

"""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 PoolFormerImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				List[str]   ,lowercase_			:				Tuple   ,lowercase_			:				int=7   ,lowercase_			:				List[str]=3   ,lowercase_			:				Any=3_0   ,lowercase_			:				int=4_0_0   ,lowercase_			:				List[str]=True   ,lowercase_			:				Tuple=None   ,lowercase_			:				int=0.9   ,lowercase_			:				str=None   ,lowercase_			:				List[str]=True   ,lowercase_			:				str=[0.5, 0.5, 0.5]   ,lowercase_			:				Optional[Any]=[0.5, 0.5, 0.5]   ,):
						lowerCAmelCase__	:						Optional[int]  				=	size if size is not None else {'''shortest_edge''': 3_0}
						lowerCAmelCase__	:						int  				=	crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
						lowerCAmelCase__	:						List[str]  				=	parent
						lowerCAmelCase__	:						Any  				=	batch_size
						lowerCAmelCase__	:						Union[str, Any]  				=	num_channels
						lowerCAmelCase__	:						List[str]  				=	min_resolution
						lowerCAmelCase__	:						int  				=	max_resolution
						lowerCAmelCase__	:						Any  				=	do_resize_and_center_crop
						lowerCAmelCase__	:						Tuple  				=	size
						lowerCAmelCase__	:						List[str]  				=	crop_pct
						lowerCAmelCase__	:						Union[str, Any]  				=	crop_size
						lowerCAmelCase__	:						Any  				=	do_normalize
						lowerCAmelCase__	:						int  				=	image_mean
						lowerCAmelCase__	:						List[Any]  				=	image_std







					def        __lowerCAmelCase (       self			:				Dict		):
						return {
						    "size": self.size,
						    "do_resize_and_center_crop": self.do_resize_and_center_crop,
						    "crop_pct": self.crop_pct,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}







@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       PoolFormerImageProcessor if is_vision_available() else None







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						str  				=	PoolFormerImageProcessingTester(self		)







					@property
					def        __lowerCAmelCase (       self			:				List[Any]		):
						return self.image_processor_tester.prepare_image_processor_dict()







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(lowercase_   ,'''do_resize_and_center_crop'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''size'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''crop_pct'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_mean'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_std'''		)		)







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size   ,{'''shortest_edge''': 3_0}		)
						self.assertEqual(image_processor.crop_size   ,{'''height''': 3_0, '''width''': 3_0}		)

						lowerCAmelCase__	:						Union[str, Any]  				=	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[Any]		):
						pass







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						# Initialize image_processing
						lowerCAmelCase__	:						Dict  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						lowerCAmelCase__	:						Tuple  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,Image.Image		)

						# Test not batched input
						lowerCAmelCase__	:						Dict  				=	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
						lowerCAmelCase__	:						Optional[Any]  				=	image_processing(lowercase_   ,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			:				Tuple		):
						# Initialize image_processing
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						lowerCAmelCase__	:						List[Any]  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,numpify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,np.ndarray		)

						# Test not batched input
						lowerCAmelCase__	:						Optional[Any]  				=	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
						lowerCAmelCase__	:						Any  				=	image_processing(lowercase_   ,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			:				Optional[int]		):
						# Initialize image_processing
						lowerCAmelCase__	:						int  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						lowerCAmelCase__	:						List[str]  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,torchify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,torch.Tensor		)

						# Test not batched input
						lowerCAmelCase__	:						List[str]  				=	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
						lowerCAmelCase__	:						List[str]  				=	image_processing(lowercase_   ,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'''],
						    )   ,)









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 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = "arrow",			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[int]:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			keep_in_memory=SCREAMING_SNAKE_CASE_,			streaming=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  List[str]							=	load_from_cache_file
          UpperCamelCase    :  List[str]							=	file_format
          UpperCamelCase    :  Optional[int]							=	Spark(
              df=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			working_dir=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)






       def 					snake_case_       (			self       )     ->		int:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split       )
          UpperCamelCase    :  Union[str, Any]							=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,			file_format=self._file_format,			)
          return self.builder.as_dataset(split=self.split       )



