





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 GLPNImageProcessor



class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  def __init__(				self :		List[str]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		List[Any]=7					,  lowerCAmelCase_ :		Any=3					,  lowerCAmelCase_ :		List[Any]=1_8					,  lowerCAmelCase_ :		str=3_0					,  lowerCAmelCase_ :		Tuple=4_0_0					,  lowerCAmelCase_ :		Optional[int]=True					,  lowerCAmelCase_ :		Tuple=3_2					,  lowerCAmelCase_ :		List[Any]=True					,  )							->						Dict:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	image_size
     __lowerCAmelCase								=	min_resolution
     __lowerCAmelCase								=	max_resolution
     __lowerCAmelCase								=	do_resize
     __lowerCAmelCase								=	size_divisor
     __lowerCAmelCase								=	do_rescale






  def 				lowercase		(				self :		Optional[int]     )							->						List[str]:
     return {
         "do_resize": self.do_resize,
         "size_divisor": self.size_divisor,
         "do_rescale": self.do_rescale,
     }





@require_torch
@require_vision
class 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = GLPNImageProcessor if is_vision_available() else None






  def 				lowercase		(				self :		Optional[Any]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	GLPNImageProcessingTester(self     )






  @property
  def 				lowercase		(				self :		List[Any]     )							->						Union[str, Any]:
     return self.image_processor_tester.prepare_image_processor_dict()






  def 				lowercase		(				self :		List[Any]     )							->						List[Any]:
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'do_resize'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'size_divisor'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'resample'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'do_rescale'     )     )






  def 				lowercase		(				self :		int     )							->						str:
     pass






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     # Initialize image_processing
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # create random PIL images
     __lowerCAmelCase								=	prepare_image_inputs(self.image_processor_tester					,  equal_resolution=lowerCAmelCase_     )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_					,  Image.Image     )

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     __lowerCAmelCase								=	image_processing(image_inputs[0]					,  return_tensors='pt'     ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )






  def 				lowercase		(				self :		List[str]     )							->						Optional[Any]:
     # Initialize image_processing
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # create random numpy tensors
     __lowerCAmelCase								=	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 (GLPNImageProcessor doesn't support batching)
     __lowerCAmelCase								=	image_processing(image_inputs[0]					,  return_tensors='pt'     ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )






  def 				lowercase		(				self :		Tuple     )							->						Optional[Any]:
     # Initialize image_processing
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # create random PyTorch tensors
     __lowerCAmelCase								=	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 (GLPNImageProcessor doesn't support batching)
     __lowerCAmelCase								=	image_processing(image_inputs[0]					,  return_tensors='pt'     ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )








import re

from filelock import FileLock


try:
     import nltk

     _snake_case  :    Any						    =  True
except (ImportError, ModuleNotFoundError):
     _snake_case  :    Union[str, Any]						    =  False

if NLTK_AVAILABLE:
     with FileLock('.lock') as lock:
          nltk.download('punkt', quiet=True)

def   a_    ( lowerCAmelCase_      :			str	):
   re.sub('<n>',	'',	lowerCAmelCase_	)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(lowerCAmelCase_	)	)

