




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 ViltImageProcessor




class       UpperCAmelCase_   ( unittest.TestCase							):




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						str							,  a							:						str=7							,  a							:						Union[str, Any]=3							,  a							:						str=18							,  a							:						List[Any]=30							,  a							:						Any=400							,  a							:						List[str]=True							,  a							:						int=None							,  a							:						int=2							,  a							:						Tuple=True							,  a							:						Any=[0.5, 0.5, 0.5]							,  a							:						Tuple=[0.5, 0.5, 0.5]							,  )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       size if size is not None else {"""shortest_edge""": 30}
										SCREAMING_SNAKE_CASE     =       parent
										SCREAMING_SNAKE_CASE     =       batch_size
										SCREAMING_SNAKE_CASE     =       num_channels
										SCREAMING_SNAKE_CASE     =       image_size
										SCREAMING_SNAKE_CASE     =       min_resolution
										SCREAMING_SNAKE_CASE     =       max_resolution
										SCREAMING_SNAKE_CASE     =       do_resize
										SCREAMING_SNAKE_CASE     =       size
										SCREAMING_SNAKE_CASE     =       size_divisor
										SCREAMING_SNAKE_CASE     =       do_normalize
										SCREAMING_SNAKE_CASE     =       image_mean
										SCREAMING_SNAKE_CASE     =       image_std





							def  _UpperCAmelCase  (       self							:						int      )      ->       Dict:
										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  _UpperCAmelCase  (       self							:						str							,  a							:						int							,  a							:						Optional[int]=False      )      ->       Tuple:
										if not batched:
													SCREAMING_SNAKE_CASE     =       self.size["""shortest_edge"""]
													SCREAMING_SNAKE_CASE     =       image_inputs[0]
													if isinstance(a							,  Image.Image      ):
																SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       image.size
													else:
																SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       image.shape[1], image.shape[2]
													SCREAMING_SNAKE_CASE     =       size / min(a							,  a      )
													if h < w:
																SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       size, scale * w
													else:
																SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       scale * h, size

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

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

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

										return expected_height, expected_width





@require_torch
@require_vision
class       UpperCAmelCase_   ( __snake_case		,   unittest.TestCase							):




							'''simple docstring'''



							a__    	=  ViltImageProcessor if is_vision_available() else None





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       str:
										SCREAMING_SNAKE_CASE     =       ViltImageProcessingTester(self      )





							@property
							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       int:
										return self.image_processor_tester.prepare_image_processor_dict()





							def  _UpperCAmelCase  (       self							:						str      )      ->       str:
										SCREAMING_SNAKE_CASE     =       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  _UpperCAmelCase  (       self							:						str      )      ->       Union[str, Any]:
										SCREAMING_SNAKE_CASE     =       self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size							,  {"""shortest_edge""": 30}      )

										SCREAMING_SNAKE_CASE     =       self.image_processing_class.from_dict(self.image_processor_dict							,  size=42      )
										self.assertEqual(image_processor.size							,  {"""shortest_edge""": 42}      )





							def  _UpperCAmelCase  (       self							:						int      )      ->       Union[str, Any]:
										pass





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										SCREAMING_SNAKE_CASE     =       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=a      )
										for image in image_inputs:
													self.assertIsInstance(a							,  Image.Image      )

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

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       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
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_tensors="""pt"""      ).pixel_values

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

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       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
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_tensors="""pt"""      ).pixel_values

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       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  _UpperCAmelCase  (       self							:						Tuple      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										SCREAMING_SNAKE_CASE     =       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
										SCREAMING_SNAKE_CASE     =       image_processing(image_inputs[0]							,  return_tensors="""pt"""      ).pixel_values

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       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
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_tensors="""pt"""      ).pixel_values

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       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"""






from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase				      =   {'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
							from .tokenization_phobert import PhobertTokenizer

else:
							import sys

							__lowerCAmelCase				      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)