






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


__UpperCAmelCase							:   Dict							=  datasets.utils.logging.get_logger(__name__)


@dataclass
class 					_snake_case						(			datasets.BuilderConfig ):
	_A          =   10000
	_A          =   None
	_A          =   None




class 					_snake_case						(			datasets.ArrowBasedBuilder ):
	_A          =   ParquetConfig






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					return datasets.DatasetInfo(features=self.config.features       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    str:
					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}'       )
					snake_case__      :Dict         =   dl_manager.download_and_extract(self.config.data_files       )
					if isinstance(A__					,(str, list, tuple)       ):
									snake_case__      :List[Any]         =   data_files
									if isinstance(A__					,A__       ):
													snake_case__      :Any         =   [files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									snake_case__      :Optional[int]         =   [dl_manager.iter_files(A__       ) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,gen_kwargs={"files": files}       )]
					snake_case__      :Any         =   []
					for split_name, files in data_files.items():
									if isinstance(A__					,A__       ):
													snake_case__      :List[Any]         =   [files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									snake_case__      :Union[str, Any]         =   [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:
																					snake_case__      :Optional[Any]         =   datasets.Features.from_arrow_schema(pq.read_schema(A__       )       )
																	break
									splits.append(datasets.SplitGenerator(name=A__					,gen_kwargs={"files": files}       )       )
					return splits






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    pa.Table:
					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
									snake_case__      :int         =   table_cast(A__					,self.info.features.arrow_schema       )
					return pa_table






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Optional[int]:
					snake_case__      :Optional[int]         =   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:
													snake_case__      :List[str]         =   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       )       ):
																					snake_case__      :Optional[Any]         =   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







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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__UpperCAmelCase							:   Optional[Any]							=  logging.get_logger(__name__)





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any      )     -> Any:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   b.T
				snake_case__      :Optional[Any]         =   np.sum(np.square(__snake_case      )	,	axis=1      )
				snake_case__      :Tuple         =   np.sum(np.square(__snake_case      )	,	axis=0      )
				snake_case__      :Union[str, Any]         =   np.matmul(__snake_case	,	__snake_case      )
				snake_case__      :Union[str, Any]         =   aa[:, None] - 2 * ab + ba[None, :]
				return d





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							int      )     -> Any:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   x.reshape(-1	,	3      )
				snake_case__      :List[str]         =   squared_euclidean_distance(__snake_case	,	__snake_case      )
				return np.argmin(__snake_case	,	axis=1      )


class 					_snake_case						(			_A ):
	_A          =   ['pixel_values']






	def __init__(		self					,UpperCamelCase = None					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = True					,UpperCamelCase = True					,**UpperCamelCase					,)						->    None:
					super().__init__(**UpperCamelCase       )
					snake_case__      :List[Any]         =   size if size is not None else {"height": 256, "width": 256}
					snake_case__      :str         =   get_size_dict(UpperCamelCase       )
					snake_case__      :Dict         =   np.array(UpperCamelCase       ) if clusters is not None else None
					snake_case__      :str         =   do_resize
					snake_case__      :List[str]         =   size
					snake_case__      :List[Any]         =   resample
					snake_case__      :Union[str, Any]         =   do_normalize
					snake_case__      :int         =   do_color_quantize






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					snake_case__      :List[str]         =   get_size_dict(UpperCamelCase       )
					if "height" not in size or "width" not in size:
									raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'       )
					return resize(
					    UpperCamelCase					,size=(size["height"], size["width"])					,resample=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,)						->    np.ndarray:
					snake_case__      :Tuple         =   rescale(image=UpperCamelCase					,scale=1 / 127.5					,data_format=UpperCamelCase       )
					snake_case__      :List[Any]         =   image - 1
					return image






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = ChannelDimension.FIRST					,**UpperCamelCase					,)						->    PIL.Image.Image:
					snake_case__      :Optional[int]         =   do_resize if do_resize is not None else self.do_resize
					snake_case__      :int         =   size if size is not None else self.size
					snake_case__      :Tuple         =   get_size_dict(UpperCamelCase       )
					snake_case__      :str         =   resample if resample is not None else self.resample
					snake_case__      :Dict         =   do_normalize if do_normalize is not None else self.do_normalize
					snake_case__      :Tuple         =   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					snake_case__      :List[Any]         =   clusters if clusters is not None else self.clusters
					snake_case__      :str         =   np.array(UpperCamelCase       )

					snake_case__      :int         =   make_list_of_images(UpperCamelCase       )

					if not valid_images(UpperCamelCase       ):
									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 or resample is None:
									raise ValueError("Size and resample must be specified if do_resize is True."       )

					if do_color_quantize and clusters is None:
									raise ValueError("Clusters must be specified if do_color_quantize is True."       )

					# All transformations expect numpy arrays.
					snake_case__      :Union[str, Any]         =   [to_numpy_array(UpperCamelCase       ) for image in images]

					if do_resize:
									snake_case__      :int         =   [self.resize(image=UpperCamelCase					,size=UpperCamelCase					,resample=UpperCamelCase       ) for image in images]

					if do_normalize:
									snake_case__      :Any         =   [self.normalize(image=UpperCamelCase       ) for image in images]

					if do_color_quantize:
									snake_case__      :Optional[Any]         =   [to_channel_dimension_format(UpperCamelCase					,ChannelDimension.LAST       ) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									snake_case__      :Union[str, Any]         =   np.array(UpperCamelCase       )
									snake_case__      :Optional[int]         =   color_quantize(UpperCamelCase					,UpperCamelCase       ).reshape(images.shape[:-1]       )

									# flatten to (batch_size, height*width)
									snake_case__      :List[Any]         =   images.shape[0]
									snake_case__      :str         =   images.reshape(UpperCamelCase					,-1       )

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									snake_case__      :Any         =   list(UpperCamelCase       )
					else:
									snake_case__      :List[str]         =   [to_channel_dimension_format(UpperCamelCase					,UpperCamelCase       ) for image in images]

					snake_case__      :List[str]         =   {"input_ids": images}
					return BatchFeature(data=UpperCamelCase					,tensor_type=UpperCamelCase       )