
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(
         beta_start=0.00_085				,     beta_end=0.012				,     beta_schedule='scaled_linear'				,     clip_sample=snake_case__				,     set_alpha_to_one=snake_case__				,     )
     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      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     )
     SCREAMING_SNAKE_CASE		    =      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				,     )
     SCREAMING_SNAKE_CASE		    =      CLIPTextModel(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

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

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   str				,     snake_case__			:   int=0     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      floats_tensor((1, 3, 3, 3_2, 3_2)				,     rng=random.Random(snake_case__     )     ).to(snake_case__     )

     if str(snake_case__     ).startswith('mps'     ):
      SCREAMING_SNAKE_CASE		    =      torch.manual_seed(snake_case__     )
     else:
      SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         '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 UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      VideoToVideoSDPipeline(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      sd_pipe.to(snake_case__     )
     sd_pipe.set_progress_bar_config(disable=snake_case__     )

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

     assert frames[0].shape == (3_2, 3_2, 3)
     SCREAMING_SNAKE_CASE		    =      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 UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case__				,     expected_max_diff=5E-3     )

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

     """simple docstring"""






     pass

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

     """simple docstring"""






     pass

    @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'     )
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     pass




    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     return super().test_progress_bar()




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

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL'				,     torch_dtype=torch.floataa     )
     pipe.enable_model_cpu_offload()

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

     SCREAMING_SNAKE_CASE		    =      'Spiderman is surfing'

     SCREAMING_SNAKE_CASE		    =      pipe(snake_case__				,     video=snake_case__				,     generator=snake_case__				,     num_inference_steps=3				,     output_type='pt'     ).frames

     SCREAMING_SNAKE_CASE		    =      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 argparse
import os
import re


a_     :    List[str]     		=   "src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a_     :    Optional[Any]     		=   re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
a_     :    Optional[int]     		=   re.compile(R"\s*\(\s*\"(\S[^\"]+)\"")


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	bool = False						)		->				Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,				'r'		,				encoding='utf-8'						) as f:
  SCREAMING_SNAKE_CASE		    =      f.read()

 SCREAMING_SNAKE_CASE		    =      content.split('\n'						)
 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      0
 while line_idx < len(_UpperCamelCase						):
  if _re_intro_mapping.search(lines[line_idx]						) is not None:
   SCREAMING_SNAKE_CASE		    =      len(re.search(R'^(\s*)\S'		,				lines[line_idx]						).groups()[0]						) + 8
   # Start of a new mapping!
   while not lines[line_idx].startswith(' ' * indent + '('						):
    new_lines.append(lines[line_idx]						)
    line_idx += 1

   SCREAMING_SNAKE_CASE		    =      []
   while lines[line_idx].strip() != "]":
    # Blocks either fit in one line or not
    if lines[line_idx].strip() == "(":
     SCREAMING_SNAKE_CASE		    =      line_idx
     while not lines[line_idx].startswith(' ' * indent + ')'						):
      line_idx += 1
     blocks.append('\n'.join(lines[start_idx : line_idx + 1]						)						)
    else:
     blocks.append(lines[line_idx]						)
    line_idx += 1

   # Sort blocks by their identifiers
   SCREAMING_SNAKE_CASE		    =      sorted(_UpperCamelCase		,				key=lambda _UpperCamelCase						: _re_identifier.search(_UpperCamelCase						).groups()[0]						)
   new_lines += blocks
  else:
   new_lines.append(lines[line_idx]						)
   line_idx += 1

 if overwrite:
  with open(_UpperCamelCase		,				'w'		,				encoding='utf-8'						) as f:
   f.write('\n'.join(_UpperCamelCase						)						)
 elif "\n".join(_UpperCamelCase						) != content:
  return True


def 							__lowerCAmelCase     (							_UpperCamelCase		:	bool = False						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [os.path.join(_UpperCamelCase		,				_UpperCamelCase						) for f in os.listdir(_UpperCamelCase						) if f.endswith('.py'						)]
 SCREAMING_SNAKE_CASE		    =      [sort_auto_mapping(_UpperCamelCase		,				overwrite=_UpperCamelCase						) for fname in fnames]

 if not overwrite and any(_UpperCamelCase						):
  SCREAMING_SNAKE_CASE		    =      [f for f, d in zip(_UpperCamelCase		,				_UpperCamelCase						) if d]
  raise ValueError(
      f"""The following files have auto mappings that need sorting: {', '.join(_UpperCamelCase						)}. Run `make style` to fix"""
      ' this.'						)


if __name__ == "__main__":
      a_     :    Union[str, Any]     		=   argparse.ArgumentParser()
      parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
      a_     :    List[str]     		=   parser.parse_args()

      sort_all_auto_mappings(not args.check_only)

