






"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A		:       Union[str, Any]  				=		logging.get_logger(__name__)





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   ["pixel_values"]




					def __init__(				self     :						str    ,						A     :						bool = True    ,						A     :						Optional[Dict[str, int]] = None    ,						A     :						PILImageResampling = PILImageResampling.BICUBIC    ,						A     :						bool = True    ,						A     :						bool = True    ,						A     :						Union[int, float] = 1 / 2_55    ,						A     :						Dict[str, int] = None    ,						A     :						bool = True    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						**A     :						Dict    ,						)  ->  None:
							super().__init__(**A   )
							lowercase_ : Tuple							       =   size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							lowercase_ : Dict							       =   get_size_dict(A   )
							lowercase_ : Union[str, Any]							       =   crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							lowercase_ : List[Any]							       =   get_size_dict(A    ,						default_to_square=A    ,						param_name='''crop_size'''   )

							lowercase_ : Optional[Any]							       =   do_resize
							lowercase_ : List[str]							       =   do_rescale
							lowercase_ : Optional[int]							       =   do_normalize
							lowercase_ : List[str]							       =   do_center_crop
							lowercase_ : Union[str, Any]							       =   crop_size
							lowercase_ : Optional[Any]							       =   size
							lowercase_ : Optional[Any]							       =   resample
							lowercase_ : Optional[Any]							       =   rescale_factor
							lowercase_ : Tuple							       =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							lowercase_ : Tuple							       =   image_std if image_std is not None else IMAGENET_DEFAULT_STD




					def   A  (				self     :						str    ,						A     :						np.ndarray    ,						A     :						Dict[str, int]    ,						A     :						PILImageResampling = PILImageResampling.BILINEAR    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						List[str]    ,						)  ->  np.ndarray:
							lowercase_ : str							       =   get_size_dict(A   )

							if "shortest_edge" in size:
									lowercase_ : Dict							       =   get_resize_output_image_size(A    ,						size=size['''shortest_edge''']    ,						default_to_square=A   )
									# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
									lowercase_ : List[Any]							       =   (size['''height'''], size['''width'''])
							else:
									raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''   )
							return resize(A    ,						size=A    ,						resample=A    ,						data_format=A    ,						**A   )




					def   A  (				self     :						str    ,						A     :						np.ndarray    ,						A     :						Dict[str, int]    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Dict    ,						)  ->  np.ndarray:
							lowercase_ : List[Any]							       =   get_size_dict(A   )
							if "height" not in size or "width" not in size:
									raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''   )
							return center_crop(A    ,						size=(size['''height'''], size['''width'''])    ,						data_format=A    ,						**A   )




					def   A  (				self     :						Tuple    ,						A     :						np.ndarray    ,						A     :						float    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Any   )  ->  np.ndarray:
							return rescale(A    ,						scale=A    ,						data_format=A    ,						**A   )




					def   A  (				self     :						Optional[int]    ,						A     :						np.ndarray    ,						A     :						Union[float, List[float]]    ,						A     :						Union[float, List[float]]    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Tuple    ,						)  ->  np.ndarray:
							return normalize(A    ,						mean=A    ,						std=A    ,						data_format=A    ,						**A   )






					def   A  (				self     :						Dict    ,						A     :						ImageInput    ,						A     :						Optional[bool] = None    ,						A     :						Dict[str, int] = None    ,						A     :						PILImageResampling = None    ,						A     :						bool = None    ,						A     :						int = None    ,						A     :						Optional[bool] = None    ,						A     :						Optional[float] = None    ,						A     :						Optional[bool] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[str, TensorType]] = None    ,						A     :						Union[str, ChannelDimension] = ChannelDimension.FIRST    ,						**A     :						List[str]    ,						)  ->  BatchFeature:
							lowercase_ : str							       =   do_resize if do_resize is not None else self.do_resize
							lowercase_ : int							       =   do_rescale if do_rescale is not None else self.do_rescale
							lowercase_ : str							       =   do_normalize if do_normalize is not None else self.do_normalize
							lowercase_ : Any							       =   do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase_ : str							       =   crop_size if crop_size is not None else self.crop_size
							lowercase_ : int							       =   get_size_dict(A    ,						param_name='''crop_size'''    ,						default_to_square=A   )
							lowercase_ : Union[str, Any]							       =   resample if resample is not None else self.resample
							lowercase_ : Any							       =   rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase_ : Tuple							       =   image_mean if image_mean is not None else self.image_mean
							lowercase_ : Tuple							       =   image_std if image_std is not None else self.image_std

							lowercase_ : Union[str, Any]							       =   size if size is not None else self.size
							lowercase_ : str							       =   get_size_dict(A   )

							if not is_batched(A   ):
									lowercase_ : List[Any]							       =   [images]

							if not valid_images(A   ):
									raise ValueError(
									    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
									    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

							if do_resize and size is None:
									raise ValueError('''Size must be specified if do_resize is True.'''   )

							if do_center_crop and crop_size is None:
									raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

							if do_rescale and rescale_factor is None:
									raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

							# All transformations expect numpy arrays.
							lowercase_ : Tuple							       =   [to_numpy_array(A   ) for image in images]

							if do_resize:
									lowercase_ : List[str]							       =   [self.resize(image=A    ,						size=A    ,						resample=A   ) for image in images]

							if do_center_crop:
									lowercase_ : List[Any]							       =   [self.center_crop(image=A    ,						size=A   ) for image in images]

							if do_rescale:
									lowercase_ : List[Any]							       =   [self.rescale(image=A    ,						scale=A   ) for image in images]

							if do_normalize:
									lowercase_ : Dict							       =   [self.normalize(image=A    ,						mean=A    ,						std=A   ) for image in images]

							lowercase_ : List[str]							       =   [to_channel_dimension_format(A    ,						A   ) for image in images]

							lowercase_ : Optional[Any]							       =   {'''pixel_values''': images}
							return BatchFeature(data=A    ,						tensor_type=A   )






"""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


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

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




@dataclass
class 					_lowerCAmelCase  (	datasets.BuilderConfig   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	","
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, List[int], str]]   		=	"infer"
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[List[str]]   		=	None
 __UpperCAmelCase	:					Optional[Union[int, str, List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[Union[List[int], List[str]]]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[Literal["c", "python", "pyarrow"]]   		=	None
 __UpperCAmelCase	:					Dict[Union[int, str], Callable[[Any], Any]]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					Optional[list]   		=	None
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[Union[int, List[int]]]   		=	None
 __UpperCAmelCase	:					Optional[int]   		=	None
 __UpperCAmelCase	:					Optional[Union[str, List[str]]]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	"."
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					str   		=	'"'
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					int   		=	0
 __UpperCAmelCase	:					bool   		=	True
 __UpperCAmelCase	:					bool   		=	False
 __UpperCAmelCase	:					Optional[str]   		=	None
 __UpperCAmelCase	:					int   		=	1_0_0_0_0
 __UpperCAmelCase	:					Optional[datasets.Features]   		=	None
 __UpperCAmelCase	:					Optional[str]   		=	"strict"
 __UpperCAmelCase	:					Literal["error", "warn", "skip"]   		=	"error"
 __UpperCAmelCase	:					Optional[str]   		=	None


 def 					_lowercase (			self							:			Tuple	):
    if self.delimiter is not None:
       __lowercase								=						self.delimiter
    if self.column_names is not None:
       __lowercase								=						self.column_names



 @property
 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						{
        "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 					_lowerCAmelCase  (	datasets.ArrowBasedBuilder   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	CsvConfig


 def 					_lowercase (			self							:			List[str]	):
    return datasets.DatasetInfo(features=self.config.features	)


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			Dict	):
    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}"""	)
    __lowercase								=						dl_manager.download_and_extract(self.config.data_files	)
    if isinstance(UpperCAmelCase__,					(str, list, tuple)	):
       __lowercase								=						data_files
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN,					gen_kwargs={"files": files}	)]
    __lowercase								=						[]
    for split_name, files in data_files.items():
       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[files]
       __lowercase								=						[dl_manager.iter_files(UpperCAmelCase__	) for file in files]
       splits.append(datasets.SplitGenerator(name=UpperCAmelCase__,					gen_kwargs={"files": files}	)	)
    return splits


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			pa.Table	):
    if self.config.features is not None:
       __lowercase								=						self.config.features.arrow_schema
       if all(not require_storage_cast(UpperCAmelCase__	) for feature in self.config.features.values()	):
          # cheaper cast
          __lowercase								=						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
          __lowercase								=						table_cast(UpperCAmelCase__,					UpperCAmelCase__	)
    return pa_table



 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.config.features.arrow_schema if self.config.features else None
    # dtype allows reading an int column as str
    __lowercase								=						(
        {
            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__	)	):
       __lowercase								=						pd.read_csv(UpperCAmelCase__,					iterator=UpperCAmelCase__,					dtype=UpperCAmelCase__,					**self.config.pd_read_csv_kwargs	)
       try:
          for batch_idx, df in enumerate(UpperCAmelCase__	):
             __lowercase								=						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




