'''simple docstring'''

import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def __init__(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=7      ,       SCREAMING_SNAKE_CASE__   :	Any=3      ,       SCREAMING_SNAKE_CASE__   :	Tuple=18      ,       SCREAMING_SNAKE_CASE__   :	List[str]=30      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=4_00      ,       SCREAMING_SNAKE_CASE__   :	List[str]=True      ,       SCREAMING_SNAKE_CASE__   :	int=None      ,       SCREAMING_SNAKE_CASE__   :	Any=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=[0.5, 0.5, 0.5]      ,       SCREAMING_SNAKE_CASE__   :	Dict=[0.5, 0.5, 0.5]      ,       )  ->    Tuple:
  __lowerCAmelCase											=						size if size is not None else {"""height""": 18, """width""": 18}
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						num_channels
  __lowerCAmelCase											=						image_size
  __lowerCAmelCase											=						min_resolution
  __lowerCAmelCase											=						max_resolution
  __lowerCAmelCase											=						do_resize
  __lowerCAmelCase											=						size
  __lowerCAmelCase											=						do_normalize
  __lowerCAmelCase											=						image_mean
  __lowerCAmelCase											=						image_std





 def a					(      self   :	str							)  ->    Union[str, 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,
  }







@require_torch
@require_vision
class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					DPTImageProcessor if is_vision_available() else None
 def a					(      self   :	List[Any]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						DPTImageProcessingTester(self							)
 @property
 def a					(      self   :	Tuple							)  ->    str:
  return self.image_processor_tester.prepare_image_processor_dict()
 def a					(      self   :	str							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """image_mean"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """image_std"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """do_normalize"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """do_resize"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """size"""							)							)
 def a					(      self   :	Dict							)  ->    str:
  __lowerCAmelCase											=						self.image_processing_class.from_dict(self.image_processor_dict							)
  self.assertEqual(image_processor.size      ,       {"""height""": 18, """width""": 18}							)

  __lowerCAmelCase											=						self.image_processing_class.from_dict(self.image_processor_dict      ,       size=42							)
  self.assertEqual(image_processor.size      ,       {"""height""": 42, """width""": 42}							)
 def a					(      self   :	Any							)  ->    List[Any]:
  # 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=SCREAMING_SNAKE_CASE__							)
  for image in image_inputs:
   self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       Image.Image							)

  # Test not batched input
  __lowerCAmelCase											=						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.size["""height"""],
          self.image_processor_tester.size["""width"""],
      )      ,       )

  # Test batched
  __lowerCAmelCase											=						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.size["""height"""],
          self.image_processor_tester.size["""width"""],
      )      ,       )
 def a					(      self   :	Optional[int]							)  ->    str:
  # 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=SCREAMING_SNAKE_CASE__      ,       numpify=SCREAMING_SNAKE_CASE__							)
  for image in image_inputs:
   self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       np.ndarray							)

  # Test not batched input
  __lowerCAmelCase											=						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.size["""height"""],
          self.image_processor_tester.size["""width"""],
      )      ,       )

  # Test batched
  __lowerCAmelCase											=						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.size["""height"""],
          self.image_processor_tester.size["""width"""],
      )      ,       )





 def a					(      self   :	List[Any]							)  ->    List[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=SCREAMING_SNAKE_CASE__      ,       torchify=SCREAMING_SNAKE_CASE__							)
  for image in image_inputs:
   self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,       torch.Tensor							)

  # Test not batched input
  __lowerCAmelCase											=						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.size["""height"""],
          self.image_processor_tester.size["""width"""],
      )      ,       )

  # Test batched
  __lowerCAmelCase											=						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.size["""height"""],
          self.image_processor_tester.size["""width"""],
      )      ,       )







'''simple docstring'''

import re

from filelock import FileLock


try:
 import nltk

 _A			:      int					      =				True
except (ImportError, ModuleNotFoundError):
 _A			:      Optional[Any]					      =				False

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







def      UpperCamelCase_						(					snake_case_       :       str      )					->     str:






 '''simple docstring'''

 re.sub("""<n>"""						,		""""""						,		snake_case_      )  # 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(snake_case_      )      )






