

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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				['pixel_values']


    def __init__( self					:			Optional[Any],lowercase_					:			bool = True,lowercase_					:			Dict[str, int] = None,lowercase_					:			PILImageResampling = PIL.Image.NEAREST,lowercase_					:			bool = False,lowercase_					:			Dict[str, int] = None,lowercase_					:			Union[int, float] = 1 / 2_5_5,lowercase_					:			bool = False,lowercase_					:			bool = True,lowercase_					:			bool = True,lowercase_					:			Optional[Union[float, List[float]]] = None,lowercase_					:			Optional[Union[float, List[float]]] = None,lowercase_					:			bool = True,**lowercase_					:			List[str],)->       None:





        '''simple docstring'''







        super().__init__(**lowercase_						)
        A__        =  size if size is not None else {'height': 3_4_6, 'width': 3_4_6}
        A__        =  get_size_dict(lowercase_						)
        A__        =  crop_size if crop_size is not None else {'height': 2_8_9, 'width': 2_8_9}
        A__        =  get_size_dict(lowercase_,param_name='crop_size'						)

        A__        =  do_resize
        A__        =  size
        A__        =  resample
        A__        =  do_center_crop
        A__        =  crop_size
        A__        =  do_rescale
        A__        =  rescale_factor
        A__        =  rescale_offset
        A__        =  do_normalize
        A__        =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        A__        =  image_std if image_std is not None else IMAGENET_STANDARD_STD
        A__        =  include_top


    def 		snake_case__	( self					:			Any,lowercase_					:			np.ndarray,lowercase_					:			Dict[str, int],lowercase_					:			PILImageResampling = PIL.Image.NEAREST,lowercase_					:			Optional[Union[str, ChannelDimension]] = None,**lowercase_					:			List[str],)->       np.ndarray:





        '''simple docstring'''







        A__        =  get_size_dict(lowercase_						)
        if "height" not in size or "width" not in size:
            raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
        return resize(
            lowercase_,size=(size['height'], size['width']),resample=lowercase_,data_format=lowercase_,**lowercase_						)


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





        '''simple docstring'''







        A__        =  get_size_dict(lowercase_						)
        if "height" not in size or "width" not in size:
            raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
        return center_crop(lowercase_,size=(size['height'], size['width']),data_format=lowercase_,**lowercase_						)


    def 		snake_case__	( self					:			List[Any],lowercase_					:			np.ndarray,lowercase_					:			Union[int, float],lowercase_					:			bool = True,lowercase_					:			Optional[Union[str, ChannelDimension]] = None,**lowercase_					:			Union[str, Any],)->       Union[str, Any]:





        '''simple docstring'''







        if offset:
            A__        =  (image - 127.5) * scale
            if data_format is not None:
                A__        =  to_channel_dimension_format(lowercase_,lowercase_						)
            A__        =  rescaled_image.astype(np.floataa						)
        else:
            A__        =  rescale(lowercase_,scale=lowercase_,data_format=lowercase_,**lowercase_						)
        return rescaled_image


    def 		snake_case__	( self					:			Union[str, Any],lowercase_					:			np.ndarray,lowercase_					:			Union[float, List[float]],lowercase_					:			Union[float, List[float]],lowercase_					:			Optional[Union[str, ChannelDimension]] = None,**lowercase_					:			Optional[int],)->       np.ndarray:





        '''simple docstring'''







        return normalize(lowercase_,mean=lowercase_,std=lowercase_,data_format=lowercase_,**lowercase_						)


    def 		snake_case__	( self					:			List[Any],lowercase_					:			ImageInput,lowercase_					:			bool = None,lowercase_					:			Dict[str, int] = None,lowercase_					:			Dict=None,lowercase_					:			bool = None,lowercase_					:			Dict[str, int] = None,lowercase_					:			bool = None,lowercase_					:			float = None,lowercase_					:			bool = None,lowercase_					:			bool = None,lowercase_					:			Optional[Union[float, List[float]]] = None,lowercase_					:			Optional[Union[float, List[float]]] = None,lowercase_					:			bool = None,lowercase_					:			Optional[Union[str, TensorType]] = None,lowercase_					:			ChannelDimension = ChannelDimension.FIRST,**lowercase_					:			Dict,)->       PIL.Image.Image:





        '''simple docstring'''







        A__        =  do_resize if do_resize is not None else self.do_resize
        A__        =  resample if resample is not None else self.resample
        A__        =  do_center_crop if do_center_crop is not None else self.do_center_crop
        A__        =  do_rescale if do_rescale is not None else self.do_rescale
        A__        =  rescale_factor if rescale_factor is not None else self.rescale_factor
        A__        =  rescale_offset if rescale_offset is not None else self.rescale_offset
        A__        =  do_normalize if do_normalize is not None else self.do_normalize
        A__        =  image_mean if image_mean is not None else self.image_mean
        A__        =  image_std if image_std is not None else self.image_std
        A__        =  include_top if include_top is not None else self.include_top

        A__        =  size if size is not None else self.size
        A__        =  get_size_dict(lowercase_						)
        A__        =  crop_size if crop_size is not None else self.crop_size
        A__        =  get_size_dict(lowercase_,param_name='crop_size'						)

        A__        =  make_list_of_images(lowercase_						)

        if not valid_images(lowercase_						):
            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_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.'						)

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

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

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

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

        if do_rescale:
            A__        =  [self.rescale(image=lowercase_,scale=lowercase_,offset=lowercase_						) for image in images]

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

        if include_top:
            A__        =  [self.normalize(image=lowercase_,mean=[0, 0, 0],std=lowercase_						) for image in images]

        A__        =  [to_channel_dimension_format(lowercase_,lowercase_						) for image in images]

        A__        =  {'pixel_values': images}
        return BatchFeature(data=lowercase_,tensor_type=lowercase_						)




import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, 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


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


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


@dataclass
class 					A		(				datasets.BuilderConfig					):







    """simple docstring"""





    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				True
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				1_00_00
    lowerCamelCase						      =				None


    def 		snake_case__	( self					:			Tuple						)->       Optional[int]:





        '''simple docstring'''







        if self.sql is None:
            raise ValueError('sql must be specified'						)
        if self.con is None:
            raise ValueError('con must be specified'						)


    def 		snake_case__	( self					:			Dict,lowercase_					:			dict,lowercase_					:			Optional[datasets.Features] = None,)->       str:





        '''simple docstring'''







        A__        =  config_kwargs.copy()
        # We need to stringify the Selectable object to make its hash deterministic

        # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
        A__        =  config_kwargs['sql']
        if not isinstance(lowercase_,lowercase_						):
            if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                import sqlalchemy

                if isinstance(lowercase_,sqlalchemy.sql.Selectable						):
                    A__        =  sqlalchemy.create_engine(config_kwargs['con'].split('://'						)[0] + '://'						)
                    A__        =  str(sql.compile(dialect=engine.dialect						)						)
                    A__        =  sql_str
                else:
                    raise TypeError(
                        F'Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowercase_						)}: {sql}'						)
            else:
                raise TypeError(
                    F'Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowercase_						)}: {sql}'						)
        A__        =  config_kwargs['con']
        if not isinstance(lowercase_,lowercase_						):
            A__        =  id(lowercase_						)
            logger.info(
                F'SQL connection \'con\' of type {type(lowercase_						)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'						)

        return super().create_config_id(lowercase_,custom_features=lowercase_						)


    @property
    def 		snake_case__	( self					:			Union[str, Any]						)->       Any:





        '''simple docstring'''







        A__        =  {
            'index_col': self.index_col,
            'columns': self.columns,
            'params': self.params,
            'coerce_float': self.coerce_float,
            'parse_dates': self.parse_dates,
        }
        return pd_read_sql_kwargs




class 					A		(				datasets.ArrowBasedBuilder					):







    """simple docstring"""





    lowerCamelCase						      =				SqlConfig


    def 		snake_case__	( self					:			Any						)->       Any:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Tuple,lowercase_					:			List[Any]						)->       Union[str, Any]:





        '''simple docstring'''







        return [datasets.SplitGenerator(name=datasets.Split.TRAIN,gen_kwargs={}						)]


    def 		snake_case__	( self					:			Any,lowercase_					:			pa.Table						)->       pa.Table:





        '''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 		snake_case__	( self					:			List[str]						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  self.config.chunksize
        A__        =  pd.read_sql(
            self.config.sql,self.config.con,chunksize=lowercase_,**self.config.pd_read_sql_kwargs						)
        A__        =  [sql_reader] if chunksize is None else sql_reader
        for chunk_idx, df in enumerate(lowercase_						):
            A__        =  pa.Table.from_pandas(lowercase_						)
            yield chunk_idx, self._cast_table(lowercase_						)

