code: 
  
  
  
  
  """simple docstring"""
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Optional[int]       ,     lowercase__  :     Tuple   ):
  		snake_case		:						Optional[Any]								=				UNetaDModel(**lowercase__   )
  		snake_case		:						str								=				checkpoint["all_modules.0.W"].data
  		snake_case		:						List[str]								=				checkpoint["all_modules.0.W"].data
  		snake_case		:						Dict								=				checkpoint["all_modules.1.weight"].data
  		snake_case		:						Optional[Any]								=				checkpoint["all_modules.1.bias"].data
  
  		snake_case		:						Tuple								=				checkpoint["all_modules.2.weight"].data
  		snake_case		:						Optional[int]								=				checkpoint["all_modules.2.bias"].data
  
  		snake_case		:						Tuple								=				checkpoint["all_modules.3.weight"].data
  		snake_case		:						List[str]								=				checkpoint["all_modules.3.bias"].data
  
  		snake_case		:						Tuple								=				checkpoint[list(checkpoint.keys()   )[-4]].data
  		snake_case		:						List[str]								=				checkpoint[list(checkpoint.keys()   )[-3]].data
  		snake_case		:						Any								=				checkpoint[list(checkpoint.keys()   )[-2]].data
  		snake_case		:						Tuple								=				checkpoint[list(checkpoint.keys()   )[-1]].data
  
  		snake_case		:						List[Any]								=				4
  
  		def set_attention_weights(lowercase__  :     str       ,     lowercase__  :     Union[str, Any]       ,     lowercase__  :     List[Any]   ):
  				snake_case		:						Optional[Any]								=				old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
  				snake_case		:						Optional[int]								=				old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
  				snake_case		:						List[str]								=				old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T
  
  				snake_case		:						Any								=				old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
  				snake_case		:						Dict								=				old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
  				snake_case		:						Union[str, Any]								=				old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data
  
  				snake_case		:						Tuple								=				old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
  				snake_case		:						Union[str, Any]								=				old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data
  
  				snake_case		:						str								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
  				snake_case		:						List[str]								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data
  
  		def set_resnet_weights(lowercase__  :     Tuple       ,     lowercase__  :     Union[str, Any]       ,     lowercase__  :     Any   ):
  				snake_case		:						Dict								=				old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
  				snake_case		:						int								=				old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
  				snake_case		:						Any								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
  				snake_case		:						Optional[int]								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data
  
  				snake_case		:						Optional[Any]								=				old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
  				snake_case		:						Union[str, Any]								=				old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
  				snake_case		:						Any								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
  				snake_case		:						List[Any]								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data
  
  				snake_case		:						Dict								=				old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
  				snake_case		:						Union[str, 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:
  						snake_case		:						Optional[Any]								=				old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
  						snake_case		:						Any								=				old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data
  
  		for i, block in enumerate(new_model_architecture.downsample_blocks   ):
  				snake_case		:						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
  						snake_case		:						Union[str, Any]								=				checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
  						snake_case		:						Any								=				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   ):
  				snake_case		:						int								=				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:
  						snake_case		:						str								=				checkpoint[F'''all_modules.{module_index}.weight'''].data
  						snake_case		:						List[Any]								=				checkpoint[F'''all_modules.{module_index}.bias'''].data
  						module_index += 1
  						snake_case		:						List[str]								=				checkpoint[F'''all_modules.{module_index}.weight'''].data
  						snake_case		:						Tuple								=				checkpoint[F'''all_modules.{module_index}.bias'''].data
  						module_index += 1
  						set_resnet_weights(block.resnet_up       ,     lowercase__       ,     lowercase__   )
  						module_index += 1
  
  		snake_case		:						List[Any]								=				checkpoint[F'''all_modules.{module_index}.weight'''].data
  		snake_case		:						int								=				checkpoint[F'''all_modules.{module_index}.bias'''].data
  		module_index += 1
  		snake_case		:						int								=				checkpoint[F'''all_modules.{module_index}.weight'''].data
  		snake_case		:						Any								=				checkpoint[F'''all_modules.{module_index}.bias'''].data
  
  		return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
  							__A 		=			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.",
  							)
  
  							__A 		=			parser.parse_args()
  
  							__A 		=			torch.load(args.checkpoint_path, map_location="cpu")
  
  							with open(args.config_file) as f:
  														__A 		=			json.loads(f.read())
  
  							__A 		=			convert_ncsnpp_checkpoint(
  							    checkpoint,
  							    config,
  							)
  
  							if "sde" in config:
  														del config["sde"]
  
  							__A 		=			UNetaDModel(**config)
  							model.load_state_dict(converted_checkpoint)
  
  							try:
  														__A 		=			ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
  
  														__A 		=			ScoreSdeVePipeline(unet=model, scheduler=scheduler)
  														pipe.save_pretrained(args.dump_path)
  							except:  # noqa: E722
  														model.save_pretrained(args.dump_path)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  @pytest.fixture
  def   UpperCamelCase__     (		lowercase__  :     str   ):
  		snake_case		:						List[str]								=				tmp_path / "text.txt"
  		snake_case		:						Tuple								=				textwrap.dedent(
  		    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "   )
  		with open(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
  				f.write(lowercase__   )
  		return str(lowercase__   )
  
  
  
  @pytest.fixture
  def   UpperCamelCase__     (		lowercase__  :     str       ,     lowercase__  :     Dict   ):
  		snake_case		:						Any								=				tmp_path / "text_with_image.txt"
  		with open(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
  				f.write(lowercase__   )
  		return str(lowercase__   )
  
  
  
  @pytest.mark.parametrize("keep_linebreaks"       ,     [True, False]   )
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     Tuple   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						List[Any]								=				f.read().splitlines(keepends=lowercase__   )
  		snake_case		:						List[Any]								=				Text(keep_linebreaks=lowercase__       ,     encoding="utf-8"   )
  		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
  		snake_case		:						int								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  
  
  @require_pil
  def   UpperCamelCase__     (		lowercase__  :     Optional[Any]   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						Union[str, Any]								=				f.read().splitlines()[0]
  		snake_case		:						List[Any]								=				Text(encoding="utf-8"       ,     features=Features({"image": Image()}   )   )
  		snake_case		:						Tuple								=				text._generate_tables([[text_file_with_image]]   )
  		snake_case		:						List[str]								=				pa.concat_tables([table for _, table in generator]   )
  		assert pa_table.schema.field("image"   ).type == Image()()
  		snake_case		:						Optional[Any]								=				pa_table.to_pydict()["image"]
  		assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  @pytest.mark.parametrize("sample_by"       ,     ["line", "paragraph", "document"]   )
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     List[str]   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						Tuple								=				f.read()
  		if sample_by == "line":
  				snake_case		:						Optional[int]								=				expected_content.splitlines()
  		elif sample_by == "paragraph":
  				snake_case		:						str								=				expected_content.split("\n\n"   )
  		elif sample_by == "document":
  				snake_case		:						List[Any]								=				[expected_content]
  		snake_case		:						str								=				Text(sample_by=lowercase__       ,     encoding="utf-8"       ,     chunksize=100   )
  		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
  		snake_case		:						Optional[Any]								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  