
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						:    List[Any]			,    _snake_case						:    Any			,    _snake_case						:    Optional[Any]=7			,    _snake_case						:    Optional[int]=3			,    _snake_case						:    List[str]=18			,    _snake_case						:    List[str]=30			,    _snake_case						:    List[str]=400			,    _snake_case						:    Optional[int]=True			,    _snake_case						:    Optional[Any]=None			,    _snake_case						:    int=2			,    _snake_case						:    int=True			,    _snake_case						:    Union[str, Any]=[0.5, 0.5, 0.5]			,    _snake_case						:    Optional[Any]=[0.5, 0.5, 0.5]			,    ):
          __lowercase						:     List[str]		      =    size if size is not None else {'''shortest_edge''': 30}
          __lowercase						:     Optional[Any]		      =    parent
          __lowercase						:     Optional[int]		      =    batch_size
          __lowercase						:     int		      =    num_channels
          __lowercase						:     Tuple		      =    image_size
          __lowercase						:     Any		      =    min_resolution
          __lowercase						:     Union[str, Any]		      =    max_resolution
          __lowercase						:     str		      =    do_resize
          __lowercase						:     Tuple		      =    size
          __lowercase						:     Tuple		      =    size_divisor
          __lowercase						:     Optional[Any]		      =    do_normalize
          __lowercase						:     Any		      =    image_mean
          __lowercase						:     Dict		      =    image_std




      def 						snake_case_     ( self						:    List[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						:    int			,    _snake_case						:    List[str]			,    _snake_case						:    List[Any]=False			):
          if not batched:
              __lowercase						:     int		      =    self.size['''shortest_edge''']
              __lowercase						:     Dict		      =    image_inputs[0]
              if isinstance(_snake_case			,    Image.Image			):
                  __lowercase						:     Tuple		      =    image.size
              else:
                  __lowercase						:     Tuple		      =    image.shape[1], image.shape[2]
              __lowercase						:     Optional[Any]		      =    size / min(_snake_case			,    _snake_case			)
              if h < w:
                  __lowercase						:     Any		      =    size, scale * w
              else:
                  __lowercase						:     List[str]		      =    scale * h, size

              __lowercase						:     str		      =    int((1333 / 800) * size			)
              if max(_snake_case			,    _snake_case			) > max_size:
                  __lowercase						:     List[Any]		      =    max_size / max(_snake_case			,    _snake_case			)
                  __lowercase						:     Tuple		      =    newh * scale
                  __lowercase						:     str		      =    neww * scale

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

          else:
              __lowercase						:     str		      =    []
              for image in image_inputs:
                  __lowercase						:     List[str]		      =    self.get_expected_values([image]			)
                  expected_values.append((expected_height, expected_width)			)
              __lowercase						:     Union[str, Any]		      =    max(_snake_case			,    key=lambda _snake_case			: item[0]			)[0]
              __lowercase						:     Optional[int]		      =    max(_snake_case			,    key=lambda _snake_case			: item[1]			)[1]

          return expected_height, expected_width






@require_torch
@require_vision
class   __lowerCAmelCase		(		lowerCAmelCase_	,	unittest.TestCase ):



      """simple docstring"""







      A__		:     List[Any]       				=      ViltImageProcessor if is_vision_available() else None




      def 						snake_case_     ( self						:    int			):
          __lowercase						:     int		      =    ViltImageProcessingTester(self			)




      @property
      def 						snake_case_     ( self						:    Dict			):
          return self.image_processor_tester.prepare_image_processor_dict()




      def 						snake_case_     ( self						:    int			):
          __lowercase						:     Dict		      =    self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(_snake_case			,    '''image_mean'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''image_std'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''do_normalize'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''do_resize'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''size'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''size_divisor'''			)			)




      def 						snake_case_     ( self						:    str			):
          __lowercase						:     Optional[Any]		      =    self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size			,    {'''shortest_edge''': 30}			)

          __lowercase						:     Dict		      =    self.image_processing_class.from_dict(self.image_processor_dict			,    size=42			)
          self.assertEqual(image_processor.size			,    {'''shortest_edge''': 42}			)




      def 						snake_case_     ( self						:    List[str]			):
          pass




      def 						snake_case_     ( self						:    int			):
          # Initialize image_processing
          __lowercase						:     Optional[Any]		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          __lowercase						:     Any		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    Image.Image			)

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

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

          # Test batched
          __lowercase						:     List[Any]		      =    image_processing(_snake_case			,    return_tensors='''pt'''			).pixel_values

          __lowercase						:     List[Any]		      =    self.image_processor_tester.get_expected_values(_snake_case			,    batched=_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						:    str			):
          # Initialize image_processing
          __lowercase						:     Optional[int]		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          __lowercase						:     Optional[int]		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			,    numpify=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    np.ndarray			)

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

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

          # Test batched
          __lowercase						:     Tuple		      =    image_processing(_snake_case			,    return_tensors='''pt'''			).pixel_values

          __lowercase						:     int		      =    self.image_processor_tester.get_expected_values(_snake_case			,    batched=_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						:    Optional[int]			):
          # Initialize image_processing
          __lowercase						:     str		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          __lowercase						:     List[str]		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			,    torchify=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    torch.Tensor			)

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

          __lowercase						:     List[str]		      =    self.image_processor_tester.get_expected_values(_snake_case			)
          self.assertEqual(
              encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

          # Test batched
          __lowercase						:     Optional[Any]		      =    image_processing(_snake_case			,    return_tensors='''pt'''			).pixel_values

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




import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


__lowerCAmelCase   :     Optional[Any]      =						importlib.metadata.version("numpy")
__lowerCAmelCase   :     Any      =						".".join([str(x) for x in sys.version_info[:3]])


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      def 						snake_case_     ( self						:    List[Any]			):
          # lt + different version strings
          require_version_core('''numpy<1000.4.5'''			)
          require_version_core('''numpy<1000.4'''			)
          require_version_core('''numpy<1000'''			)

          # le
          require_version_core('''numpy<=1000.4.5'''			)
          require_version_core(F'numpy<={numpy_ver}'			)

          # eq
          require_version_core(F'numpy=={numpy_ver}'			)

          # ne
          require_version_core('''numpy!=1000.4.5'''			)

          # ge
          require_version_core('''numpy>=1.0'''			)
          require_version_core('''numpy>=1.0.0'''			)
          require_version_core(F'numpy>={numpy_ver}'			)

          # gt
          require_version_core('''numpy>1.0.0'''			)

          # mix
          require_version_core('''numpy>1.0.0,<1000'''			)

          # requirement w/o version
          require_version_core('''numpy'''			)

          # unmet requirements due to version conflict
          for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'numpy<{numpy_ver}']:
              try:
                  require_version_core(_snake_case			)
              except ImportError as e:
                  self.assertIn(F'{req} is required'			,    str(_snake_case			)			)
                  self.assertIn('''but found'''			,    str(_snake_case			)			)

        # unmet requirements due to missing module
          for req in ["numpipypie>1", "numpipypie2"]:
              try:
                  require_version_core(_snake_case			)
              except importlib.metadata.PackageNotFoundError as e:
                  self.assertIn(F'The \'{req}\' distribution was not found and is required by this application'			,    str(_snake_case			)			)
                  self.assertIn('''Try: pip install transformers -U'''			,    str(_snake_case			)			)

        # bogus requirements formats:
        # 1. whole thing
          for req in ["numpy??1.0.0", "numpy1.0.0"]:
              try:
                  require_version_core(_snake_case			)
              except ValueError as e:
                  self.assertIn('''requirement needs to be in the pip package format'''			,    str(_snake_case			)			)
        # 2. only operators
          for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
              try:
                  require_version_core(_snake_case			)
              except ValueError as e:
                  self.assertIn('''need one of '''			,    str(_snake_case			)			)




      def 						snake_case_     ( self						:    List[str]			):
          # matching requirement
          require_version('''python>=3.6.0'''			)

          # not matching requirements
          for req in ["python>9.9.9", "python<3.0.0"]:
              try:
                  require_version_core(_snake_case			)
              except ImportError as e:
                  self.assertIn(F'{req} is required'			,    str(_snake_case			)			)
                  self.assertIn(F'but found python=={python_ver}'			,    str(_snake_case			)			)


