



import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class 		lowercase						(			unittest.TestCase):






 """simple docstring"""


 def __init__(     self     :						List[str]						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						Optional[int] = True						,  __UpperCAmelCase     :						Optional[int] = None						,  __UpperCAmelCase     :						Dict = 32						,  __UpperCAmelCase     :						Optional[Any] = True						,  __UpperCAmelCase     :						Any = 1 / 255						,  __UpperCAmelCase     :						Any = True						,  __UpperCAmelCase     :						str = True						,  __UpperCAmelCase     :						Optional[Any] = [0.48_145_466, 0.4_578_275, 0.40_821_073]						,  __UpperCAmelCase     :						Tuple = [0.26_862_954, 0.26_130_258, 0.27_577_711]						,  __UpperCAmelCase     :						List[Any] = True						,  __UpperCAmelCase     :						Dict=7						,  __UpperCAmelCase     :						Optional[Any]=30						,  __UpperCAmelCase     :						int=400						,  __UpperCAmelCase     :						Union[str, Any]=3						,  )      ->			List[Any]:
       UpperCAmelCase_=       parent
       UpperCAmelCase_=       do_resize
       UpperCAmelCase_=       size if size is not None else {"""shortest_edge""": 288}
       UpperCAmelCase_=       size_divisor
       UpperCAmelCase_=       do_rescale
       UpperCAmelCase_=       rescale_factor
       UpperCAmelCase_=       do_normalize
       UpperCAmelCase_=       do_center_crop
       UpperCAmelCase_=       image_mean
       UpperCAmelCase_=       image_std
       UpperCAmelCase_=       do_pad
       UpperCAmelCase_=       batch_size
       UpperCAmelCase_=       num_channels
       UpperCAmelCase_=       min_resolution
       UpperCAmelCase_=       max_resolution




 def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Tuple:
       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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						Dict=False					)      ->			Any:







       if not batched:
             UpperCAmelCase_=       self.size["""shortest_edge"""]
             UpperCAmelCase_=       image_inputs[0]
             if isinstance(__UpperCAmelCase						,  Image.Image					):
                   UpperCAmelCase_,       UpperCAmelCase_=       image.size
             else:
                   UpperCAmelCase_,       UpperCAmelCase_=       image.shape[1], image.shape[2]
             UpperCAmelCase_=       size / min(__UpperCAmelCase						,  __UpperCAmelCase					)
             if h < w:
                   UpperCAmelCase_,       UpperCAmelCase_=       size, scale * w
             else:
                   UpperCAmelCase_,       UpperCAmelCase_=       scale * h, size

             UpperCAmelCase_=       int((1_333 / 800) * size					)
             if max(__UpperCAmelCase						,  __UpperCAmelCase					) > max_size:
                   UpperCAmelCase_=       max_size / max(__UpperCAmelCase						,  __UpperCAmelCase					)
                   UpperCAmelCase_=       newh * scale
                   UpperCAmelCase_=       neww * scale

             UpperCAmelCase_,       UpperCAmelCase_=       int(newh + 0.5					), int(neww + 0.5					)
             UpperCAmelCase_,       UpperCAmelCase_=       (
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

       else:
             UpperCAmelCase_=       []
             for image in image_inputs:
                   UpperCAmelCase_,       UpperCAmelCase_=       self.get_expected_values([image]					)
                   expected_values.append((expected_height, expected_width)					)
             UpperCAmelCase_=       max(__UpperCAmelCase						,  key=lambda __UpperCAmelCase					: item[0]					)[0]
             UpperCAmelCase_=       max(__UpperCAmelCase						,  key=lambda __UpperCAmelCase					: item[1]					)[1]

       return expected_height, expected_width






@require_torch
@require_vision
class 		lowercase						(			lowerCamelCase__			,   unittest.TestCase):






 """simple docstring"""


 a__				: Optional[Any]		       =							BridgeTowerImageProcessor if is_vision_available() else None




 def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Any:
       UpperCAmelCase_=       BridgeTowerImageProcessingTester(self					)




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Any:
       return self.image_processor_tester.prepare_image_processor_dict()




 def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Union[str, Any]:
       UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(__UpperCAmelCase						,  """image_mean"""					)					)
       self.assertTrue(hasattr(__UpperCAmelCase						,  """image_std"""					)					)
       self.assertTrue(hasattr(__UpperCAmelCase						,  """do_normalize"""					)					)
       self.assertTrue(hasattr(__UpperCAmelCase						,  """do_resize"""					)					)
       self.assertTrue(hasattr(__UpperCAmelCase						,  """size"""					)					)
       self.assertTrue(hasattr(__UpperCAmelCase						,  """size_divisor"""					)					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Tuple:
       pass




 def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			str:
       UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase					)
       for image in image_inputs:
             self.assertIsInstance(__UpperCAmelCase						,  Image.Image					)

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

       UpperCAmelCase_,       UpperCAmelCase_=       self.image_processor_tester.get_expected_values(__UpperCAmelCase					)
       self.assertEqual(
           encoded_images.shape						,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,  )

       # Test batched
       UpperCAmelCase_=       image_processing(__UpperCAmelCase						,  return_tensors="""pt"""					).pixel_values

       UpperCAmelCase_,       UpperCAmelCase_=       self.image_processor_tester.get_expected_values(__UpperCAmelCase						,  batched=__UpperCAmelCase					)
       self.assertEqual(
           encoded_images.shape						,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,  )




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			int:
       UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       UpperCAmelCase_=       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
       UpperCAmelCase_=       image_processing(image_inputs[0]						,  return_tensors="""pt"""					).pixel_values

       UpperCAmelCase_,       UpperCAmelCase_=       self.image_processor_tester.get_expected_values(__UpperCAmelCase					)
       self.assertEqual(
           encoded_images.shape						,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,  )

       # Test batched
       UpperCAmelCase_=       image_processing(__UpperCAmelCase						,  return_tensors="""pt"""					).pixel_values

       UpperCAmelCase_,       UpperCAmelCase_=       self.image_processor_tester.get_expected_values(__UpperCAmelCase						,  batched=__UpperCAmelCase					)
       self.assertEqual(
           encoded_images.shape						,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,  )




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Optional[int]:
       UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       UpperCAmelCase_=       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
       UpperCAmelCase_=       image_processing(image_inputs[0]						,  return_tensors="""pt"""					).pixel_values

       UpperCAmelCase_,       UpperCAmelCase_=       self.image_processor_tester.get_expected_values(__UpperCAmelCase					)
       self.assertEqual(
           encoded_images.shape						,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,  )

       # Test batched
       UpperCAmelCase_=       image_processing(__UpperCAmelCase						,  return_tensors="""pt"""					).pixel_values

       UpperCAmelCase_,       UpperCAmelCase_=       self.image_processor_tester.get_expected_values(__UpperCAmelCase						,  batched=__UpperCAmelCase					)
       self.assertEqual(
           encoded_images.shape						,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_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 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						pyspark.sql.DataFrame						,  __UpperCAmelCase     :						Optional[NamedSplit] = None						,  __UpperCAmelCase     :						Optional[Features] = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = "arrow"						,  **__UpperCAmelCase     :						str						,  )      ->			Dict:
							super().__init__(
							    split=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  keep_in_memory=__UpperCAmelCase						,  streaming=__UpperCAmelCase						,  **__UpperCAmelCase						,  )
							UpperCAmelCase_=       load_from_cache_file
							UpperCAmelCase_=       file_format
							UpperCAmelCase_=       Spark(
							    df=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  working_dir=__UpperCAmelCase						,  **__UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							if self.streaming:
													return self.builder.as_streaming_dataset(split=self.split					)
							UpperCAmelCase_=       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					)




