




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 GLPNImageProcessor


class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=7       , UpperCAmelCase_=3       , UpperCAmelCase_=18       , UpperCAmelCase_=30       , UpperCAmelCase_=4_00       , UpperCAmelCase_=True       , UpperCAmelCase_=32       , UpperCAmelCase_=True       , ):
											lowerCAmelCase   =		parent
											lowerCAmelCase   =		batch_size
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		image_size
											lowerCAmelCase   =		min_resolution
											lowerCAmelCase   =		max_resolution
											lowerCAmelCase   =		do_resize
											lowerCAmelCase   =		size_divisor
											lowerCAmelCase   =		do_rescale






				def       __snake_case		( self						):
											return {
											    "do_resize": self.do_resize,
											    "size_divisor": self.size_divisor,
											    "do_rescale": self.do_rescale,
											}




@require_torch
@require_vision
class 							__UpperCamelCase			( __UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Dict										=GLPNImageProcessor if is_vision_available() else None



				def       __snake_case		( self						):
											lowerCAmelCase   =		GLPNImageProcessingTester(self						)



				@property
				def       __snake_case		( self						):
											return self.image_processor_tester.prepare_image_processor_dict()



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''do_resize'''						)						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''size_divisor'''						)						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''resample'''						)						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''do_rescale'''						)						)



				def       __snake_case		( self						):
											pass



				def       __snake_case		( self						):
											# Initialize image_processing
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											lowerCAmelCase   =		prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_						)
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_       , Image.Image						)

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											lowerCAmelCase   =		image_processing(image_inputs[0]       , return_tensors='''pt'''						).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)



				def       __snake_case		( self						):
											# Initialize image_processing
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											lowerCAmelCase   =		prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , numpify=UpperCAmelCase_						)
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_       , np.ndarray						)

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											lowerCAmelCase   =		image_processing(image_inputs[0]       , return_tensors='''pt'''						).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)






				def       __snake_case		( self						):
											# Initialize image_processing
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											lowerCAmelCase   =		prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , torchify=UpperCAmelCase_						)
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_       , torch.Tensor						)

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											lowerCAmelCase   =		image_processing(image_inputs[0]       , return_tensors='''pt'''						).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)









from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = "arrow"       , **UpperCAmelCase_       , ):
											super().__init__(
											    split=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_       , streaming=UpperCAmelCase_       , **UpperCAmelCase_       , )
											lowerCAmelCase   =		load_from_cache_file
											lowerCAmelCase   =		file_format
											lowerCAmelCase   =		Spark(
											    df=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , working_dir=UpperCAmelCase_       , **UpperCAmelCase_       , )






				def       __snake_case		( self						):
											if self.streaming:
																		return self.builder.as_streaming_dataset(split=self.split						)
											lowerCAmelCase   =		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
											self.builder.download_and_prepare(
											    download_mode=UpperCAmelCase_       , file_format=self._file_format       , )
											return self.builder.as_dataset(split=self.split						)



