code: 
  
  
  '''simple docstring'''
  
  
  
  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
  
  
  if is_torch_available():
        import torch
  
  if is_vision_available():
        from PIL import Image
  
        from transformers import CLIPImageProcessor
  
  
  
  
  
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=7    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=18    ,  SCREAMING_SNAKE_CASE_=30    ,  SCREAMING_SNAKE_CASE_=4_00    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,  SCREAMING_SNAKE_CASE_=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,  SCREAMING_SNAKE_CASE_=True    ,  )       ->  List[Any]:
           __lowerCamelCase     :			Union[str, Any]              =	size if size is not None else {'shortest_edge': 20}
           __lowerCamelCase     :			List[Any]              =	crop_size if crop_size is not None else {'height': 18, 'width': 18}
           __lowerCamelCase     :			Tuple              =	parent
           __lowerCamelCase     :			str              =	batch_size
           __lowerCamelCase     :			List[str]              =	num_channels
           __lowerCamelCase     :			Tuple              =	image_size
           __lowerCamelCase     :			Optional[int]              =	min_resolution
           __lowerCamelCase     :			Union[str, Any]              =	max_resolution
           __lowerCamelCase     :			Any              =	do_resize
           __lowerCamelCase     :			int              =	size
           __lowerCamelCase     :			Optional[Any]              =	do_center_crop
           __lowerCamelCase     :			Optional[int]              =	crop_size
           __lowerCamelCase     :			Dict              =	do_normalize
           __lowerCamelCase     :			Optional[int]              =	image_mean
           __lowerCamelCase     :			Union[str, Any]              =	image_std
           __lowerCamelCase     :			Union[str, Any]              =	do_convert_rgb
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Tuple:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_convert_rgb": self.do_convert_rgb,
           }
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_=False    ,  SCREAMING_SNAKE_CASE_=False    ,  SCREAMING_SNAKE_CASE_=False			)       ->  Optional[int]:
           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
           if equal_resolution:
                __lowerCamelCase     :			Optional[Any]              =	[]
                for i in range(self.batch_size			):
                     image_inputs.append(
                         np.random.randint(
                             2_55    ,  size=(self.num_channels, self.max_resolution, self.max_resolution)    ,  dtype=np.uinta			)			)
           else:
                __lowerCamelCase     :			Any              =	[]
                for i in range(self.batch_size			):
                     __lowerCamelCase       , __lowerCamelCase     :			List[str]              =	np.random.choice(np.arange(self.min_resolution    ,  self.max_resolution			)    ,  2			)
                     image_inputs.append(np.random.randint(2_55    ,  size=(self.num_channels, width, height)    ,  dtype=np.uinta			)			)
  
           if not numpify and not torchify:
                # PIL expects the channel dimension as last dimension
                __lowerCamelCase     :			Union[str, Any]              =	[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_    ,  0    ,  -1			)			) for x in image_inputs]
  
           if torchify:
                __lowerCamelCase     :			Union[str, Any]              =	[torch.from_numpy(SCREAMING_SNAKE_CASE_			) for x in image_inputs]
  
           return image_inputs
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      List[str]							  =    CLIPImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase     :			List[Any]              =	CLIPImageProcessingTester(self			)
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Tuple:
           __lowerCamelCase     :			Optional[int]              =	self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_resize'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'size'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_center_crop'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'center_crop'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_normalize'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'image_mean'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'image_std'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_convert_rgb'			)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Any:
           __lowerCamelCase     :			List[Any]              =	self.image_processing_class.from_dict(self.image_processor_dict			)
           self.assertEqual(image_processor.size    ,  {'shortest_edge': 20}			)
           self.assertEqual(image_processor.crop_size    ,  {'height': 18, 'width': 18}			)
  
           __lowerCamelCase     :			Optional[Any]              =	self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42    ,  crop_size=84			)
           self.assertEqual(image_processor.size    ,  {'shortest_edge': 42}			)
           self.assertEqual(image_processor.crop_size    ,  {'height': 84, 'width': 84}			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           # Initialize image_processing
           __lowerCamelCase     :			str              =	self.image_processing_class(**self.image_processor_dict			)
           # create random PIL images
           __lowerCamelCase     :			int              =	self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_			)
           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  Image.Image			)
  
           # Test not batched input
           __lowerCamelCase     :			int              =	image_processing(image_inputs[0]    ,  return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape    ,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
           # Test batched
           __lowerCamelCase     :			Optional[Any]              =	image_processing(SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape    ,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           # Initialize image_processing
           __lowerCamelCase     :			List[Any]              =	self.image_processing_class(**self.image_processor_dict			)
           # create random numpy tensors
           __lowerCamelCase     :			Any              =	self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_    ,  numpify=SCREAMING_SNAKE_CASE_			)
           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  np.ndarray			)
  
           # Test not batched input
           __lowerCamelCase     :			Any              =	image_processing(image_inputs[0]    ,  return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape    ,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
           # Test batched
           __lowerCamelCase     :			Tuple              =	image_processing(SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape    ,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[Any]:
           # Initialize image_processing
           __lowerCamelCase     :			Dict              =	self.image_processing_class(**self.image_processor_dict			)
           # create random PyTorch tensors
           __lowerCamelCase     :			List[str]              =	self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_    ,  torchify=SCREAMING_SNAKE_CASE_			)
           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			)
  
           # Test not batched input
           __lowerCamelCase     :			Optional[Any]              =	image_processing(image_inputs[0]    ,  return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape    ,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
           # Test batched
           __lowerCamelCase     :			List[Any]              =	image_processing(SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape    ,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[Any]							  =    CLIPImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           __lowerCamelCase     :			str              =	CLIPImageProcessingTester(self    ,  num_channels=4			)
           __lowerCamelCase     :			Dict              =	3
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  List[str]:
           return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Any:
           __lowerCamelCase     :			List[str]              =	self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_resize'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'size'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_center_crop'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'center_crop'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_normalize'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'image_mean'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'image_std'			)			)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,  'do_convert_rgb'			)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[Any]:
           # Initialize image_processing
           __lowerCamelCase     :			str              =	self.image_processing_class(**self.image_processor_dict			)
           # create random PIL images
           __lowerCamelCase     :			List[Any]              =	self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_			)
           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  Image.Image			)
  
           # Test not batched input
           __lowerCamelCase     :			Optional[Any]              =	image_processing(image_inputs[0]    ,  return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape    ,  (
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
           # Test batched
           __lowerCamelCase     :			Union[str, Any]              =	image_processing(SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			).pixel_values
           self.assertEqual(
               encoded_images.shape    ,  (
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )    ,  )
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  A__	:      Optional[Any]				=						{
      """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
  }
  
  try:
        if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      List[Any]				=						["""XLMProphetNetTokenizer"""]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      Optional[Any]				=						[
            """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """XLMProphetNetDecoder""",
            """XLMProphetNetEncoder""",
            """XLMProphetNetForCausalLM""",
            """XLMProphetNetForConditionalGeneration""",
            """XLMProphetNetModel""",
            """XLMProphetNetPreTrainedModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
        try:
              if not is_sentencepiece_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_xlm_prophetnet import (
                  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMProphetNetDecoder,
                  XLMProphetNetEncoder,
                  XLMProphetNetForCausalLM,
                  XLMProphetNetForConditionalGeneration,
                  XLMProphetNetModel,
                  XLMProphetNetPreTrainedModel,
              )
  
  else:
        import sys
  
        A__	:      List[str]				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  