



'''simple docstring'''





import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


UpperCamelCase__    :     Any			  =   datasets.utils.logging.get_logger(__name__)


@dataclass
class 							_lowerCAmelCase	(  datasets.BuilderConfig							):


 """simple docstring"""

 lowerCamelCase									=     None


class 							_lowerCAmelCase	(  datasets.ArrowBasedBuilder							):


 """simple docstring"""

 lowerCamelCase									=     ArrowConfig







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







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[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}"  )
    A_  :		int						=					dl_manager.download_and_extract(self.config.data_files  )
    if isinstance(_lowerCamelCase					,    (str, list, tuple)  ):
       A_  :		List[str]						=					data_files
       if isinstance(_lowerCamelCase					,    _lowerCamelCase  ):
          A_  :		Optional[Any]						=					[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       A_  :		Optional[Any]						=					[dl_manager.iter_files(_lowerCamelCase  ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,    gen_kwargs={"""files""": files}  )]
    A_  :		List[str]						=					[]
    for split_name, files in data_files.items():
       if isinstance(_lowerCamelCase					,    _lowerCamelCase  ):
          A_  :		Optional[Any]						=					[files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       A_  :		Optional[int]						=					[dl_manager.iter_files(_lowerCamelCase  ) 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(_lowerCamelCase  ):
             with open(_lowerCamelCase					,    """rb"""  ) as f:
                A_  :		Any						=					datasets.Features.from_arrow_schema(pa.ipc.open_stream(_lowerCamelCase  ).schema  )
             break
       splits.append(datasets.SplitGenerator(name=_lowerCamelCase					,    gen_kwargs={"""files""": files}  )  )
    return splits







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    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
       A_  :		int						=					table_cast(_lowerCamelCase					,    self.info.features.arrow_schema  )
    return pa_table







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCamelCase  )  ):
       with open(_lowerCamelCase					,    """rb"""  ) as f:
          try:
             for batch_idx, record_batch in enumerate(pa.ipc.open_stream(_lowerCamelCase  )  ):
                A_  :		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(_lowerCamelCase  )
          except ValueError as e:
             logger.error(F"Failed to read file '{file}' with error {type(_lowerCamelCase  )}: {e}"  )
             raise





'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViltImageProcessor


class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=7					,    _lowerCamelCase=3					,    _lowerCamelCase=18					,    _lowerCamelCase=30					,    _lowerCamelCase=400					,    _lowerCamelCase=True					,    _lowerCamelCase=None					,    _lowerCamelCase=2					,    _lowerCamelCase=True					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    )    ->    Tuple:
    A_  :		Dict						=					size if size is not None else {"""shortest_edge""": 30}
    A_  :		List[Any]						=					parent
    A_  :		Optional[Any]						=					batch_size
    A_  :		Tuple						=					num_channels
    A_  :		Any						=					image_size
    A_  :		str						=					min_resolution
    A_  :		str						=					max_resolution
    A_  :		Any						=					do_resize
    A_  :		List[Any]						=					size
    A_  :		Union[str, Any]						=					size_divisor
    A_  :		Dict						=					do_normalize
    A_  :		Dict						=					image_mean
    A_  :		Optional[Any]						=					image_std







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
        "size_divisor": self.size_divisor,
    }







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=False  )    ->    Union[str, Any]:
    if not batched:
       A_  :		List[str]						=					self.size["""shortest_edge"""]
       A_  :		str						=					image_inputs[0]
       if isinstance(_lowerCamelCase					,    Image.Image  ):
          A_						,  A_  :		Dict						=					image.size
       else:
          A_						,  A_  :		Dict						=					image.shape[1], image.shape[2]
       A_  :		Dict						=					size / min(_lowerCamelCase					,    _lowerCamelCase  )
       if h < w:
          A_						,  A_  :		Any						=					size, scale * w
       else:
          A_						,  A_  :		List[str]						=					scale * h, size

       A_  :		Dict						=					int((1333 / 800) * size  )
       if max(_lowerCamelCase					,    _lowerCamelCase  ) > max_size:
          A_  :		Dict						=					max_size / max(_lowerCamelCase					,    _lowerCamelCase  )
          A_  :		str						=					newh * scale
          A_  :		Tuple						=					neww * scale

       A_						,  A_  :		str						=					int(newh + 0.5  ), int(neww + 0.5  )
       A_						,  A_  :		Union[str, Any]						=					(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

    else:
       A_  :		Union[str, Any]						=					[]
       for image in image_inputs:
          A_						,  A_  :		Any						=					self.get_expected_values([image]  )
          expected_values.append((expected_height, expected_width)  )
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[0]  )[0]
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[1]  )[1]

    return expected_height, expected_width


@require_torch
@require_vision
class 							_lowerCAmelCase	(  __A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     ViltImageProcessor if is_vision_available() else None







 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Optional[int]						=					ViltImageProcessingTester(self  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    return self.image_processor_tester.prepare_image_processor_dict()







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_mean"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_std"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_normalize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_resize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size_divisor"""  )  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 30}  )

    A_  :		Union[str, Any]						=					self.image_processing_class.from_dict(self.image_processor_dict					,    size=42  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 42}  )







 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    # Initialize image_processing
    A_  :		Any						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    A_  :		Any						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    Image.Image  )

    # Test not batched input
    A_  :		Tuple						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[Any]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    str:
    # Initialize image_processing
    A_  :		Tuple						=					self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    A_  :		List[str]						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    numpify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    np.ndarray  )

    # Test not batched input
    A_  :		Any						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		int						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[str]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    # Initialize image_processing
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    A_  :		str						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    torchify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    torch.Tensor  )

    # Test not batched input
    A_  :		Optional[int]						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		List[str]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		Any						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )
