code: 
  
  
  """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 MobileNetVaImageProcessor
  
  
  
  
  class  __snake_case      (unittest.TestCase     ):
  
  
  
  
  							def __init__(    self:						Optional[Any]    ,       A_:						List[str]    ,       A_:						Tuple=7    ,       A_:						Union[str, Any]=3    ,       A_:						List[Any]=18    ,       A_:						Optional[Any]=30    ,       A_:						Tuple=4_00    ,       A_:						Union[str, Any]=True    ,       A_:						List[str]=None    ,       A_:						Optional[int]=True    ,       A_:						int=None    ,       ):
  										__lowerCamelCase        =			size if size is not None else {'shortest_edge': 20}
  										__lowerCamelCase        =			crop_size if crop_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_center_crop
  										__lowerCamelCase        =			crop_size
  
  
  
  
  							def      __a      (    self:						List[Any]      ):
  										return {
  										    "do_resize": self.do_resize,
  										    "size": self.size,
  										    "do_center_crop": self.do_center_crop,
  										    "crop_size": self.crop_size,
  										}
  
  
  
  
  @require_torch
  @require_vision
  class  __snake_case      (a_ ,		unittest.TestCase     ):
  							__a				   =					MobileNetVaImageProcessor if is_vision_available() else None
  
  
  
  
  							def      __a      (    self:						Tuple      ):
  										__lowerCamelCase        =			MobileNetVaImageProcessingTester(self      )
  
  
  
  
  							@property
  							def      __a      (    self:						List[str]      ):
  										return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__lowerCamelCase        =			self.image_processing_class(**self.image_processor_dict      )
  										self.assertTrue(hasattr(A_    ,       """do_resize"""      )      )
  										self.assertTrue(hasattr(A_    ,       """size"""      )      )
  										self.assertTrue(hasattr(A_    ,       """do_center_crop"""      )      )
  										self.assertTrue(hasattr(A_    ,       """center_crop"""      )      )
  
  
  
  
  							def      __a      (    self:						Any      ):
  										__lowerCamelCase        =			self.image_processing_class.from_dict(self.image_processor_dict      )
  										self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 20}      )
  										self.assertEqual(image_processor.crop_size    ,       {"""height""": 18, """width""": 18}      )
  
  										__lowerCamelCase        =			self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42    ,       crop_size=84      )
  										self.assertEqual(image_processor.size    ,       {"""shortest_edge""": 42}      )
  										self.assertEqual(image_processor.crop_size    ,       {"""height""": 84, """width""": 84}      )
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										pass
  
  
  
  
  							def      __a      (    self:						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=A_      )
  										for image in image_inputs:
  													self.assertIsInstance(A_    ,       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.crop_size["""height"""],
  										        self.image_processor_tester.crop_size["""width"""],
  										    )    ,       )
  
  										# Test batched
  										__lowerCamelCase        =			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.crop_size["""height"""],
  										        self.image_processor_tester.crop_size["""width"""],
  										    )    ,       )
  
  
  
  
  							def      __a      (    self:						Optional[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=A_    ,       numpify=A_      )
  										for image in image_inputs:
  													self.assertIsInstance(A_    ,       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.crop_size["""height"""],
  										        self.image_processor_tester.crop_size["""width"""],
  										    )    ,       )
  
  										# Test batched
  										__lowerCamelCase        =			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.crop_size["""height"""],
  										        self.image_processor_tester.crop_size["""width"""],
  										    )    ,       )
  
  
  
  
  							def      __a      (    self:						int      ):
  										# 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=A_    ,       torchify=A_      )
  										for image in image_inputs:
  													self.assertIsInstance(A_    ,       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.crop_size["""height"""],
  										        self.image_processor_tester.crop_size["""width"""],
  										    )    ,       )
  
  										# Test batched
  										__lowerCamelCase        =			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.crop_size["""height"""],
  										        self.image_processor_tester.crop_size["""width"""],
  										    )    ,       )
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  __magic_name__	:   Optional[Any]          =							{
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   List[Any]          =							[
  					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'FlaubertForMultipleChoice',
  					    'FlaubertForQuestionAnswering',
  					    'FlaubertForQuestionAnsweringSimple',
  					    'FlaubertForSequenceClassification',
  					    'FlaubertForTokenClassification',
  					    'FlaubertModel',
  					    'FlaubertWithLMHeadModel',
  					    'FlaubertPreTrainedModel',
  					]
  
  try:
  					if not is_tf_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   Tuple          =							[
  					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'TFFlaubertForMultipleChoice',
  					    'TFFlaubertForQuestionAnsweringSimple',
  					    'TFFlaubertForSequenceClassification',
  					    'TFFlaubertForTokenClassification',
  					    'TFFlaubertModel',
  					    'TFFlaubertPreTrainedModel',
  					    'TFFlaubertWithLMHeadModel',
  					]
  
  
  if TYPE_CHECKING:
  					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  					from .tokenization_flaubert import FlaubertTokenizer
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_flaubert import (
  										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    FlaubertForMultipleChoice,
  										    FlaubertForQuestionAnswering,
  										    FlaubertForQuestionAnsweringSimple,
  										    FlaubertForSequenceClassification,
  										    FlaubertForTokenClassification,
  										    FlaubertModel,
  										    FlaubertPreTrainedModel,
  										    FlaubertWithLMHeadModel,
  										)
  
  					try:
  										if not is_tf_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_tf_flaubert import (
  										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    TFFlaubertForMultipleChoice,
  										    TFFlaubertForQuestionAnsweringSimple,
  										    TFFlaubertForSequenceClassification,
  										    TFFlaubertForTokenClassification,
  										    TFFlaubertModel,
  										    TFFlaubertPreTrainedModel,
  										    TFFlaubertWithLMHeadModel,
  										)
  
  else:
  					import sys
  
  					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  