import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	Optional[int]				):
       _UpperCamelCase     :       int  						=  UNetaDModel(**lowercase_				)
       _UpperCamelCase     :       Optional[int]  						=  checkpoint['all_modules.0.W'].data
       _UpperCamelCase     :       List[Any]  						=  checkpoint['all_modules.0.W'].data
       _UpperCamelCase     :       str  						=  checkpoint['all_modules.1.weight'].data
       _UpperCamelCase     :       List[Any]  						=  checkpoint['all_modules.1.bias'].data

       _UpperCamelCase     :       str  						=  checkpoint['all_modules.2.weight'].data
       _UpperCamelCase     :       Optional[Any]  						=  checkpoint['all_modules.2.bias'].data

       _UpperCamelCase     :       Any  						=  checkpoint['all_modules.3.weight'].data
       _UpperCamelCase     :       Optional[Any]  						=  checkpoint['all_modules.3.bias'].data

       _UpperCamelCase     :       Optional[int]  						=  checkpoint[list(checkpoint.keys()				)[-4]].data
       _UpperCamelCase     :       Any  						=  checkpoint[list(checkpoint.keys()				)[-3]].data
       _UpperCamelCase     :       Optional[Any]  						=  checkpoint[list(checkpoint.keys()				)[-2]].data
       _UpperCamelCase     :       List[str]  						=  checkpoint[list(checkpoint.keys()				)[-1]].data

       _UpperCamelCase     :       Any  						=  4

       def set_attention_weights(lowercase_:	Dict		,						lowercase_:	List[Any]		,						lowercase_:	Optional[Any]				):
              _UpperCamelCase     :       Optional[Any]  						=  old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
              _UpperCamelCase     :       Tuple  						=  old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
              _UpperCamelCase     :       Union[str, Any]  						=  old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

              _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
              _UpperCamelCase     :       List[str]  						=  old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
              _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.NIN_2.b"].data

              _UpperCamelCase     :       Optional[Any]  						=  old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
              _UpperCamelCase     :       int  						=  old_checkpoint[F"all_modules.{index}.NIN_3.b"].data

              _UpperCamelCase     :       int  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
              _UpperCamelCase     :       Tuple  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

       def set_resnet_weights(lowercase_:	Optional[Any]		,						lowercase_:	int		,						lowercase_:	Tuple				):
              _UpperCamelCase     :       List[Any]  						=  old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
              _UpperCamelCase     :       List[str]  						=  old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
              _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
              _UpperCamelCase     :       Optional[int]  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

              _UpperCamelCase     :       Optional[int]  						=  old_checkpoint[F"all_modules.{index}.Conv_1.weight"].data
              _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.Conv_1.bias"].data
              _UpperCamelCase     :       Union[str, Any]  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_1.weight"].data
              _UpperCamelCase     :       Union[str, Any]  						=  old_checkpoint[F"all_modules.{index}.GroupNorm_1.bias"].data

              _UpperCamelCase     :       Any  						=  old_checkpoint[F"all_modules.{index}.Dense_0.weight"].data
              _UpperCamelCase     :       Optional[Any]  						=  old_checkpoint[F"all_modules.{index}.Dense_0.bias"].data

              if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                     _UpperCamelCase     :       int  						=  old_checkpoint[F"all_modules.{index}.Conv_2.weight"].data
                     _UpperCamelCase     :       Dict  						=  old_checkpoint[F"all_modules.{index}.Conv_2.bias"].data

       for i, block in enumerate(new_model_architecture.downsample_blocks				):
              _UpperCamelCase     :       List[str]  						=  hasattr(lowercase_		,						'attentions'				)
              for j in range(len(block.resnets				)				):
                     set_resnet_weights(block.resnets[j]		,						lowercase_		,						lowercase_				)
                     module_index += 1
                     if has_attentions:
                            set_attention_weights(block.attentions[j]		,						lowercase_		,						lowercase_				)
                            module_index += 1

              if hasattr(lowercase_		,						'downsamplers'				) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down		,						lowercase_		,						lowercase_				)
                     module_index += 1
                     _UpperCamelCase     :       str  						=  checkpoint[F"all_modules.{module_index}.Conv_0.weight"].data
                     _UpperCamelCase     :       Optional[int]  						=  checkpoint[F"all_modules.{module_index}.Conv_0.bias"].data
                     module_index += 1

       set_resnet_weights(new_model_architecture.mid_block.resnets[0]		,						lowercase_		,						lowercase_				)
       module_index += 1
       set_attention_weights(new_model_architecture.mid_block.attentions[0]		,						lowercase_		,						lowercase_				)
       module_index += 1
       set_resnet_weights(new_model_architecture.mid_block.resnets[1]		,						lowercase_		,						lowercase_				)
       module_index += 1

       for i, block in enumerate(new_model_architecture.up_blocks				):
              _UpperCamelCase     :       Optional[Any]  						=  hasattr(lowercase_		,						'attentions'				)
              for j in range(len(block.resnets				)				):
                     set_resnet_weights(block.resnets[j]		,						lowercase_		,						lowercase_				)
                     module_index += 1
              if has_attentions:
                     set_attention_weights(
                         block.attentions[0]		,						lowercase_		,						lowercase_				)  # why can there only be a single attention layer for up?
                     module_index += 1

              if hasattr(lowercase_		,						'resnet_up'				) and block.resnet_up is not None:
                     _UpperCamelCase     :       int  						=  checkpoint[F"all_modules.{module_index}.weight"].data
                     _UpperCamelCase     :       Dict  						=  checkpoint[F"all_modules.{module_index}.bias"].data
                     module_index += 1
                     _UpperCamelCase     :       Tuple  						=  checkpoint[F"all_modules.{module_index}.weight"].data
                     _UpperCamelCase     :       str  						=  checkpoint[F"all_modules.{module_index}.bias"].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up		,						lowercase_		,						lowercase_				)
                     module_index += 1

       _UpperCamelCase     :       int  						=  checkpoint[F"all_modules.{module_index}.weight"].data
       _UpperCamelCase     :       int  						=  checkpoint[F"all_modules.{module_index}.bias"].data
       module_index += 1
       _UpperCamelCase     :       Tuple  						=  checkpoint[F"all_modules.{module_index}.weight"].data
       _UpperCamelCase     :       List[str]  						=  checkpoint[F"all_modules.{module_index}.bias"].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
 UpperCAmelCase					=				argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''',
     default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
     type=str,
     required=False,
     help='''Path to the checkpoint to convert.''',
 )

 parser.add_argument(
     '''--config_file''',
     default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
     type=str,
     required=False,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument(
     '''--dump_path''',
     default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
     type=str,
     required=False,
     help='''Path to the output model.''',
 )

 UpperCAmelCase					=				parser.parse_args()

 UpperCAmelCase					=				torch.load(args.checkpoint_path, map_location='''cpu''')

 with open(args.config_file) as f:
  UpperCAmelCase					=				json.loads(f.read())

 UpperCAmelCase					=				convert_ncsnpp_checkpoint(
     checkpoint,
     config,
 )

 if "sde" in config:
  del config["sde"]

 UpperCAmelCase					=				UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  UpperCAmelCase					=				ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  UpperCAmelCase					=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)




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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import TvltImageProcessor


def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	Optional[int]=10		,						lowercase_:	Optional[Any]=10		,						lowercase_:	Optional[Any]=False		,						lowercase_:	Any=False				):
       _UpperCamelCase     :       List[Any]  						=  []
       for i in range(image_processor_tester.num_frames				):
              video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta				)				)

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              _UpperCamelCase     :       Tuple  						=  [Image.fromarray(np.moveaxis(lowercase_		,						0		,						-1				)				) for frame in video]

       if torchify:
              _UpperCamelCase     :       Union[str, Any]  						=  [torch.from_numpy(lowercase_				) for frame in video]

       return video


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]		,						lowercase_:	Optional[Any]=False		,						lowercase_:	Tuple=False		,						lowercase_:	Union[str, Any]=False				):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase     :       Any  						=  []
       for i in range(image_processor_tester.batch_size				):
              if equal_resolution:
                     _UpperCamelCase     :       str  						=  image_processor_tester.max_resolution
              else:
                     _UpperCamelCase						,      _UpperCamelCase     :       int  						=  np.random.choice(
                         np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution				)		,						2				)
                     _UpperCamelCase     :       Optional[Any]  						=  prepare_video(
                         image_processor_tester=lowercase_		,						width=lowercase_		,						height=lowercase_		,						numpify=lowercase_		,						torchify=lowercase_		,						)
              video_inputs.append(lowercase_				)

       return video_inputs




class    __lowerCAmelCase  (       unittest.TestCase						):



 '''simple docstring'''

 def __init__(			self       :		Optional[Any]       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Optional[int]=7       ,			lowerCamelCase       :		List[Any]=3       ,			lowerCamelCase       :		str=4       ,			lowerCamelCase       :		Union[str, Any]=1_8       ,			lowerCamelCase       :		str=3_0       ,			lowerCamelCase       :		List[str]=4_0_0       ,			lowerCamelCase       :		Tuple=True       ,			lowerCamelCase       :		Union[str, Any]=None       ,			lowerCamelCase       :		List[str]=True       ,			lowerCamelCase       :		Optional[Any]=[0.5, 0.5, 0.5]       ,			lowerCamelCase       :		Any=[0.5, 0.5, 0.5]       ,			lowerCamelCase       :		str=True       ,			lowerCamelCase       :		Optional[Any]=None       ,			) ->							Union[str, Any]:
        _UpperCamelCase     :       Optional[int]  						=  size if size is not None else {'shortest_edge': 1_8}
        _UpperCamelCase     :       Dict  						=  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

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

 def 					__snake_case					(			self       :		Tuple  ) ->							Optional[Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class    __lowerCAmelCase  (       a_,	unittest.TestCase						):



 '''simple docstring'''
 _lowerCamelCase						:	Any				    = TvltImageProcessor if is_vision_available() else None

 def 					__snake_case					(			self       :		int  ) ->							Optional[int]:
        _UpperCamelCase     :       Optional[Any]  						=  TvltImageProcessorTester(self  )

 @property
 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Any:
        return self.image_processor_tester.prepare_image_processor_dict()

 def 					__snake_case					(			self       :		Union[str, Any]  ) ->							str:
        _UpperCamelCase     :       Tuple  						=  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 					__snake_case					(			self       :		Dict  ) ->							Optional[int]:
        # Initialize image_processor
        _UpperCamelCase     :       str  						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL videos
        _UpperCamelCase     :       int  						=  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
        _UpperCamelCase     :       Dict  						=  image_processor(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
        _UpperCamelCase     :       Any  						=  image_processor(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 					__snake_case					(			self       :		Union[str, Any]  ) ->							List[str]:
        # Initialize image_processor
        _UpperCamelCase     :       Optional[int]  						=  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        _UpperCamelCase     :       int  						=  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
        _UpperCamelCase     :       Optional[int]  						=  image_processor(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
        _UpperCamelCase     :       int  						=  image_processor(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 					__snake_case					(			self       :		Optional[Any]  ) ->							Any:
        # Initialize image_processor
        _UpperCamelCase     :       int  						=  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        _UpperCamelCase     :       Optional[Any]  						=  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
        _UpperCamelCase     :       Dict  						=  image_processor(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
        _UpperCamelCase     :       Optional[int]  						=  image_processor(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'],
            )       ,			)



