



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     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 32,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = 1 / 255,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],      SCREAMING_SNAKE_CASE_ = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=3,      )					->		Tuple:
          UpperCamelCase   :  int						     =     parent
          UpperCamelCase   :  str						     =     do_resize
          UpperCamelCase   :  Tuple						     =     size if size is not None else {'shortest_edge': 288}
          UpperCamelCase   :  Any						     =     size_divisor
          UpperCamelCase   :  str						     =     do_rescale
          UpperCamelCase   :  Dict						     =     rescale_factor
          UpperCamelCase   :  Union[str, Any]						     =     do_normalize
          UpperCamelCase   :  Any						     =     do_center_crop
          UpperCamelCase   :  Optional[Any]						     =     image_mean
          UpperCamelCase   :  Tuple						     =     image_std
          UpperCamelCase   :  Optional[int]						     =     do_pad
          UpperCamelCase   :  Dict						     =     batch_size
          UpperCamelCase   :  Tuple						     =     num_channels
          UpperCamelCase   :  int						     =     min_resolution
          UpperCamelCase   :  Optional[int]						     =     max_resolution
       def  snake_case_	(      self	)					->		Union[str, Any]:
          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  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=False	)					->		Optional[Any]:
          if not batched:
             UpperCamelCase   :  Optional[int]						     =     self.size['shortest_edge']
             UpperCamelCase   :  Any						     =     image_inputs[0]
             if isinstance(SCREAMING_SNAKE_CASE_,      Image.Image	):
                UpperCamelCase   :  List[Any]						     =     image.size
             else:
                UpperCamelCase   :  Tuple						     =     image.shape[1], image.shape[2]
             UpperCamelCase   :  Union[str, Any]						     =     size / min(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             if h < w:
                UpperCamelCase   :  int						     =     size, scale * w
             else:
                UpperCamelCase   :  str						     =     scale * h, size

             UpperCamelCase   :  int						     =     int((1333 / 800) * size	)
             if max(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) > max_size:
                UpperCamelCase   :  Union[str, Any]						     =     max_size / max(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
                UpperCamelCase   :  int						     =     newh * scale
                UpperCamelCase   :  Any						     =     neww * scale

             UpperCamelCase   :  List[str]						     =     int(newh + 0.5	), int(neww + 0.5	)
             UpperCamelCase   :  Optional[int]						     =     (
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

          else:
             UpperCamelCase   :  Any						     =     []
             for image in image_inputs:
                UpperCamelCase   :  int						     =     self.get_expected_values([image]	)
                expected_values.append((expected_height, expected_width)	)
             UpperCamelCase   :  Dict						     =     max(SCREAMING_SNAKE_CASE_,      key=lambda SCREAMING_SNAKE_CASE_	: item[0]	)[0]
             UpperCamelCase   :  Tuple						     =     max(SCREAMING_SNAKE_CASE_,      key=lambda SCREAMING_SNAKE_CASE_	: item[1]	)[1]

          return expected_height, expected_width






@require_torch
@require_vision
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Any       					=  BridgeTowerImageProcessor if is_vision_available() else None
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  int						     =     BridgeTowerImageProcessingTester(self	)
       @property
       def  snake_case_	(      self	)					->		Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Dict						     =     self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_mean'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_std'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_normalize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_resize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'size'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'size_divisor'	)	)
       def  snake_case_	(      self	)					->		int:
          pass
       def  snake_case_	(      self	)					->		Dict:
          # Initialize image processor
          UpperCamelCase   :  Union[str, Any]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          UpperCamelCase   :  Any						     =     prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image	)

          # Test not batched input
          UpperCamelCase   :  Optional[Any]						     =     image_processing(image_inputs[0],      return_tensors='pt'	).pixel_values

          UpperCamelCase   :  str						     =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              encoded_images.shape,      (1, self.image_processor_tester.num_channels, expected_height, expected_width),      )

          # Test batched
          UpperCamelCase   :  Union[str, Any]						     =     image_processing(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values

          UpperCamelCase   :  Tuple						     =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,      batched=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),      )
       def  snake_case_	(      self	)					->		List[Any]:
          # Initialize image processor
          UpperCamelCase   :  Union[str, Any]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          UpperCamelCase   :  Tuple						     =     prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)

          # Test not batched input
          UpperCamelCase   :  Union[str, Any]						     =     image_processing(image_inputs[0],      return_tensors='pt'	).pixel_values

          UpperCamelCase   :  Optional[Any]						     =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              encoded_images.shape,      (1, self.image_processor_tester.num_channels, expected_height, expected_width),      )

          # Test batched
          UpperCamelCase   :  List[str]						     =     image_processing(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values

          UpperCamelCase   :  Union[str, Any]						     =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,      batched=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),      )






       def  snake_case_	(      self	)					->		Tuple:
          # Initialize image processor
          UpperCamelCase   :  Optional[int]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          UpperCamelCase   :  List[str]						     =     prepare_image_inputs(self.image_processor_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)

          # Test not batched input
          UpperCamelCase   :  List[Any]						     =     image_processing(image_inputs[0],      return_tensors='pt'	).pixel_values

          UpperCamelCase   :  Tuple						     =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              encoded_images.shape,      (1, self.image_processor_tester.num_channels, expected_height, expected_width),      )

          # Test batched
          UpperCamelCase   :  Any						     =     image_processing(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values

          UpperCamelCase   :  Any						     =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,      batched=SCREAMING_SNAKE_CASE_	)
          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     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_,      )					->		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   :  Any						     =     load_from_cache_file
          UpperCamelCase   :  Any						     =     file_format
          UpperCamelCase   :  str						     =     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	)					->		Tuple:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split	)
          UpperCamelCase   :  Dict						     =     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	)



