




from typing import Callable, Optional, Union

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


lowercase__   :    Tuple 					=      logging.get_logger(__name__)

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




class     a__ (      UpperCamelCase__   ):
     a	:    Any	      =			"""prophetnet"""
     a	:    Any	      =			["""past_key_values"""]
     a	:    str	      =			{
         """num_attention_heads""": """num_encoder_attention_heads""",
     }







     def __init__(			self						,	A = 0.1						,	A = "gelu"						,	A = 30522						,	A = 1024						,	A = 4096						,	A = 12						,	A = 16						,	A = 4096						,	A = 12						,	A = 16						,	A = 0.1						,	A = 0.1						,	A = 512						,	A = 0.0_2						,	A = True						,	A = True						,	A = 0						,	A = 2						,	A = 32						,	A = 128						,	A = False						,	A = 0.0						,	A = True						,	A = 0						,	A = 1						,	A = 2						,	**A						,	)						->					List[str]:



          '''simple docstring'''



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

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

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

          a											=   use_cache

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







     @property
     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          return self.num_encoder_layers + self.num_decoder_layers







     @num_hidden_layers.setter
     def     lowerCAmelCase_       (			self						,	A				)						->					Tuple:



          '''simple docstring'''



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







import unittest

from transformers.image_processing_utils import get_size_dict




class     a__ (      unittest.TestCase   ):







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   {"wrong_key": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"height": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"width": 224, "shortest_edge": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          # Test a dict with the correct keys is returned as is
          a											=   {"height": 224, "width": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	A				)

          a											=   {"shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          a											=   {"longest_edge": 224, "shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"longest_edge": 224, "shortest_edge": 224}				)

          # Test a single int value which  represents (size, size)
          a											=   get_size_dict(224				)
          self.assertEqual(A						,	{"height": 224, "width": 224}				)

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

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

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

          # Test an int representing the shortest edge and max_size which represents the longest edge
          a											=   get_size_dict(224						,	max_size=256						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224, "longest_edge": 256}				)

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

