


'''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


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

lowerCamelCase__					=   ['names', 'prefix']
lowerCamelCase__					=   ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowerCamelCase__					=   ['encoding_errors', 'on_bad_lines']
lowerCamelCase__					=   ['date_format']






@dataclass
class 		lowerCAmelCase__	(	datasets.BuilderConfig		):
			lowerCAmelCase       :		str				      =   ","
			lowerCAmelCase       :		Optional[str]				      =   None
			lowerCAmelCase       :		Optional[Union[int, List[int], str]]				      =   "infer"
			lowerCAmelCase       :		Optional[List[str]]				      =   None
			lowerCAmelCase       :		Optional[List[str]]				      =   None
			lowerCAmelCase       :		Optional[Union[int, str, List[int], List[str]]]				      =   None
			lowerCAmelCase       :		Optional[Union[List[int], List[str]]]				      =   None
			lowerCAmelCase       :		Optional[str]				      =   None
			lowerCAmelCase       :		bool				      =   True
			lowerCAmelCase       :		Optional[Literal["c", "python", "pyarrow"]]				      =   None
			lowerCAmelCase       :		Dict[Union[int, str], Callable[[Any], Any]]				      =   None
			lowerCAmelCase       :		Optional[list]				      =   None
			lowerCAmelCase       :		Optional[list]				      =   None
			lowerCAmelCase       :		bool				      =   False
			lowerCAmelCase       :		Optional[Union[int, List[int]]]				      =   None
			lowerCAmelCase       :		Optional[int]				      =   None
			lowerCAmelCase       :		Optional[Union[str, List[str]]]				      =   None
			lowerCAmelCase       :		bool				      =   True
			lowerCAmelCase       :		bool				      =   True
			lowerCAmelCase       :		bool				      =   False
			lowerCAmelCase       :		bool				      =   True
			lowerCAmelCase       :		Optional[str]				      =   None
			lowerCAmelCase       :		str				      =   "."
			lowerCAmelCase       :		Optional[str]				      =   None
			lowerCAmelCase       :		str				      =   '"'
			lowerCAmelCase       :		int				      =   0
			lowerCAmelCase       :		Optional[str]				      =   None
			lowerCAmelCase       :		Optional[str]				      =   None
			lowerCAmelCase       :		Optional[str]				      =   None
			lowerCAmelCase       :		Optional[str]				      =   None
			lowerCAmelCase       :		bool				      =   True
			lowerCAmelCase       :		bool				      =   True
			lowerCAmelCase       :		int				      =   0
			lowerCAmelCase       :		bool				      =   True
			lowerCAmelCase       :		bool				      =   False
			lowerCAmelCase       :		Optional[str]				      =   None
			lowerCAmelCase       :		int				      =   10_000
			lowerCAmelCase       :		Optional[datasets.Features]				      =   None
			lowerCAmelCase       :		Optional[str]				      =   "strict"
			lowerCAmelCase       :		Literal["error", "warn", "skip"]				      =   "error"
			lowerCAmelCase       :		Optional[str]				      =   None




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Union[str, Any]:



					'''simple docstring'''







					if self.delimiter is not None:
							_UpperCAmelCase						:				Any	    =					self.delimiter
					if self.column_names is not None:
							_UpperCAmelCase						:				List[Any]	    =					self.column_names






			@property
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					{
					    "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()		,     lowerCamelCase__       ):
									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 		lowerCAmelCase__	(	datasets.ArrowBasedBuilder		):
			lowerCAmelCase       :		Tuple				      =   CsvConfig




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Optional[Any]:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   Union[str, Any]       )      ->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}"""       )
					_UpperCAmelCase						:				Tuple	    =					dl_manager.download_and_extract(self.config.data_files       )
					if isinstance(lowerCamelCase__		,     (str, list, tuple)       ):
							_UpperCAmelCase						:				Optional[Any]	    =					data_files
							if isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
									_UpperCAmelCase						:				Optional[Any]	    =					[files]
							_UpperCAmelCase						:				List[Any]	    =					[dl_manager.iter_files(lowerCamelCase__       ) for file in files]
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={"files": files}       )]
					_UpperCAmelCase						:				List[str]	    =					[]
					for split_name, files in data_files.items():
							if isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
									_UpperCAmelCase						:				Any	    =					[files]
							_UpperCAmelCase						:				Dict	    =					[dl_manager.iter_files(lowerCamelCase__       ) for file in files]
							splits.append(datasets.SplitGenerator(name=lowerCamelCase__		,     gen_kwargs={"files": files}       )       )
					return splits




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   pa.Table       )      ->pa.Table:



					'''simple docstring'''







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






			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Union[str, Any]       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.config.features.arrow_schema if self.config.features else None
					# dtype allows reading an int column as str
					_UpperCAmelCase						:				List[str]	    =					(
					    {
					        name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCamelCase__       ) 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(lowerCamelCase__       )       ):
							_UpperCAmelCase						:				Optional[Any]	    =					pd.read_csv(lowerCamelCase__		,     iterator=lowerCamelCase__		,     dtype=lowerCamelCase__		,     **self.config.pd_read_csv_kwargs       )
							try:
									for batch_idx, df in enumerate(lowerCamelCase__       ):
											_UpperCAmelCase						:				Any	    =					pa.Table.from_pandas(lowerCamelCase__       )
											# 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(lowerCamelCase__       )
							except ValueError as e:
									logger.error(F"""Failed to read file '{file}' with error {type(lowerCamelCase__       )}: {e}"""       )
									raise







'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)



