'''simple docstring'''





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class __snake_case       :




   def __init__(					self    :       Optional[int] ,			_lowercase    :       Optional[int] ,			_lowercase    :       str=sys.maxsize   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """bilinear"""
     SCREAMING_SNAKE_CASE__    							=   max_size
     SCREAMING_SNAKE_CASE__    							=   short_edge_length




   def __call__(					self    :       Tuple ,			_lowercase    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   []
     for img in imgs:
       SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   img.shape[:2]
       # later: provide list and randomly choose index for resize
       SCREAMING_SNAKE_CASE__    							=   np.random.randint(self.short_edge_length[0] ,			self.short_edge_length[1] + 1   )
       if size == 0:
         return img
       SCREAMING_SNAKE_CASE__    							=   size * 1.0 / min(_lowercase ,			_lowercase   )
       if h < w:
         SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   size, scale * w
       else:
         SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   scale * h, size
       if max(_lowercase ,			_lowercase   ) > self.max_size:
         SCREAMING_SNAKE_CASE__    							=   self.max_size * 1.0 / max(_lowercase ,			_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   newh * scale
         SCREAMING_SNAKE_CASE__    							=   neww * scale
       SCREAMING_SNAKE_CASE__    							=   int(neww + 0.5   )
       SCREAMING_SNAKE_CASE__    							=   int(newh + 0.5   )

       if img.dtype == np.uinta:
         SCREAMING_SNAKE_CASE__    							=   Image.fromarray(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   pil_image.resize((neww, newh) ,			PILImageResampling.BILINEAR   )
         SCREAMING_SNAKE_CASE__    							=   np.asarray(_lowercase   )
       else:
         SCREAMING_SNAKE_CASE__    							=   img.permute(2 ,			0 ,			1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
         SCREAMING_SNAKE_CASE__    							=   nn.functional.interpolate(
             _lowercase ,			(newh, neww) ,			mode=self.interp_method ,			align_corners=_lowercase   ).squeeze(0   )
       img_augs.append(_lowercase   )

     return img_augs







class __snake_case       :




   def __init__(					self    :       Optional[int] ,			_lowercase    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,			cfg.INPUT.MAX_SIZE_TEST   )
     SCREAMING_SNAKE_CASE__    							=   cfg.INPUT.FORMAT
     SCREAMING_SNAKE_CASE__    							=   cfg.SIZE_DIVISIBILITY
     SCREAMING_SNAKE_CASE__    							=   cfg.PAD_VALUE
     SCREAMING_SNAKE_CASE__    							=   cfg.INPUT.MAX_SIZE_TEST
     SCREAMING_SNAKE_CASE__    							=   cfg.MODEL.DEVICE
     SCREAMING_SNAKE_CASE__    							=   torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   ) ,			1 ,			1   )
     SCREAMING_SNAKE_CASE__    							=   torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   ) ,			1 ,			1   )
     SCREAMING_SNAKE_CASE__    							=   lambda _lowercase   : (x - self.pixel_mean) / self.pixel_std




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   tuple(max(_lowercase   ) for s in zip(*[img.shape for img in images]   )   )
     SCREAMING_SNAKE_CASE__    							=   [im.shape[-2:] for im in images]
     SCREAMING_SNAKE_CASE__    							=   [
         nn.functional.pad(
             _lowercase ,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,			value=self.pad_value ,			)
         for size, im in zip(_lowercase ,			_lowercase   )
     ]

     return torch.stack(_lowercase   ), torch.tensor(_lowercase   )




   def __call__(					self    :       str ,			_lowercase    :       List[Any] ,			_lowercase    :       Dict=False   ):



     """simple docstring"""






     with torch.no_grad():
       if not isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [images]
       if single_image:
         assert len(_lowercase   ) == 1
       for i in range(len(_lowercase   )   ):
         if isinstance(images[i] ,			torch.Tensor   ):
           images.insert(_lowercase ,			images.pop(_lowercase   ).to(self.device   ).float()   )
         elif not isinstance(images[i] ,			torch.Tensor   ):
           images.insert(
               _lowercase ,			torch.as_tensor(img_tensorize(images.pop(_lowercase   ) ,			input_format=self.input_format   )   )
               .to(self.device   )
               .float() ,			)
            # resize smallest edge
       SCREAMING_SNAKE_CASE__    							=   torch.tensor([im.shape[:2] for im in images]   )
       SCREAMING_SNAKE_CASE__    							=   self.aug(_lowercase   )
       # transpose images and convert to torch tensors
       # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
       # now normalize before pad to avoid useless arithmetic
       SCREAMING_SNAKE_CASE__    							=   [self.normalizer(_lowercase   ) for x in images]
       # now pad them to do the following operations
       SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.pad(_lowercase   )
       # Normalize

       if self.size_divisibility > 0:
         raise NotImplementedError()
       # pad
       SCREAMING_SNAKE_CASE__    							=   torch.true_divide(_lowercase ,			_lowercase   )
       if single_image:
         return images[0], sizes[0], scales_yx[0]
       else:
         return images, sizes, scales_yx





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Any			)							->      str:







  """simple docstring"""





  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes






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







  """simple docstring"""





  assert torch.isfinite(__UpperCamelCase			).all(), "Box tensor contains infinite or NaN!"
  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   box_size
  tensor[:, 0].clamp_(min=0				,  max=__UpperCamelCase			)
  tensor[:, 1].clamp_(min=0				,  max=__UpperCamelCase			)
  tensor[:, 2].clamp_(min=0				,  max=__UpperCamelCase			)
  tensor[:, 3].clamp_(min=0				,  max=__UpperCamelCase			)

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


__lowerCamelCase    :				Union[str, Any]							     =	datasets.utils.logging.get_logger(__name__)







@dataclass
class __snake_case       (		datasets.BuilderConfig	):
   lowerCAmelCase_														=							1_00_00
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None







class __snake_case       (		datasets.ArrowBasedBuilder	):
   lowerCAmelCase_														=							ParquetConfig




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[str]   ):



     """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__    							=   dl_manager.download_and_extract(self.config.data_files   )
     if isinstance(_lowercase ,			(str, list, tuple)   ):
       SCREAMING_SNAKE_CASE__    							=   data_files
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,			gen_kwargs={"""files""": files}   )]
     SCREAMING_SNAKE_CASE__    							=   []
     for split_name, files in data_files.items():
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) 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(_lowercase   ):
           with open(_lowercase ,			"""rb"""   ) as f:
             SCREAMING_SNAKE_CASE__    							=   datasets.Features.from_arrow_schema(pq.read_schema(_lowercase   )   )
           break
       splits.append(datasets.SplitGenerator(name=_lowercase ,			gen_kwargs={"""files""": files}   )   )
     return splits




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       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__    							=   table_cast(_lowercase ,			self.info.features.arrow_schema   )
     return pa_table




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   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(_lowercase   )   ):
       with open(_lowercase ,			"""rb"""   ) as f:
         SCREAMING_SNAKE_CASE__    							=   pq.ParquetFile(_lowercase   )
         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__    							=   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(_lowercase   )
         except ValueError as e:
           logger.error(f"""Failed to read file '{file}' with error {type(_lowercase   )}: {e}"""   )
           raise
