code: 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      lowercase_						(						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=True	,    __SCREAMING_SNAKE_CASE=None	,    )		->Tuple:
  								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     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								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      lowercase_						(						UpperCamelCase_   ,  unittest.TestCase				):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      List[Any]     =       MobileNetVaImageProcessor if is_vision_available() else None
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
  								lowerCAmelCase				=	MobileNetVaImageProcessingTester(self       )
  
  
  	@property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
  								return self.image_processor_tester.prepare_image_processor_dict()
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
  								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
  								self.assertTrue(hasattr(__lowerCamelCase	,    '''do_resize'''       )       )
  								self.assertTrue(hasattr(__lowerCamelCase	,    '''size'''       )       )
  								self.assertTrue(hasattr(__lowerCamelCase	,    '''do_center_crop'''       )       )
  								self.assertTrue(hasattr(__lowerCamelCase	,    '''center_crop'''       )       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, 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     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, Any]:
  								pass
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[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=__lowerCamelCase       )
  								for image in image_inputs:
  															self.assertIsInstance(__lowerCamelCase	,    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(__lowerCamelCase	,    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     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								# 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=__lowerCamelCase	,    numpify=__lowerCamelCase       )
  								for image in image_inputs:
  															self.assertIsInstance(__lowerCamelCase	,    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(__lowerCamelCase	,    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     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
  								# 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=__lowerCamelCase	,    torchify=__lowerCamelCase       )
  								for image in image_inputs:
  															self.assertIsInstance(__lowerCamelCase	,    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(__lowerCamelCase	,    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: from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowercase__    :       Optional[Any]						=			logging.get_logger(__name__)
  
  lowercase__    :       Union[str, Any]						=			{
      '''microsoft/prophetnet-large-uncased''': (
          '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
      ),
  }
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Optional[int]     =       """prophetnet"""
  	UpperCAmelCase_						:      Optional[int]     =       ["""past_key_values"""]
  	UpperCAmelCase_						:      Any     =       {
  	    """num_attention_heads""": """num_encoder_attention_heads""",
  	}
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = "gelu"	,    __SCREAMING_SNAKE_CASE = 30522	,    __SCREAMING_SNAKE_CASE = 1024	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 512	,    __SCREAMING_SNAKE_CASE = 0.0_2	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 2	,    __SCREAMING_SNAKE_CASE = 32	,    __SCREAMING_SNAKE_CASE = 128	,    __SCREAMING_SNAKE_CASE = False	,    __SCREAMING_SNAKE_CASE = 0.0	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 1	,    __SCREAMING_SNAKE_CASE = 2	,    **__SCREAMING_SNAKE_CASE	,    )		->int:
  								lowerCAmelCase				=	vocab_size
  								lowerCAmelCase				=	hidden_size
  								lowerCAmelCase				=	encoder_ffn_dim
  								lowerCAmelCase				=	num_encoder_layers
  								lowerCAmelCase				=	num_encoder_attention_heads
  								lowerCAmelCase				=	decoder_ffn_dim
  								lowerCAmelCase				=	num_decoder_layers
  								lowerCAmelCase				=	num_decoder_attention_heads
  								lowerCAmelCase				=	max_position_embeddings
  								lowerCAmelCase				=	init_std  # Normal(0, this parameter)
  								lowerCAmelCase				=	activation_function
  
  								# parameters for prophetnet
  								lowerCAmelCase				=	ngram
  								lowerCAmelCase				=	num_buckets
  								lowerCAmelCase				=	relative_max_distance
  								lowerCAmelCase				=	disable_ngram_loss
  								lowerCAmelCase				=	eps
  
  								# 3 Types of Dropout
  								lowerCAmelCase				=	attention_dropout
  								lowerCAmelCase				=	activation_dropout
  								lowerCAmelCase				=	dropout
  
  								lowerCAmelCase				=	use_cache
  
  								super().__init__(
  								    pad_token_id=__SCREAMING_SNAKE_CASE	,    bos_token_id=__SCREAMING_SNAKE_CASE	,    eos_token_id=__SCREAMING_SNAKE_CASE	,    is_encoder_decoder=__SCREAMING_SNAKE_CASE	,    add_cross_attention=__SCREAMING_SNAKE_CASE	,    decoder_start_token_id=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )
  
  
  	@property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  	@num_hidden_layers.setter
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								raise NotImplementedError(
  								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
  								    ''' `num_decoder_layers`.'''       )
  
  
  
  