

"""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   __a				(unittest.TestCase):






     '''simple docstring'''

     def __init__(    self						,     _a						,     _a=7						,     _a=3						,     _a=18						,     _a=30						,     _a=400						,     _a=True						,     _a=None						,     _a=True						,     _a=[0.5, 0.5, 0.5]						,     _a=[0.5, 0.5, 0.5]						,     )	->   Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						size if size is not None else {"""height""": 18, """width""": 18}
           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						parent
           SCREAMING_SNAKE_CASE__ :   int   		=						batch_size
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						num_channels
           SCREAMING_SNAKE_CASE__ :   Any   		=						image_size
           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						min_resolution
           SCREAMING_SNAKE_CASE__ :   Dict   		=						max_resolution
           SCREAMING_SNAKE_CASE__ :   str   		=						do_resize
           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						size
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						do_normalize
           SCREAMING_SNAKE_CASE__ :   Dict   		=						image_mean
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						image_std





     def        _a      (    self	)	->   Tuple:


           """simple docstring"""


           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   __a				(UpperCamelCase_ ,						unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :str							=			DPTImageProcessor if is_vision_available() else None

     def        _a      (    self	)	->   List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						DPTImageProcessingTester(self	)

     @property
     def        _a      (    self	)	->   int:


           """simple docstring"""


           return self.image_processor_tester.prepare_image_processor_dict()

     def        _a      (    self	)	->   Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						self.image_processing_class(**self.image_processor_dict	)
           self.assertTrue(hasattr(A_						,     """image_mean"""	)	)
           self.assertTrue(hasattr(A_						,     """image_std"""	)	)
           self.assertTrue(hasattr(A_						,     """do_normalize"""	)	)
           self.assertTrue(hasattr(A_						,     """do_resize"""	)	)
           self.assertTrue(hasattr(A_						,     """size"""	)	)

     def        _a      (    self	)	->   str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size						,     {"""height""": 18, """width""": 18}	)

           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						self.image_processing_class.from_dict(self.image_processor_dict						,     size=42	)
           self.assertEqual(image_processor.size						,     {"""height""": 42, """width""": 42}	)

     def        _a      (    self	)	->   Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						self.image_processing_class(**self.image_processor_dict	)
           # create random PIL images
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						prepare_image_inputs(self.image_processor_tester						,     equal_resolution=A_	)
           for image in image_inputs:
                 self.assertIsInstance(A_						,     Image.Image	)

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

           # Test batched
           SCREAMING_SNAKE_CASE__ :   Any   		=						image_processing(A_						,     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	)	->   Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   List[str]   		=						self.image_processing_class(**self.image_processor_dict	)
           # create random numpy tensors
           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						prepare_image_inputs(self.image_processor_tester						,     equal_resolution=A_						,     numpify=A_	)
           for image in image_inputs:
                 self.assertIsInstance(A_						,     np.ndarray	)

           # Test not batched input
           SCREAMING_SNAKE_CASE__ :   Optional[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.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,     )

           # Test batched
           SCREAMING_SNAKE_CASE__ :   Dict   		=						image_processing(A_						,     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	)	->   Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Any   		=						self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE__ :   Tuple   		=						prepare_image_inputs(self.image_processor_tester						,     equal_resolution=A_						,     torchify=A_	)
           for image in image_inputs:
                 self.assertIsInstance(A_						,     torch.Tensor	)

           # Test not batched input
           SCREAMING_SNAKE_CASE__ :   Tuple   		=						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
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						image_processing(A_						,     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"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__magic_name__						: Tuple    =				{}


try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: str    =				['MLukeTokenizer']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mluke import MLukeTokenizer


else:
					import sys

					__magic_name__						: int    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




