
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def 				lowerCamelCase__				(						a__  :  Optional[int]	,      a__  :  Tuple     )		->	List[Any]:
       UpperCamelCase_							       =       UNetaDModel(**lowercase__     )
       UpperCamelCase_							       =       checkpoint["all_modules.0.W"].data
       UpperCamelCase_							       =       checkpoint["all_modules.0.W"].data
       UpperCamelCase_							       =       checkpoint["all_modules.1.weight"].data
       UpperCamelCase_							       =       checkpoint["all_modules.1.bias"].data

       UpperCamelCase_							       =       checkpoint["all_modules.2.weight"].data
       UpperCamelCase_							       =       checkpoint["all_modules.2.bias"].data

       UpperCamelCase_							       =       checkpoint["all_modules.3.weight"].data
       UpperCamelCase_							       =       checkpoint["all_modules.3.bias"].data

       UpperCamelCase_							       =       checkpoint[list(checkpoint.keys()     )[-4]].data
       UpperCamelCase_							       =       checkpoint[list(checkpoint.keys()     )[-3]].data
       UpperCamelCase_							       =       checkpoint[list(checkpoint.keys()     )[-2]].data
       UpperCamelCase_							       =       checkpoint[list(checkpoint.keys()     )[-1]].data

       UpperCamelCase_							       =       4

       def set_attention_weights(a__  :  str	,      a__  :  Union[str, Any]	,      a__  :  List[Any]     ):
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

       def set_resnet_weights(a__  :  Tuple	,      a__  :  Union[str, Any]	,      a__  :  Any     ):
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

              UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
              UpperCamelCase_							       =       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_							       =       old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
                     UpperCamelCase_							       =       old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

       for i, block in enumerate(new_model_architecture.downsample_blocks     ):
              UpperCamelCase_							       =       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_							       =       checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
                     UpperCamelCase_							       =       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_							       =       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_							       =       checkpoint[f'''all_modules.{module_index}.weight'''].data
                     UpperCamelCase_							       =       checkpoint[f'''all_modules.{module_index}.bias'''].data
                     module_index += 1
                     UpperCamelCase_							       =       checkpoint[f'''all_modules.{module_index}.weight'''].data
                     UpperCamelCase_							       =       checkpoint[f'''all_modules.{module_index}.bias'''].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up	,      lowercase__	,      lowercase__     )
                     module_index += 1

       UpperCamelCase_							       =       checkpoint[f'''all_modules.{module_index}.weight'''].data
       UpperCamelCase_							       =       checkpoint[f'''all_modules.{module_index}.bias'''].data
       module_index += 1
       UpperCamelCase_							       =       checkpoint[f'''all_modules.{module_index}.weight'''].data
       UpperCamelCase_							       =       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)





import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


_A	  =					datasets.utils.logging.get_logger(__name__)

@dataclass
class 		lowercase_       (       datasets.BuilderConfig				):
 A__						:				Optional[datasets.Features]         =    None
 A__						:				str         =    "utf-8"
 A__						:				Optional[str]         =    None
 A__						:				int         =    10 << 20  # 10MB
 A__						:				bool         =    False
 A__						:				str         =    "line"



class 		lowercase_       (       datasets.ArrowBasedBuilder				):
 A__						:				str         =    TextConfig

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return datasets.DatasetInfo(features=self.config.features     )

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        if not self.config.data_files:
               raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''     )
        UpperCamelCase_							       =       dl_manager.download_and_extract(self.config.data_files     )
        if isinstance(__UpperCamelCase     ,	(str, list, tuple)     ):
               UpperCamelCase_							       =       data_files
               if isinstance(__UpperCamelCase     ,	__UpperCamelCase     ):
                      UpperCamelCase_							       =       [files]
               UpperCamelCase_							       =       [dl_manager.iter_files(__UpperCamelCase     ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,	gen_kwargs={"""files""": files}     )]
        UpperCamelCase_							       =       []
        for split_name, files in data_files.items():
               if isinstance(__UpperCamelCase     ,	__UpperCamelCase     ):
                      UpperCamelCase_							       =       [files]
               UpperCamelCase_							       =       [dl_manager.iter_files(__UpperCamelCase     ) for file in files]
               splits.append(datasets.SplitGenerator(name=__UpperCamelCase     ,	gen_kwargs={"""files""": files}     )     )
        return splits

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        if self.config.features is not None:
               UpperCamelCase_							       =       self.config.features.arrow_schema
               if all(not require_storage_cast(__UpperCamelCase     ) for feature in self.config.features.values()     ):
                      # cheaper cast
                      UpperCamelCase_							       =       pa_table.cast(__UpperCamelCase     )
               else:
                      # more expensive cast; allows str <-> int/float or str to Audio for example
                      UpperCamelCase_							       =       table_cast(__UpperCamelCase     ,	__UpperCamelCase     )
               return pa_table
        else:
               return pa_table.cast(pa.schema({"""text""": pa.string()}     )     )

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       list(self.config.features     ) if self.config.features is not None else ["""text"""]
        for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCamelCase     )     ):
               # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
               with open(__UpperCamelCase     ,	encoding=self.config.encoding     ,	errors=self.config.errors     ) as f:
                      if self.config.sample_by == "line":
                             UpperCamelCase_							       =       0
                             while True:
                                    UpperCamelCase_							       =       f.read(self.config.chunksize     )
                                    if not batch:
                                           break
                                    batch += f.readline()  # finish current line
                                    # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                                    UpperCamelCase_							       =       StringIO(__UpperCamelCase     ).readlines()
                                    if not self.config.keep_linebreaks:
                                           UpperCamelCase_							       =       [line.rstrip("""\n"""     ) for line in batch]
                                    UpperCamelCase_							       =       pa.Table.from_arrays([pa.array(__UpperCamelCase     )]     ,	names=__UpperCamelCase     )
                                    # Uncomment for debugging (will print the Arrow table size and elements)
                                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                    yield (file_idx, batch_idx), self._cast_table(__UpperCamelCase     )
                                    batch_idx += 1
                      elif self.config.sample_by == "paragraph":
                             UpperCamelCase_							       =       0
                             UpperCamelCase_							       =       """"""
                             while True:
                                    UpperCamelCase_							       =       f.read(self.config.chunksize     )
                                    if not new_batch:
                                           break
                                    batch += new_batch
                                    batch += f.readline()  # finish current line
                                    UpperCamelCase_							       =       batch.split("""\n\n"""     )
                                    UpperCamelCase_							       =       pa.Table.from_arrays(
                                        [pa.array([example for example in batch[:-1] if example]     )]     ,	names=__UpperCamelCase     )
                                    # Uncomment for debugging (will print the Arrow table size and elements)
                                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                    yield (file_idx, batch_idx), self._cast_table(__UpperCamelCase     )
                                    batch_idx += 1
                                    UpperCamelCase_							       =       batch[-1]
                             if batch:
                                    UpperCamelCase_							       =       pa.Table.from_arrays([pa.array([batch]     )]     ,	names=__UpperCamelCase     )
                                    yield (file_idx, batch_idx), self._cast_table(__UpperCamelCase     )
                      elif self.config.sample_by == "document":
                             UpperCamelCase_							       =       f.read()
                             UpperCamelCase_							       =       pa.Table.from_arrays([pa.array([text]     )]     ,	names=__UpperCamelCase     )
                             yield file_idx, self._cast_table(__UpperCamelCase     )



