


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import VivitImageProcessor







class 		_snake_case  (      unittest.TestCase							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase=7					,  _lowerCamelCase=3					,  _lowerCamelCase=10					,  _lowerCamelCase=18					,  _lowerCamelCase=30					,  _lowerCamelCase=400					,  _lowerCamelCase=True					,  _lowerCamelCase=None					,  _lowerCamelCase=True					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  _lowerCamelCase=[0.5, 0.5, 0.5]					,  _lowerCamelCase=None					,  ):
       a						:Any 							=       size if size is not None else {'''shortest_edge''': 18}
       a						:Optional[int] 							=       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

       a						:Tuple 							=       parent
       a						:str 							=       batch_size
       a						:Any 							=       num_channels
       a						:Optional[Any] 							=       num_frames
       a						:Dict 							=       image_size
       a						:List[Any] 							=       min_resolution
       a						:Dict 							=       max_resolution
       a						:Any 							=       do_resize
       a						:Tuple 							=       size
       a						:Tuple 							=       do_normalize
       a						:Optional[int] 							=       image_mean
       a						:int 							=       image_std
       a						:Dict 							=       crop_size




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }







@require_torch
@require_vision
class 		_snake_case  (      _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       VivitImageProcessor if is_vision_available() else None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       VivitImageProcessingTester(self    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_mean'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''image_std'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_normalize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_resize'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''do_center_crop'''    )    )
       self.assertTrue(hasattr(_lowerCamelCase					,  '''size'''    )    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size					,  {'''shortest_edge''': 18}    )
       self.assertEqual(image_processor.crop_size					,  {'''height''': 18, '''width''': 18}    )

       a						:str 							=       self.image_processing_class.from_dict(self.image_processor_dict					,  size=42					,  crop_size=84    )
       self.assertEqual(image_processor.size					,  {'''shortest_edge''': 42}    )
       self.assertEqual(image_processor.crop_size					,  {'''height''': 84, '''width''': 84}    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:List[Any] 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random PIL videos
       a						:Union[str, Any] 							=       prepare_video_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase    )
       for video in video_inputs:
             self.assertIsInstance(_lowerCamelCase					,  _lowerCamelCase    )
             self.assertIsInstance(video[0]					,  Image.Image    )

       # Test not batched input
       a						:List[str] 							=       image_processing(video_inputs[0]					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_videos.shape					,  (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,  )

       # Test batched
       a						:List[str] 							=       image_processing(_lowerCamelCase					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_videos.shape					,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,  )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:Optional[int] 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       a						:Any 							=       prepare_video_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase					,  numpify=_lowerCamelCase    )
       for video in video_inputs:
             self.assertIsInstance(_lowerCamelCase					,  _lowerCamelCase    )
             self.assertIsInstance(video[0]					,  np.ndarray    )

       # Test not batched input
       a						:List[Any] 							=       image_processing(video_inputs[0]					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_videos.shape					,  (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,  )

       # Test batched
       a						:str 							=       image_processing(_lowerCamelCase					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_videos.shape					,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,  )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Initialize image_processing
       a						:int 							=       self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       a						:Dict 							=       prepare_video_inputs(self.image_processor_tester					,  equal_resolution=_lowerCamelCase					,  torchify=_lowerCamelCase    )
       for video in video_inputs:
             self.assertIsInstance(_lowerCamelCase					,  _lowerCamelCase    )
             self.assertIsInstance(video[0]					,  torch.Tensor    )

       # Test not batched input
       a						:Optional[int] 							=       image_processing(video_inputs[0]					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_videos.shape					,  (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,  )

       # Test batched
       a						:Any 							=       image_processing(_lowerCamelCase					,  return_tensors='''pt'''    ).pixel_values
       self.assertEqual(
           encoded_videos.shape					,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,  )










"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    DDIMScheduler(
        timesteps=config.model.params.timesteps     ,       beta_schedule='''scaled_linear'''     ,       beta_start=config.model.params.linear_start     ,       beta_end=config.model.params.linear_end     ,       clip_sample=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


