from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


lowercase_  : List[str]								=					logging.get_logger(__name__)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   ["pixel_values"]
    def __init__(	self			,			lowerCAmelCase = True			,			lowerCAmelCase = None			,			lowerCAmelCase = PILImageResampling.BILINEAR			,			lowerCAmelCase = True			,			lowerCAmelCase = 1 / 255			,			lowerCAmelCase = True			,			lowerCAmelCase = None			,			lowerCAmelCase = True			,			**lowerCAmelCase			,			)							->       None:
     super().__init__(**lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   size if size is not None else {'''shortest_edge''': 224}
     SCREAMING_SNAKE_CASE__: Any=   get_size_dict(lowerCAmelCase			,			default_to_square=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[int]=   crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
     SCREAMING_SNAKE_CASE__: Tuple=   get_size_dict(lowerCAmelCase			,			param_name='''crop_size''' )

     SCREAMING_SNAKE_CASE__: List[str]=   do_resize
     SCREAMING_SNAKE_CASE__: List[Any]=   size
     SCREAMING_SNAKE_CASE__: Optional[int]=   resample
     SCREAMING_SNAKE_CASE__: Any=   do_rescale
     SCREAMING_SNAKE_CASE__: str=   rescale_factor
     SCREAMING_SNAKE_CASE__: str=   do_center_crop
     SCREAMING_SNAKE_CASE__: int=   crop_size
     SCREAMING_SNAKE_CASE__: List[str]=   do_flip_channel_order
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = PIL.Image.BILINEAR			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       np.ndarray:
     SCREAMING_SNAKE_CASE__: Any=   get_size_dict(lowerCAmelCase			,			default_to_square=lowerCAmelCase )
     if "shortest_edge" not in size:
      raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}' )
     SCREAMING_SNAKE_CASE__: Dict=   get_resize_output_image_size(lowerCAmelCase			,			size=size['''shortest_edge''']			,			default_to_square=lowerCAmelCase )
     return resize(lowerCAmelCase			,			size=lowerCAmelCase			,			resample=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       np.ndarray:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   get_size_dict(lowerCAmelCase )
     if "height" not in size or "width" not in size:
      raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
     return center_crop(lowerCAmelCase			,			size=(size['''height'''], size['''width'''])			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       List[Any]:
     return rescale(lowerCAmelCase			,			scale=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None )							->       np.ndarray:
     return flip_channel_order(lowerCAmelCase			,			data_format=lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = ChannelDimension.FIRST			,			**lowerCAmelCase			,			)							->       PIL.Image.Image:
     SCREAMING_SNAKE_CASE__: int=   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__: List[str]=   resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__: List[str]=   do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__: Optional[int]=   rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__: str=   do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__: Optional[Any]=   (
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     SCREAMING_SNAKE_CASE__: List[Any]=   size if size is not None else self.size
     SCREAMING_SNAKE_CASE__: int=   get_size_dict(lowerCAmelCase			,			default_to_square=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[int]=   crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__: Optional[Any]=   get_size_dict(lowerCAmelCase			,			param_name='''crop_size''' )

     SCREAMING_SNAKE_CASE__: Tuple=   make_list_of_images(lowerCAmelCase )

     if not valid_images(lowerCAmelCase ):
      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_rescale and rescale_factor is None:
      raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

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

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__: Dict=   [to_numpy_array(lowerCAmelCase ) for image in images]

     if do_resize:
      SCREAMING_SNAKE_CASE__: Any=   [self.resize(image=lowerCAmelCase			,			size=lowerCAmelCase			,			resample=lowerCAmelCase ) for image in images]

     if do_center_crop:
      SCREAMING_SNAKE_CASE__: Dict=   [self.center_crop(image=lowerCAmelCase			,			size=lowerCAmelCase ) for image in images]

     if do_rescale:
      SCREAMING_SNAKE_CASE__: Dict=   [self.rescale(image=lowerCAmelCase			,			scale=lowerCAmelCase ) for image in images]

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
      SCREAMING_SNAKE_CASE__: Any=   [self.flip_channel_order(image=lowerCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__: int=   [to_channel_dimension_format(lowerCAmelCase			,			lowerCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__: Dict=   {'''pixel_values''': images}
     return BatchFeature(data=lowerCAmelCase			,			tensor_type=lowerCAmelCase )



    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
      if len(lowerCAmelCase ) != len(lowerCAmelCase ):
       raise ValueError(
           '''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )

      if is_torch_tensor(lowerCAmelCase ):
       SCREAMING_SNAKE_CASE__: Any=   target_sizes.numpy()

      SCREAMING_SNAKE_CASE__: Any=   []

      for idx in range(len(lowerCAmelCase ) ):
       SCREAMING_SNAKE_CASE__: str=   torch.nn.functional.interpolate(
           logits[idx].unsqueeze(dim=0 )			,			size=target_sizes[idx]			,			mode='''bilinear'''			,			align_corners=lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: List[Any]=   resized_logits[0].argmax(dim=0 )
       semantic_segmentation.append(lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: List[Any]=   logits.argmax(dim=1 )
      SCREAMING_SNAKE_CASE__: List[Any]=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

     return semantic_segmentation








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

@dataclass
class   lowerCAmelCase				(   datasets.BuilderConfig     ):
 UpperCAmelCase__						=						1_00_00
 UpperCAmelCase__						=						None
 UpperCAmelCase__						=						None


class   lowerCAmelCase				(   datasets.ArrowBasedBuilder     ):
 UpperCAmelCase__						=						ParquetConfig
 def 	A_      ( self    : int      )     ->      Optional[int]:
     return datasets.DatasetInfo(features=self.config.features      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : str      )     ->      Any:
     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}"""      )
     lowerCamelCase__       : List[Any]   					=						dl_manager.download_and_extract(self.config.data_files      )
     if isinstance(UpperCAmelCase     ,	(str, list, tuple)      ):
         lowerCamelCase__       : int   					=						data_files
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : int   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : str   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,	gen_kwargs={'files': files}      )]
     lowerCamelCase__       : Optional[Any]   					=						[]
     for split_name, files in data_files.items():
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : Optional[Any]   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : Dict   					=						[dl_manager.iter_files(UpperCAmelCase      ) 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(UpperCAmelCase      ):
                 with open(UpperCAmelCase     ,	'rb'      ) as f:
                     lowerCamelCase__       : Optional[Any]   					=						datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase      )      )
                 break
         splits.append(datasets.SplitGenerator(name=UpperCAmelCase     ,	gen_kwargs={'files': files}      )      )
     return splits
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : pa.Table      )     ->      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
         lowerCamelCase__       : Any   					=						table_cast(UpperCAmelCase     ,	self.info.features.arrow_schema      )
     return pa_table







 def 	A_      ( self    : Any     ,	UpperCAmelCase    : Tuple      )     ->      List[str]:
     lowerCamelCase__       : Optional[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(UpperCAmelCase      )      ):
         with open(UpperCAmelCase     ,	'rb'      ) as f:
             lowerCamelCase__       : List[Any]   					=						pq.ParquetFile(UpperCAmelCase      )
             try:
                 for batch_idx, record_batch in enumerate(
                     parquet_file.iter_batches(batch_size=self.config.batch_size     ,	columns=self.config.columns      )      ):
                     lowerCamelCase__       : Tuple   					=						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(UpperCAmelCase      )
             except ValueError as e:
                 logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase      )}: {e}"""      )
                 raise





