





'''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 MobileNetVaImageProcessor

class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 def __init__(       self       :							Any							,	__A       :							Union[str, Any]							,	__A       :							Tuple=7							,	__A       :							Tuple=3							,	__A       :							Optional[int]=1_8							,	__A       :							int=3_0							,	__A       :							Any=4_0_0							,	__A       :							Optional[Any]=True							,	__A       :							List[Any]=None							,	__A       :							Union[str, Any]=True							,	__A       :							Any=None							,	):
        __UpperCamelCase       =  size if size is not None else {'shortest_edge': 2_0}
        __UpperCamelCase       =  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  batch_size
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  min_resolution
        __UpperCamelCase       =  max_resolution
        __UpperCamelCase       =  do_resize
        __UpperCamelCase       =  size
        __UpperCamelCase       =  do_center_crop
        __UpperCamelCase       =  crop_size






 def        _lowerCamelCase					(       self       :							Tuple	):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }






@require_torch
@require_vision
class      snake_case     (    __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=MobileNetVaImageProcessor if is_vision_available() else None






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  MobileNetVaImageProcessingTester(self	)






 @property
 def        _lowerCamelCase					(       self       :							int	):
        return self.image_processor_tester.prepare_image_processor_dict()






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(__A							,	'do_resize'	)	)
        self.assertTrue(hasattr(__A							,	'size'	)	)
        self.assertTrue(hasattr(__A							,	'do_center_crop'	)	)
        self.assertTrue(hasattr(__A							,	'crop_size'	)	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size							,	{'shortest_edge': 2_0}	)
        self.assertEqual(image_processor.crop_size							,	{'height': 1_8, 'width': 1_8}	)

        __UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict							,	size=4_2							,	crop_size=8_4	)
        self.assertEqual(image_processor.size							,	{'shortest_edge': 4_2}	)
        self.assertEqual(image_processor.crop_size							,	{'height': 8_4, 'width': 8_4}	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        pass






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        # Initialize image_processing
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        __UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A	)
        for image in image_inputs:
               self.assertIsInstance(__A							,	Image.Image	)

        # Test not batched input
        __UpperCamelCase       =  image_processing(image_inputs[0]							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)

        # Test batched
        __UpperCamelCase       =  image_processing(__A							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        # Initialize image_processing
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        __UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	numpify=__A	)
        for image in image_inputs:
               self.assertIsInstance(__A							,	np.ndarray	)

        # Test not batched input
        __UpperCamelCase       =  image_processing(image_inputs[0]							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)

        # Test batched
        __UpperCamelCase       =  image_processing(__A							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        # Initialize image_processing
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        __UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	torchify=__A	)
        for image in image_inputs:
               self.assertIsInstance(__A							,	torch.Tensor	)

        # Test not batched input
        __UpperCamelCase       =  image_processing(image_inputs[0]							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)

        # Test batched
        __UpperCamelCase       =  image_processing(__A							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)







'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def 				lowercase__   (   __lowercase						:  Features     ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  np.inf

       def set_batch_size(__lowercase						:  FeatureType     ) -> None:
              nonlocal batch_size
              if isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ) and feature.dtype == "binary":
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

       _visit(__lowercase				,  __lowercase     )

       return None if batch_size is np.inf else batch_size

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							NestedDataStructureLike[PathLike]							,	__A       :							Optional[NamedSplit] = None							,	__A       :							Optional[Features] = None							,	__A       :							str = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        super().__init__(
            __A							,	split=__A							,	features=__A							,	cache_dir=__A							,	keep_in_memory=__A							,	streaming=__A							,	num_proc=__A							,	**__A							,	)
        __UpperCamelCase       =  path_or_paths if isinstance(__A							,	__A	) else {self.split: path_or_paths}
        __UpperCamelCase       =  _PACKAGED_DATASETS_MODULES['parquet'][1]
        __UpperCamelCase       =  Parquet(
            cache_dir=__A							,	data_files=__A							,	features=__A							,	hash=__A							,	**__A							,	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        # Build iterable dataset
        if self.streaming:
               __UpperCamelCase       =  self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None

               self.builder.download_and_prepare(
                   download_config=__A							,	download_mode=__A							,	verification_mode=__A							,	base_path=__A							,	num_proc=self.num_proc							,	)
               __UpperCamelCase       =  self.builder.as_dataset(
                   split=self.split							,	verification_mode=__A							,	in_memory=self.keep_in_memory	)
        return dataset






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							Dataset							,	__A       :							Union[PathLike, BinaryIO]							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  path_or_buf
        __UpperCamelCase       =  batch_size or get_writer_batch_size(dataset.features	)
        __UpperCamelCase       =  parquet_writer_kwargs






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf							,	(str, bytes, os.PathLike)	):
               with open(self.path_or_buf							,	'wb+'	) as buffer:
                      __UpperCamelCase       =  self._write(file_obj=__A							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        else:
               __UpperCamelCase       =  self._write(file_obj=self.path_or_buf							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        return written






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							BinaryIO							,	__A       :							int							,	**__A       :							List[str]	):
        __UpperCamelCase       =  0
        __UpperCamelCase       =  parquet_writer_kwargs.pop('path_or_buf'							,	__A	)
        __UpperCamelCase       =  self.dataset.features.arrow_schema

        __UpperCamelCase       =  pq.ParquetWriter(__A							,	schema=__A							,	**__A	)

        for offset in logging.tqdm(
            range(0							,	len(self.dataset	)							,	__A	)							,	unit='ba'							,	disable=not logging.is_progress_bar_enabled()							,	desc='Creating parquet from Arrow format'							,	):
               __UpperCamelCase       =  query_table(
                   table=self.dataset._data							,	key=slice(__A							,	offset + batch_size	)							,	indices=self.dataset._indices if self.dataset._indices is not None else None							,	)
               writer.write_table(__A	)
               written += batch.nbytes
        writer.close()
        return written
