
import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


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






@dataclass
class     UpperCAmelCase						(		datasets.BuilderConfig					):
   A__   :						int			=	1_00_00
   A__   :						Optional[List[str]]			=	None
   A__   :						Optional[datasets.Features]			=	None






class     UpperCAmelCase						(		datasets.ArrowBasedBuilder					):
   A__   :						Optional[int]			=	ParquetConfig



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      str:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   Dict					)						->      Any:


          '''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}"""					)
          snake_case			:  Dict     							= dl_manager.download_and_extract(self.config.data_files					)
          if isinstance(snake_case__       ,		(str, list, tuple)					):
                 snake_case			:  Union[str, Any]     							= data_files
                 if isinstance(snake_case__       ,		snake_case__					):
                        snake_case			:  List[str]     							= [files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 snake_case			:  Optional[int]     							= [dl_manager.iter_files(snake_case__					) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,		gen_kwargs={"files": files}					)]
          snake_case			:  Dict     							= []
          for split_name, files in data_files.items():
                 if isinstance(snake_case__       ,		snake_case__					):
                        snake_case			:  List[Any]     							= [files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 snake_case			:  int     							= [dl_manager.iter_files(snake_case__					) for file in files]
                 # Infer features is they are stoed in the arrow schema
                 if self.info.features is None:
                        for file in itertools.chain.from_iterable(snake_case__					):
                               with open(snake_case__       ,		"rb"					) as f:
                                      snake_case			:  Optional[Any]     							= datasets.Features.from_arrow_schema(pq.read_schema(snake_case__					)					)
                               break
                 splits.append(datasets.SplitGenerator(name=snake_case__       ,		gen_kwargs={"files": files}					)					)
          return splits



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   pa.Table					)						->      pa.Table:


          '''simple docstring'''


          if self.info.features is not None:
                 # more expensive cast to support nested features with keys in a different order
                 # allows str <-> int/float or str to Audio for example
                 snake_case			:  Any     							= table_cast(snake_case__       ,		self.info.features.arrow_schema					)
          return pa_table



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   int					)						->      Dict:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= self.info.features.arrow_schema if self.info.features is not None else None
          if self.info.features is not None and self.config.columns is not None:
                 if sorted(field.name for field in schema					) != sorted(self.config.columns					):
                        raise ValueError(
                            f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""					)
          for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__					)					):
                 with open(snake_case__       ,		"rb"					) as f:
                        snake_case			:  Dict     							= pq.ParquetFile(snake_case__					)
                        try:
                               for batch_idx, record_batch in enumerate(
                                   parquet_file.iter_batches(batch_size=self.config.batch_size       ,		columns=self.config.columns					)					):
                                      snake_case			:  int     							= pa.Table.from_batches([record_batch]					)
                                      # 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 f"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__					)
                        except ValueError as e:
                               logger.error(f"""Failed to read file '{file}' with error {type(snake_case__					)}: {e}"""					)
                               raise







import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowerCamelCase				=			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					tuple							,   __lowerCamelCase	:					Path							,   __lowerCamelCase	:					Tuple							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[Any]=False							,   ):
       output_path.parent.mkdir(parents=__lowerCamelCase							,   exist_ok=__lowerCamelCase       )
       # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
       # so we check the torch version for backwards compatibility
       if is_torch_less_than_1_11:
              export(
                  __lowerCamelCase							,   __lowerCamelCase							,   f=output_path.as_posix()							,   input_names=__lowerCamelCase							,   output_names=__lowerCamelCase							,   dynamic_axes=__lowerCamelCase							,   do_constant_folding=__lowerCamelCase							,   use_external_data_format=__lowerCamelCase							,   enable_onnx_checker=__lowerCamelCase							,   opset_version=__lowerCamelCase							,   )
       else:
              export(
                  __lowerCamelCase							,   __lowerCamelCase							,   f=output_path.as_posix()							,   input_names=__lowerCamelCase							,   output_names=__lowerCamelCase							,   dynamic_axes=__lowerCamelCase							,   do_constant_folding=__lowerCamelCase							,   opset_version=__lowerCamelCase							,   )


@torch.no_grad()
def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					bool = False       ):
       snake_case			:  Optional[int]     							= torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              snake_case			:  str     							= "cuda"
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("`float16` model export is only supported on GPUs with CUDA"       )
       else:
              snake_case			:  Tuple     							= "cpu"
       snake_case			:  Union[str, Any]     							= Path(__lowerCamelCase       )

       # VAE DECODER
       snake_case			:  Any     							= AutoencoderKL.from_pretrained(model_path + "/vae"       )
       snake_case			:  Tuple     							= vae_decoder.config.latent_channels
       # forward only through the decoder part
       snake_case			:  Tuple     							= vae_decoder.decode
       onnx_export(
           __lowerCamelCase							,   model_args=(
               torch.randn(1							,   __lowerCamelCase							,   25							,   25       ).to(device=__lowerCamelCase							,   dtype=__lowerCamelCase       ),
               False,
           )							,   output_path=output_path / "vae_decoder" / "model.onnx"							,   ordered_input_names=["latent_sample", "return_dict"]							,   output_names=["sample"]							,   dynamic_axes={
               "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
           }							,   opset=__lowerCamelCase							,   )
       del vae_decoder


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

   parser.add_argument(
       """--model_path""",
       type=str,
       required=True,
       help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
   )

   parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
   parser.add_argument(
       """--opset""",
       default=14,
       type=int,
       help="""The version of the ONNX operator set to use.""",
   )
   parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

   __lowerCamelCase				=			parser.parse_args()
   print(args.output_path)
   convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
   print("""SD: Done: ONNX""")





