
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							):

					def __init__(						self       :  str ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  Tuple=7 ,       __UpperCamelCase       :  Tuple=3 ,       __UpperCamelCase       :  List[str]=1_8 ,       __UpperCamelCase       :  int=3_0 ,       __UpperCamelCase       :  int=4_0_0 ,       __UpperCamelCase       :  List[str]=True ,       __UpperCamelCase       :  Tuple=None ,       __UpperCamelCase       :  Tuple=True ,       __UpperCamelCase       :  Optional[int]=[0.5, 0.5, 0.5] ,       __UpperCamelCase       :  Optional[int]=[0.5, 0.5, 0.5] ,       ):
												lowerCamelCase_					=       size if size is not None else {"""height""": 1_8, """width""": 1_8}
												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  lowercase__   (						self       :  Optional[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    __A(					UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     DPTImageProcessor if is_vision_available() else None

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       DPTImageProcessingTester(self )

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.image_processor_tester.prepare_image_processor_dict()

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       self.image_processing_class(**self.image_processor_dict )
												self.assertTrue(hasattr(_lowercase ,       """image_mean""" ) )
												self.assertTrue(hasattr(_lowercase ,       """image_std""" ) )
												self.assertTrue(hasattr(_lowercase ,       """do_normalize""" ) )
												self.assertTrue(hasattr(_lowercase ,       """do_resize""" ) )
												self.assertTrue(hasattr(_lowercase ,       """size""" ) )

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       self.image_processing_class.from_dict(self.image_processor_dict )
												self.assertEqual(image_processor.size ,       {"""height""": 1_8, """width""": 1_8} )

												lowerCamelCase_					=       self.image_processing_class.from_dict(self.image_processor_dict ,       size=4_2 )
												self.assertEqual(image_processor.size ,       {"""height""": 4_2, """width""": 4_2} )

					def  lowercase__   (						self       :  Tuple ):
												# 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=_lowercase )
												for image in image_inputs:
																			self.assertIsInstance(_lowercase ,       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(_lowercase ,       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  lowercase__   (						self       :  int ):
												# 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=_lowercase ,       numpify=_lowercase )
												for image in image_inputs:
																			self.assertIsInstance(_lowercase ,       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(_lowercase ,       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  lowercase__   (						self       :  Union[str, 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=_lowercase ,       torchify=_lowercase )
												for image in image_inputs:
																			self.assertIsInstance(_lowercase ,       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(_lowercase ,       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"""],
												    ) ,       )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE		:List[Any]						=     {"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
 from .tokenization_herbert import HerbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_herbert_fast import HerbertTokenizerFast

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
