



import unittest

from transformers.image_processing_utils import get_size_dict







class       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   def       A__    (    self:     int							)	->      List[str]:
       # Test a dict with the wrong keys raises an error
       UpperCAmelCase_     :       Optional[Any]  		=					{"""wrong_key""": 224}
       with self.assertRaises(lowerCamelCase_							):
           get_size_dict(lowerCamelCase_							)

       UpperCAmelCase_     :       List[Any]  		=					{"""height""": 224}
       with self.assertRaises(lowerCamelCase_							):
           get_size_dict(lowerCamelCase_							)

       UpperCAmelCase_     :       Optional[int]  		=					{"""width""": 224, """shortest_edge""": 224}
       with self.assertRaises(lowerCamelCase_							):
           get_size_dict(lowerCamelCase_							)

       # Test a dict with the correct keys is returned as is
       UpperCAmelCase_     :       List[str]  		=					{"""height""": 224, """width""": 224}
       UpperCAmelCase_     :       Optional[int]  		=					get_size_dict(lowerCamelCase_							)
       self.assertEqual(lowerCamelCase_  ,lowerCamelCase_							)

       UpperCAmelCase_     :       Union[str, Any]  		=					{"""shortest_edge""": 224}
       UpperCAmelCase_     :       Dict  		=					get_size_dict(lowerCamelCase_							)
       self.assertEqual(lowerCamelCase_  ,{"""shortest_edge""": 224}							)

       UpperCAmelCase_     :       Optional[int]  		=					{"""longest_edge""": 224, """shortest_edge""": 224}
       UpperCAmelCase_     :       Dict  		=					get_size_dict(lowerCamelCase_							)
       self.assertEqual(lowerCamelCase_  ,{"""longest_edge""": 224, """shortest_edge""": 224}							)

       # Test a single int value which  represents (size, size)
       UpperCAmelCase_     :       Optional[Any]  		=					get_size_dict(224							)
       self.assertEqual(lowerCamelCase_  ,{"""height""": 224, """width""": 224}							)

       # Test a single int value which represents the shortest edge
       UpperCAmelCase_     :       int  		=					get_size_dict(224  ,default_to_square=lowerCamelCase_							)
       self.assertEqual(lowerCamelCase_  ,{"""shortest_edge""": 224}							)

       # Test a tuple of ints which represents (height, width)
       UpperCAmelCase_     :       List[Any]  		=					get_size_dict((150, 200)							)
       self.assertEqual(lowerCamelCase_  ,{"""height""": 150, """width""": 200}							)

       # Test a tuple of ints which represents (width, height)
       UpperCAmelCase_     :       int  		=					get_size_dict((150, 200)  ,height_width_order=lowerCamelCase_							)
       self.assertEqual(lowerCamelCase_  ,{"""height""": 200, """width""": 150}							)

       # Test an int representing the shortest edge and max_size which represents the longest edge
       UpperCAmelCase_     :       Optional[Any]  		=					get_size_dict(224  ,max_size=256  ,default_to_square=lowerCamelCase_							)
       self.assertEqual(lowerCamelCase_  ,{"""shortest_edge""": 224, """longest_edge""": 256}							)

       # Test int with default_to_square=True and max_size fails
       with self.assertRaises(lowerCamelCase_							):
           get_size_dict(224  ,max_size=256  ,default_to_square=lowerCamelCase_							)



from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "microsoft/prophetnet-large-uncased": (
        "https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"
    ),
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Union[str, Any]												=	"prophetnet"
	_lowercase	: Any												=	["past_key_values"]
	_lowercase	: int												=	{
	    "num_attention_heads": "num_encoder_attention_heads",
	}







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[Union[str, Callable]] = "gelu"	,			__UpperCamelCase:			Optional[int] = 3_05_22	,			__UpperCamelCase:			Optional[int] = 10_24	,			__UpperCamelCase:			Optional[int] = 40_96	,			__UpperCamelCase:			Optional[int] = 12	,			__UpperCamelCase:			Optional[int] = 16	,			__UpperCamelCase:			Optional[int] = 40_96	,			__UpperCamelCase:			Optional[int] = 12	,			__UpperCamelCase:			Optional[int] = 16	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[float] = 0.1	,			__UpperCamelCase:			Optional[int] = 5_12	,			__UpperCamelCase:			Optional[float] = 0.02	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[int] = 0	,			__UpperCamelCase:			Optional[int] = 2	,			__UpperCamelCase:			Optional[int] = 32	,			__UpperCamelCase:			Optional[int] = 1_28	,			__UpperCamelCase:			Optional[bool] = False	,			__UpperCamelCase:			Optional[float] = 0.0	,			__UpperCamelCase:			Optional[bool] = True	,			__UpperCamelCase:			Optional[int] = 0	,			__UpperCamelCase:			Optional[int] = 1	,			__UpperCamelCase:			Optional[int] = 2	,			**__UpperCamelCase:			Optional[int]	,			):

						'''simple docstring'''





						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     encoder_ffn_dim
						__magic_name__				=     num_encoder_layers
						__magic_name__				=     num_encoder_attention_heads
						__magic_name__				=     decoder_ffn_dim
						__magic_name__				=     num_decoder_layers
						__magic_name__				=     num_decoder_attention_heads
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     init_std  # Normal(0, this parameter)
						__magic_name__				=     activation_function

						# parameters for prophetnet
						__magic_name__				=     ngram
						__magic_name__				=     num_buckets
						__magic_name__				=     relative_max_distance
						__magic_name__				=     disable_ngram_loss
						__magic_name__				=     eps

						# 3 Types of Dropout
						__magic_name__				=     attention_dropout
						__magic_name__				=     activation_dropout
						__magic_name__				=     dropout

						__magic_name__				=     use_cache

						super().__init__(
						    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			add_cross_attention=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			**__UpperCamelCase	,			)







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						return self.num_encoder_layers + self.num_decoder_layers







	@num_hidden_layers.setter
	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			List[Any]   ):

						'''simple docstring'''





						raise NotImplementedError(
						    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
						    ' `num_decoder_layers`.'   )




