






import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


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

UpperCAmelCase__							  =						["names", "prefix"]
UpperCAmelCase__							  =						["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
UpperCAmelCase__							  =						["encoding_errors", "on_bad_lines"]
UpperCAmelCase__							  =						["date_format"]






@dataclass
class 						lowercase_   (      datasets.BuilderConfig  ):






      '''simple docstring'''




      __snake_case	    =    ","
      __snake_case	    =    None
      __snake_case	    =    "infer"
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    True
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    False
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    True
      __snake_case	    =    True
      __snake_case	    =    False
      __snake_case	    =    True
      __snake_case	    =    None
      __snake_case	    =    "."
      __snake_case	    =    None
      __snake_case	    =    '"'
      __snake_case	    =    0
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    None
      __snake_case	    =    True
      __snake_case	    =    True
      __snake_case	    =    0
      __snake_case	    =    True
      __snake_case	    =    False
      __snake_case	    =    None
      __snake_case	    =    1_00_00
      __snake_case	    =    None
      __snake_case	    =    "strict"
      __snake_case	    =    "error"
      __snake_case	    =    None







      def  __lowerCAmelCase   ( self :		int    )       ->str:




       """simple docstring"""


       if self.delimiter is not None:
        a						    = self.delimiter
       if self.column_names is not None:
        a						    = self.column_names







      @property
      def  __lowerCAmelCase   ( self :		Dict    )       ->List[Any]:




       """simple docstring"""


       a						    = {
           '''sep''': self.sep,
           '''header''': self.header,
           '''names''': self.names,
           '''index_col''': self.index_col,
           '''usecols''': self.usecols,
           '''prefix''': self.prefix,
           '''mangle_dupe_cols''': self.mangle_dupe_cols,
           '''engine''': self.engine,
           '''converters''': self.converters,
           '''true_values''': self.true_values,
           '''false_values''': self.false_values,
           '''skipinitialspace''': self.skipinitialspace,
           '''skiprows''': self.skiprows,
           '''nrows''': self.nrows,
           '''na_values''': self.na_values,
           '''keep_default_na''': self.keep_default_na,
           '''na_filter''': self.na_filter,
           '''verbose''': self.verbose,
           '''skip_blank_lines''': self.skip_blank_lines,
           '''thousands''': self.thousands,
           '''decimal''': self.decimal,
           '''lineterminator''': self.lineterminator,
           '''quotechar''': self.quotechar,
           '''quoting''': self.quoting,
           '''escapechar''': self.escapechar,
           '''comment''': self.comment,
           '''encoding''': self.encoding,
           '''dialect''': self.dialect,
           '''error_bad_lines''': self.error_bad_lines,
           '''warn_bad_lines''': self.warn_bad_lines,
           '''skipfooter''': self.skipfooter,
           '''doublequote''': self.doublequote,
           '''memory_map''': self.memory_map,
           '''float_precision''': self.float_precision,
           '''chunksize''': self.chunksize,
           '''encoding_errors''': self.encoding_errors,
           '''on_bad_lines''': self.on_bad_lines,
           '''date_format''': self.date_format,
       }

       # some kwargs must not be passed if they don't have a default value
       # some others are deprecated and we can also not pass them if they are the default value
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
        if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()					,   __UpperCAmelCase    ):
         del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
       if not (datasets.config.PANDAS_VERSION.major >= 2):
        for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
         del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
       if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
        for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
         del pd_read_csv_kwargs[pd_read_csv_parameter]

       return pd_read_csv_kwargs






class 						lowercase_   (      datasets.ArrowBasedBuilder  ):






      '''simple docstring'''




      __snake_case	    =    CsvConfig







      def  __lowerCAmelCase   ( self :		Tuple    )       ->int:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		Optional[int]    )       ->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}"""    )
       a						    = dl_manager.download_and_extract(self.config.data_files    )
       if isinstance(__UpperCAmelCase					,   (str, list, tuple)    ):
        a						    = data_files
        if isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ):
         a						    = [files]
        a						    = [dl_manager.iter_files(__UpperCAmelCase    ) for file in files]
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,   gen_kwargs={'''files''': files}    )]
       a						    = []
       for split_name, files in data_files.items():
        if isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ):
         a						    = [files]
        a						    = [dl_manager.iter_files(__UpperCAmelCase    ) for file in files]
        splits.append(datasets.SplitGenerator(name=__UpperCAmelCase					,   gen_kwargs={'''files''': files}    )    )
       return splits







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		pa.Table    )       ->pa.Table:




       """simple docstring"""


       if self.config.features is not None:
        a						    = self.config.features.arrow_schema
        if all(not require_storage_cast(__UpperCAmelCase    ) for feature in self.config.features.values()    ):
         # cheaper cast
         a						    = pa.Table.from_arrays([pa_table[field.name] for field in schema]					,   schema=__UpperCAmelCase    )
        else:
         # more expensive cast; allows str <-> int/float or str to Audio for example
         a						    = table_cast(__UpperCAmelCase					,   __UpperCAmelCase    )
       return pa_table







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		Dict    )       ->List[str]:




       """simple docstring"""


       a						    = self.config.features.arrow_schema if self.config.features else None
       # dtype allows reading an int column as str
       a						    = (
           {
               name: dtype.to_pandas_dtype() if not require_storage_cast(__UpperCAmelCase    ) else object
               for name, dtype, feature in zip(schema.names					,   schema.types					,   self.config.features.values()    )
           }
           if schema is not None
           else None
       )
       for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase    )    ):
        a						    = pd.read_csv(__UpperCAmelCase					,   iterator=__UpperCAmelCase					,   dtype=__UpperCAmelCase					,   **self.config.pd_read_csv_kwargs    )
        try:
         for batch_idx, df in enumerate(__UpperCAmelCase    ):
          a						    = pa.Table.from_pandas(__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    )
        except ValueError as e:
         logger.error(F"""Failed to read file '{file}' with error {type(__UpperCAmelCase    )}: {e}"""    )
         raise










import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__							  =						version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 output_path.parent.mkdir(parents=a      ,				exist_ok=a					)
 # 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(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				use_external_data_format=a      ,				enable_onnx_checker=a      ,				opset_version=a      ,				)
 else:
  export(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				opset_version=a      ,				)
@torch.no_grad()
def 		_a       ( a					:str      ,				a					:str      ,				a					:int      ,				a					:bool = False					)				->     Union[str, Any]:
 a						    = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  a						    = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''					)
 else:
  a						    = '''cpu'''
 a						    = Path(a					)

 # VAE DECODER
 a						    = AutoencoderKL.from_pretrained(model_path + '''/vae'''					)
 a						    = vae_decoder.config.latent_channels
 # forward only through the decoder part
 a						    = vae_decoder.decode
 onnx_export(
     a      ,				model_args=(
         torch.randn(1      ,				a      ,				25      ,				25					).to(device=a      ,				dtype=a					),
         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=a      ,				)
 del vae_decoder


if __name__ == "__main__":
     UpperCAmelCase__							  =						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")

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


