





'''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
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam
class 					lowerCAmelCase	(					datasets.BeamBasedBuilder    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							Dict:
								return datasets.DatasetInfo(
								    features=datasets.Features({'content': datasets.Value('string'				)}				)						,       supervised_keys=lowerCAmelCase__						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							str:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,       gen_kwargs={'examples': get_test_dummy_examples()}				)]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[int]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__				)





class 					lowerCAmelCase	(					datasets.BeamBasedBuilder    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							int:
								return datasets.DatasetInfo(
								    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)						,       supervised_keys=lowerCAmelCase__						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Dict:
								return [
								    datasets.SplitGenerator(name=datasets.Split.TRAIN						,       gen_kwargs={'examples': get_test_nested_examples()}				)
								]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[Any]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__				)





def     lowercase			()   ->						str:
						return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']  )]


def     lowercase			()   ->						Optional[Any]:
						return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']  )]


class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		@require_beam
		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					len(get_test_dummy_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train.arrow'				)				)				)
														self.assertDictEqual(builder.info.features						,       datasets.Features({'content': datasets.Value('string'				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														self.assertDictEqual(dset['train'][0]						,       get_test_dummy_examples()[0][1]				)
														self.assertDictEqual(
														    dset['train'][expected_num_examples - 1]						,       get_test_dummy_examples()[expected_num_examples - 1][1]				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset







		@require_beam
		def 							__A						(	self				)     ->							int:
								import apache_beam as beam

								SCREAMING_SNAKE_CASE													=					beam.io.parquetio.WriteToParquet

								SCREAMING_SNAKE_CASE													=					len(get_test_dummy_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														with patch('apache_beam.io.parquetio.WriteToParquet'				) as write_parquet_mock:
																				SCREAMING_SNAKE_CASE													=					partial(lowerCAmelCase__						,       num_shards=2				)
																				builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train-00000-of-00002.arrow'				)				)				)
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train-00000-of-00002.arrow'				)				)				)
														self.assertDictEqual(builder.info.features						,       datasets.Features({'content': datasets.Value('string'				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														# Order is not preserved when sharding, so we just check that all the elements are there
														self.assertListEqual(sorted(dset['train']['content']				)						,       sorted(['foo', 'bar', 'foobar']				)				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset







		@require_beam
		def 							__A						(	self				)     ->							Optional[Any]:
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__				)
														self.assertRaises(datasets.builder.MissingBeamOptions						,       builder.download_and_prepare				)







		@require_beam
		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					len(get_test_nested_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					NestedBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train.arrow'				)				)				)
														self.assertDictEqual(
														    builder.info.features						,       datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														self.assertDictEqual(dset['train'][0]						,       get_test_nested_examples()[0][1]				)
														self.assertDictEqual(
														    dset['train'][expected_num_examples - 1]						,       get_test_nested_examples()[expected_num_examples - 1][1]				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset


