
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 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=7						,    __UpperCamelCase=3						,    __UpperCamelCase=18						,    __UpperCamelCase=30						,    __UpperCamelCase=400						,    __UpperCamelCase=True						,    __UpperCamelCase=None						,    __UpperCamelCase=2						,    __UpperCamelCase=True						,    __UpperCamelCase=[0.5, 0.5, 0.5]						,    __UpperCamelCase=[0.5, 0.5, 0.5]						,    ):
      A_								=					size if size is not None else {"shortest_edge": 30}
      A_								=					parent
      A_								=					batch_size
      A_								=					num_channels
      A_								=					image_size
      A_								=					min_resolution
      A_								=					max_resolution
      A_								=					do_resize
      A_								=					size
      A_								=					size_divisor
      A_								=					do_normalize
      A_								=					image_mean
      A_								=					image_std

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

          A_								=					int((1333 / 800) * size					)
          if max(__UpperCamelCase						,    __UpperCamelCase					) > max_size:
              A_								=					max_size / max(__UpperCamelCase						,    __UpperCamelCase					)
              A_								=					newh * scale
              A_								=					neww * scale

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

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

      return expected_height, expected_width



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



  """simple docstring"""





  lowerCAmelCase_								=		ViltImageProcessor if is_vision_available() else None

  def 					lowercase_						(					self					):
      A_								=					ViltImageProcessingTester(self					)

  @property
  def 					lowercase_						(					self					):
      return self.image_processor_tester.prepare_image_processor_dict()

  def 					lowercase_						(					self					):
      A_								=					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 					lowercase_						(					self					):
      A_								=					self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size						,    {"shortest_edge": 30}					)

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

  def 					lowercase_						(					self					):
      pass

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

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

      A_	,	A_								=					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
      A_								=					image_processing(__UpperCamelCase						,    return_tensors="pt"					).pixel_values

      A_	,	A_								=					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 					lowercase_						(					self					):
      # Initialize image_processing
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      A_								=					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
      A_								=					image_processing(image_inputs[0]						,    return_tensors="pt"					).pixel_values

      A_	,	A_								=					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
      A_								=					image_processing(__UpperCamelCase						,    return_tensors="pt"					).pixel_values

      A_	,	A_								=					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 					lowercase_						(					self					):
      # Initialize image_processing
      A_								=					self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      A_								=					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
      A_								=					image_processing(image_inputs[0]						,    return_tensors="pt"					).pixel_values

      A_	,	A_								=					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
      A_								=					image_processing(__UpperCamelCase						,    return_tensors="pt"					).pixel_values

      A_	,	A_								=					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,
          )						,    )








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image


class 							lowerCamelCase       (		unittest.TestCase			,  __snake_case					):



  """simple docstring"""





  def 					lowercase_						(					self					):
      A_								=					load_tool("image-question-answering"					)
      self.tool.setup()
      A_								=					load_tool("image-question-answering"						,    remote=__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.tool(__UpperCamelCase						,    "How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.remote_tool(__UpperCamelCase						,    "How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.tool(image=__UpperCamelCase						,    question="How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.remote_tool(image=__UpperCamelCase						,    question="How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)






