



"""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    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

    def __init__(				self			:				str ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Dict[str, int] = None ,  lowerCAmelCase_			:				int = 3_2 ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Union[int, float] = 1 / 2_5_5 ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073] ,  lowerCAmelCase_			:				Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711] ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Optional[Any]=7 ,  lowerCAmelCase_			:				List[str]=3_0 ,  lowerCAmelCase_			:				Optional[int]=4_0_0 ,  lowerCAmelCase_			:				List[str]=3 ,  ):







      """simple docstring"""







      lowercase_								= parent
      lowercase_								= do_resize
      lowercase_								= size if size is not None else {"""shortest_edge""": 2_8_8}
      lowercase_								= size_divisor
      lowercase_								= do_rescale
      lowercase_								= rescale_factor
      lowercase_								= do_normalize
      lowercase_								= do_center_crop
      lowercase_								= image_mean
      lowercase_								= image_std
      lowercase_								= do_pad
      lowercase_								= batch_size
      lowercase_								= num_channels
      lowercase_								= min_resolution
      lowercase_								= max_resolution

    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      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 ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Tuple=False):







      """simple docstring"""







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

        lowercase_								= int((1_3_3_3 / 8_0_0) * size)
        if max(lowerCAmelCase_ ,  lowerCAmelCase_) > max_size:
          lowercase_								= max_size / max(lowerCAmelCase_ ,  lowerCAmelCase_)
          lowercase_								= newh * scale
          lowercase_								= neww * scale

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

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

      return expected_height, expected_width







@require_torch
@require_vision
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  unittest.TestCase   ):
    lowercase__      =   BridgeTowerImageProcessor if is_vision_available() else None

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= BridgeTowerImageProcessingTester(self)

    @property
    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      return self.image_processor_tester.prepare_image_processor_dict()

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(lowerCAmelCase_ ,  """image_mean"""))
      self.assertTrue(hasattr(lowerCAmelCase_ ,  """image_std"""))
      self.assertTrue(hasattr(lowerCAmelCase_ ,  """do_normalize"""))
      self.assertTrue(hasattr(lowerCAmelCase_ ,  """do_resize"""))
      self.assertTrue(hasattr(lowerCAmelCase_ ,  """size"""))
      self.assertTrue(hasattr(lowerCAmelCase_ ,  """size_divisor"""))

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      pass

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      lowercase_								= self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      lowercase_								= prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase_)
      for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_ ,  Image.Image)

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

      lowercase_					,    lowercase_								= self.image_processor_tester.get_expected_values(lowerCAmelCase_)
      self.assertEqual(
          encoded_images.shape ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,  )

      # Test batched
      lowercase_								= image_processing(lowerCAmelCase_ ,  return_tensors="""pt""").pixel_values

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

    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      lowercase_								= self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      lowercase_								= prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase_ ,  numpify=lowerCAmelCase_)
      for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_ ,  np.ndarray)

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

      lowercase_					,    lowercase_								= self.image_processor_tester.get_expected_values(lowerCAmelCase_)
      self.assertEqual(
          encoded_images.shape ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,  )

      # Test batched
      lowercase_								= image_processing(lowerCAmelCase_ ,  return_tensors="""pt""").pixel_values

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




    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      lowercase_								= self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      lowercase_								= prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase_ ,  torchify=lowerCAmelCase_)
      for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_ ,  torch.Tensor)

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

      lowercase_					,    lowercase_								= self.image_processor_tester.get_expected_values(lowerCAmelCase_)
      self.assertEqual(
          encoded_images.shape ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,  )

      # Test batched
      lowercase_								= image_processing(lowerCAmelCase_ ,  return_tensors="""pt""").pixel_values

      lowercase_					,    lowercase_								= self.image_processor_tester.get_expected_values(lowerCAmelCase_ ,  batched=lowerCAmelCase_)
      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


UpperCAmelCase						:		List[Any]			= {"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
       from .tokenization_bertweet import BertweetTokenizer

else:
       import sys

       UpperCAmelCase						:		Tuple			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

