




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a_						     =    logging.get_logger(__name__)






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =['pixel_values']
			def __init__(			self							: Union[str, Any]      ,      a							: bool = True      ,      a							: Union[int, float] = 1 / 255      ,      a							: bool = True      ,      a							: int = 8      ,      **a							: Optional[Any]      ,      )							->       None:

				"""simple docstring"""

				super().__init__(**a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       do_rescale
				SCREAMING_SNAKE_CASE   :      Dict				     =       rescale_factor
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       do_pad
				SCREAMING_SNAKE_CASE   :      Any				     =       pad_size
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: np.ndarray      ,      a							: float      ,      a							: Optional[Union[str, ChannelDimension]] = None      ,      **a							: Any	)							->       np.ndarray:

				"""simple docstring"""

				return rescale(a      ,      scale=a      ,      data_format=a      ,      **a	)
			def 							__UpperCamelCase (			self							: Any      ,      a							: np.ndarray      ,      a							: int      ,      a							: Optional[Union[str, ChannelDimension]] = None	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       get_image_size(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (old_height // size + 1) * size - old_height
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (old_width // size + 1) * size - old_width

				return pad(a      ,      ((0, pad_height), (0, pad_width))      ,      mode="symmetric"      ,      data_format=a	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: ImageInput      ,      a							: Optional[bool] = None      ,      a							: Optional[float] = None      ,      a							: Optional[bool] = None      ,      a							: Optional[int] = None      ,      a							: Optional[Union[str, TensorType]] = None      ,      a							: Union[str, ChannelDimension] = ChannelDimension.FIRST      ,      **a							: Dict      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       do_rescale if do_rescale is not None else self.do_rescale
				SCREAMING_SNAKE_CASE   :      Dict				     =       rescale_factor if rescale_factor is not None else self.rescale_factor
				SCREAMING_SNAKE_CASE   :      List[str]				     =       do_pad if do_pad is not None else self.do_pad
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       pad_size if pad_size is not None else self.pad_size

				SCREAMING_SNAKE_CASE   :      Tuple				     =       make_list_of_images(a	)

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

				# All transformations expect numpy arrays.
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       [to_numpy_array(a	) for image in images]

				if do_rescale:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       [self.rescale(image=a      ,      scale=a	) for image in images]

				if do_pad:
					SCREAMING_SNAKE_CASE   :      int				     =       [self.pad(a      ,      size=a	) for image in images]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [to_channel_dimension_format(a      ,      a	) for image in images]

				SCREAMING_SNAKE_CASE   :      str				     =       {"pixel_values": images}
				return BatchFeature(data=a      ,      tensor_type=a	)


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


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






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           =None






class _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =ParquetConfig
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: List[Any]	)							->       Tuple:

				"""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}"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      str				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [dl_manager.iter_files(a	) 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(a	):
							with open(a      ,      "rb"	) as f:
								SCREAMING_SNAKE_CASE   :      Dict				     =       datasets.Features.from_arrow_schema(pq.read_schema(a	)	)
							break
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Dict      ,      a							: 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
					SCREAMING_SNAKE_CASE   :      str				     =       table_cast(a      ,      self.info.features.arrow_schema	)
				return pa_table






			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       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(a	)	):
					with open(a      ,      "rb"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pq.ParquetFile(a	)
						try:
							for batch_idx, record_batch in enumerate(
							    parquet_file.iter_batches(batch_size=self.config.batch_size      ,      columns=self.config.columns	)	):
								SCREAMING_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(a	)
						except ValueError as e:
							logger.error(F"Failed to read file '{file}' with error {type(a	)}: {e}"	)
							raise