"""simple docstring"""







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 ViltImageProcessor






class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=18 ,				_SCREAMING_SNAKE_CASE=30 ,				_SCREAMING_SNAKE_CASE=400 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,				_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					size if size is not None else {'shortest_edge': 30}
      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					num_channels
      __lowerCamelCase    		=					image_size
      __lowerCamelCase    		=					min_resolution
      __lowerCamelCase    		=					max_resolution
      __lowerCamelCase    		=					do_resize
      __lowerCamelCase    		=					size
      __lowerCamelCase    		=					size_divisor
      __lowerCamelCase    		=					do_normalize
      __lowerCamelCase    		=					image_mean
      __lowerCamelCase    		=					image_std







   def    _A				(	self	):


      '''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,
          "size_divisor": self.size_divisor,
      }







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=False	):


      '''simple docstring'''

      if not batched:
         __lowerCamelCase    		=					self.size['shortest_edge']
         __lowerCamelCase    		=					image_inputs[0]
         if isinstance(_SCREAMING_SNAKE_CASE ,				Image.Image	):
            __lowerCamelCase		,__lowerCamelCase    		=					image.size
         else:
            __lowerCamelCase		,__lowerCamelCase    		=					image.shape[1], image.shape[2]
         __lowerCamelCase    		=					size / min(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
         if h < w:
            __lowerCamelCase		,__lowerCamelCase    		=					size, scale * w
         else:
            __lowerCamelCase		,__lowerCamelCase    		=					scale * h, size

         __lowerCamelCase    		=					int((1333 / 800) * size	)
         if max(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	) > max_size:
            __lowerCamelCase    		=					max_size / max(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
            __lowerCamelCase    		=					newh * scale
            __lowerCamelCase    		=					neww * scale

         __lowerCamelCase		,__lowerCamelCase    		=					int(newh + 0.5	), int(neww + 0.5	)
         __lowerCamelCase		,__lowerCamelCase    		=					(
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

      else:
         __lowerCamelCase    		=					[]
         for image in image_inputs:
            __lowerCamelCase		,__lowerCamelCase    		=					self.get_expected_values([image]	)
            expected_values.append((expected_height, expected_width)	)
         __lowerCamelCase    		=					max(_SCREAMING_SNAKE_CASE ,				key=lambda _SCREAMING_SNAKE_CASE	: item[0]	)[0]
         __lowerCamelCase    		=					max(_SCREAMING_SNAKE_CASE ,				key=lambda _SCREAMING_SNAKE_CASE	: item[1]	)[1]

      return expected_height, expected_width






@require_torch
@require_vision
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    ViltImageProcessor if is_vision_available() else None







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ViltImageProcessingTester(self	)







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()







   def    _A				(	self	):


      '''simple docstring'''

      __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'	)	)
      self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'size_divisor'	)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size ,				{'shortest_edge': 30}	)

      __lowerCamelCase    		=					self.image_processing_class.from_dict(self.image_processor_dict ,				size=42	)
      self.assertEqual(image_processor.size ,				{'shortest_edge': 42}	)







   def    _A				(	self	):


      '''simple docstring'''

      pass







   def    _A				(	self	):


      '''simple docstring'''

      __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

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,				)

      # Test batched
      __lowerCamelCase    		=					image_processing(_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).pixel_values

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,				batched=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,				)







   def    _A				(	self	):


      '''simple docstring'''

      __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

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,				)

      # Test batched
      __lowerCamelCase    		=					image_processing(_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).pixel_values

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,				batched=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,				)







   def    _A				(	self	):


      '''simple docstring'''

      __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

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,				)

      # Test batched
      __lowerCamelCase    		=					image_processing(_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).pixel_values

      __lowerCamelCase		,__lowerCamelCase    		=					self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE ,				batched=_SCREAMING_SNAKE_CASE	)
      self.assertEqual(
          encoded_images.shape ,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,				)










'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_lowerCAmelCase	:		Dict		  =						{}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase	:		Union[str, Any]		  =						['BarthezTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase	:		Union[str, Any]		  =						['BarthezTokenizerFast']


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

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_barthez_fast import BarthezTokenizerFast

else:
				import sys

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