




'''simple docstring'''







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 					_a (unittest.TestCase	):





     '''simple docstring'''



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







     def 			snake_case_      (				self							)      ->				Optional[int]:
          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							,__a							,__a=False							)      ->				Union[str, Any]:
          if not batched:
               snake_case			:      Optional[Any]        =			self.size["""shortest_edge"""]
               snake_case			:      Any        =			image_inputs[0]
               if isinstance(__a							,Image.Image							):
                    snake_case ,		snake_case			:      Any        =			image.size
               else:
                    snake_case ,		snake_case			:      str        =			image.shape[1], image.shape[2]
               snake_case			:      Tuple        =			size / min(__a							,__a							)
               if h < w:
                    snake_case ,		snake_case			:      List[Any]        =			size, scale * w
               else:
                    snake_case ,		snake_case			:      List[Any]        =			scale * h, size

               snake_case			:      Union[str, Any]        =			int((1_333 / 800) * size							)
               if max(__a							,__a							) > max_size:
                    snake_case			:      Optional[int]        =			max_size / max(__a							,__a							)
                    snake_case			:      str        =			newh * scale
                    snake_case			:      int        =			neww * scale

               snake_case ,		snake_case			:      Dict        =			int(newh + 0.5							), int(neww + 0.5							)
               snake_case ,		snake_case			:      str        =			(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

          else:
               snake_case			:      Dict        =			[]
               for image in image_inputs:
                    snake_case ,		snake_case			:      str        =			self.get_expected_values([image]							)
                    expected_values.append((expected_height, expected_width)							)
               snake_case			:      Any        =			max(__a							,key=lambda __a							: item[0]							)[0]
               snake_case			:      Dict        =			max(__a							,key=lambda __a							: item[1]							)[1]

          return expected_height, expected_width







@require_torch
@require_vision
class 					_a (a__,		unittest.TestCase	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					BridgeTowerImageProcessor if is_vision_available() else None







     def 			snake_case_      (				self							)      ->				Any:
          snake_case			:      Union[str, Any]        =			BridgeTowerImageProcessingTester(self							)







     @property
     def 			snake_case_      (				self							)      ->				Any:
          return self.image_processor_tester.prepare_image_processor_dict()







     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      List[Any]        =			self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(__a							,"""image_mean"""							)							)
          self.assertTrue(hasattr(__a							,"""image_std"""							)							)
          self.assertTrue(hasattr(__a							,"""do_normalize"""							)							)
          self.assertTrue(hasattr(__a							,"""do_resize"""							)							)
          self.assertTrue(hasattr(__a							,"""size"""							)							)
          self.assertTrue(hasattr(__a							,"""size_divisor"""							)							)







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







     def 			snake_case_      (				self							)      ->				Union[str, Any]:
          # Initialize image processor
          snake_case			:      Optional[int]        =			self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          snake_case			:      Dict        =			prepare_image_inputs(self.image_processor_tester							,equal_resolution=__a							)
          for image in image_inputs:
               self.assertIsInstance(__a							,Image.Image							)

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

          snake_case ,		snake_case			:      List[Any]        =			self.image_processor_tester.get_expected_values(__a							)
          self.assertEqual(
              encoded_images.shape							,(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,)

          # Test batched
          snake_case			:      Optional[int]        =			image_processing(__a							,return_tensors="""pt"""							).pixel_values

          snake_case ,		snake_case			:      str        =			self.image_processor_tester.get_expected_values(__a							,batched=__a							)
          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							)      ->				Optional[int]:
          # Initialize image processor
          snake_case			:      int        =			self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          snake_case			:      List[str]        =			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
          snake_case			:      List[Any]        =			image_processing(image_inputs[0]							,return_tensors="""pt"""							).pixel_values

          snake_case ,		snake_case			:      Optional[int]        =			self.image_processor_tester.get_expected_values(__a							)
          self.assertEqual(
              encoded_images.shape							,(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,)

          # Test batched
          snake_case			:      Any        =			image_processing(__a							,return_tensors="""pt"""							).pixel_values

          snake_case ,		snake_case			:      List[Any]        =			self.image_processor_tester.get_expected_values(__a							,batched=__a							)
          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							)      ->				Dict:
          # Initialize image processor
          snake_case			:      Any        =			self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          snake_case			:      Union[str, Any]        =			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
          snake_case			:      List[Any]        =			image_processing(image_inputs[0]							,return_tensors="""pt"""							).pixel_values

          snake_case ,		snake_case			:      int        =			self.image_processor_tester.get_expected_values(__a							)
          self.assertEqual(
              encoded_images.shape							,(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,)

          # Test batched
          snake_case			:      str        =			image_processing(__a							,return_tensors="""pt"""							).pixel_values

          snake_case ,		snake_case			:      int        =			self.image_processor_tester.get_expected_values(__a							,batched=__a							)
          self.assertEqual(
              encoded_images.shape							,(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )							,)









'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      List[Any]        =			FileLock(str(tmpdir / """foo.lock"""				)				)
     snake_case			:      str        =			FileLock(str(tmpdir / """foo.lock"""				)				)
     snake_case			:      Any        =			0.01
     with locka.acquire():
          with pytest.raises(__lowercase				):
               snake_case			:      Union[str, Any]        =			time.time()
               locka.acquire(__lowercase				)
               assert time.time() - _start > timeout







def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      List[Any]        =			"""a""" * 1_000 + """.lock"""
     snake_case			:      List[str]        =			FileLock(str(tmpdir / filename				)				)
     assert locka._lock_file.endswith(""".lock"""				)
     assert not locka._lock_file.endswith(__lowercase				)
     assert len(os.path.basename(locka._lock_file				)				) <= 255
     snake_case			:      Any        =			FileLock(tmpdir / filename				)
     with locka.acquire():
          with pytest.raises(__lowercase				):
               locka.acquire(0				)



