





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 			UpperCAmelCase					(      unittest.TestCase       ):




      def __init__(      self      :			Dict      ,      __snake_case      :			Any      ,      __snake_case      :			Union[str, Any]=7      ,      __snake_case      :			Optional[int]=3      ,      __snake_case      :			List[Any]=18      ,      __snake_case      :			Tuple=30      ,      __snake_case      :			Tuple=4_00      ,      __snake_case      :			int=True      ,      __snake_case      :			str=None      ,      __snake_case      :			Optional[Any]=True      ,      __snake_case      :			Tuple=[0.5, 0.5, 0.5]      ,      __snake_case      :			Optional[Any]=[0.5, 0.5, 0.5]      ,      )    ->				Union[str, Any]:
            _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     lowercase__   (      self      :			str  )    ->				List[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 			UpperCAmelCase					(      snake_case_							,		unittest.TestCase       ):
      _lowercase:      List[Any]											=       DPTImageProcessor if is_vision_available() else None




      def     lowercase__   (      self      :			List[str]  )    ->				str:
            _lowerCAmelCase												=							DPTImageProcessingTester(self  )




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




      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(__snake_case      ,      """image_mean"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """image_std"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """do_normalize"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """do_resize"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """size"""  )  )




      def     lowercase__   (      self      :			Tuple  )    ->				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     lowercase__   (      self      :			int  )    ->				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=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__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(__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     lowercase__   (      self      :			Optional[int]  )    ->				List[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=__snake_case      ,      numpify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__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(__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     lowercase__   (      self      :			Tuple  )    ->				Dict:
            # 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=__snake_case      ,      torchify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__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(__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'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


A__    :       int              ={'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       Dict              =['''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

      A__    :       List[Any]              =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






