






'''simple docstring'''


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


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

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







@dataclass
class 	UpperCAmelCase	(		datasets.BuilderConfig):


 '''simple docstring'''




 __magic_name__					: str  =      ","
 __magic_name__					: Optional[Any]  =      None
 __magic_name__					: int  =      "infer"
 __magic_name__					: Union[str, Any]  =      None
 __magic_name__					: str  =      None
 __magic_name__					: str  =      None
 __magic_name__					: List[str]  =      None
 __magic_name__					: Optional[int]  =      None
 __magic_name__					: Optional[Any]  =      True
 __magic_name__					: Optional[Any]  =      None
 __magic_name__					: str  =      None
 __magic_name__					: Optional[int]  =      None
 __magic_name__					: Union[str, Any]  =      None
 __magic_name__					: Any  =      False
 __magic_name__					: Optional[int]  =      None
 __magic_name__					: Union[str, Any]  =      None
 __magic_name__					: Union[str, Any]  =      None
 __magic_name__					: Union[str, Any]  =      True
 __magic_name__					: Union[str, Any]  =      True
 __magic_name__					: Dict  =      False
 __magic_name__					: Optional[int]  =      True
 __magic_name__					: int  =      None
 __magic_name__					: Tuple  =      "."
 __magic_name__					: int  =      None
 __magic_name__					: int  =      "\""
 __magic_name__					: List[str]  =      0
 __magic_name__					: Optional[Any]  =      None
 __magic_name__					: Tuple  =      None
 __magic_name__					: str  =      None
 __magic_name__					: Optional[Any]  =      None
 __magic_name__					: Any  =      True
 __magic_name__					: str  =      True
 __magic_name__					: List[Any]  =      0
 __magic_name__					: int  =      True
 __magic_name__					: Union[str, Any]  =      False
 __magic_name__					: List[Any]  =      None
 __magic_name__					: Dict  =      10_000
 __magic_name__					: Optional[Any]  =      None
 __magic_name__					: Any  =      "strict"
 __magic_name__					: List[Any]  =      "error"
 __magic_name__					: List[str]  =      None




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







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




 @property
 def       lowercase_  (   self) ->      Dict:



      """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()							,  _lowercase):
                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 	UpperCAmelCase	(		datasets.ArrowBasedBuilder):


 '''simple docstring'''




 __magic_name__					: Union[str, Any]  =      CsvConfig




 def       lowercase_  (   self) ->      Optional[Any]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      List[str]:



      """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(_lowercase							,  (str, list, tuple)):
           a_       		=data_files
           if isinstance(_lowercase							,  _lowercase):
                a_       		=[files]
           a_       		=[dl_manager.iter_files(_lowercase) 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(_lowercase							,  _lowercase):
                a_       		=[files]
           a_       		=[dl_manager.iter_files(_lowercase) for file in files]
           splits.append(datasets.SplitGenerator(name=_lowercase							,  gen_kwargs={"files": files}))
      return splits




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      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(_lowercase) 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(_lowercase)):
           a_       		=pd.read_csv(_lowercase							,  iterator=_lowercase							,  dtype=_lowercase							,  **self.config.pd_read_csv_kwargs)
           try:
                for batch_idx, df in enumerate(_lowercase):
                     a_       		=pa.Table.from_pandas(_lowercase)
                     # 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(_lowercase)
           except ValueError as e:
                logger.error(f"""Failed to read file \'{file}\' with error {type(_lowercase)}: {e}""")
                raise







'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


lowercase							       =			'''path-to-your-trained-model'''
lowercase							       =			StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowercase							       =			'''A photo of sks dog in a bucket'''
lowercase							       =			pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


