


from typing import Callable, Optional, Union

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


a					:				Union[str, Any]       =      logging.get_logger(__name__)

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

class   lowerCamelCase						(	lowercase__  ):
       _UpperCamelCase     		=   """prophetnet"""
       _UpperCamelCase     		=   ["""past_key_values"""]
       _UpperCamelCase     		=   {
           """num_attention_heads""": """num_encoder_attention_heads""",
       }

       def __init__(      self						,    a__ = 0.1						,    a__ = "gelu"						,    a__ = 3_05_22						,    a__ = 10_24						,    a__ = 40_96						,    a__ = 12						,    a__ = 16						,    a__ = 40_96						,    a__ = 12						,    a__ = 16						,    a__ = 0.1						,    a__ = 0.1						,    a__ = 5_12						,    a__ = 0.02						,    a__ = True						,    a__ = True						,    a__ = 0						,    a__ = 2						,    a__ = 32						,    a__ = 1_28						,    a__ = False						,    a__ = 0.0						,    a__ = True						,    a__ = 0						,    a__ = 1						,    a__ = 2						,    **a__						,    ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   vocab_size
        snake_case :			Dict							=   hidden_size
        snake_case :			str							=   encoder_ffn_dim
        snake_case :			Optional[int]							=   num_encoder_layers
        snake_case :			Any							=   num_encoder_attention_heads
        snake_case :			Any							=   decoder_ffn_dim
        snake_case :			str							=   num_decoder_layers
        snake_case :			List[str]							=   num_decoder_attention_heads
        snake_case :			Tuple							=   max_position_embeddings
        snake_case :			Optional[int]							=   init_std  # Normal(0, this parameter)
        snake_case :			Optional[int]							=   activation_function

        # parameters for prophetnet
        snake_case :			Union[str, Any]							=   ngram
        snake_case :			str							=   num_buckets
        snake_case :			List[str]							=   relative_max_distance
        snake_case :			Tuple							=   disable_ngram_loss
        snake_case :			Union[str, Any]							=   eps

        # 3 Types of Dropout
        snake_case :			Dict							=   attention_dropout
        snake_case :			List[Any]							=   activation_dropout
        snake_case :			List[str]							=   dropout

        snake_case :			int							=   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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return self.num_encoder_layers + self.num_decoder_layers



       @num_hidden_layers.setter
       def 					UpperCAmelCase					(      self						,    a__       ):






        '''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 os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__lowerCamelCase								=      [(1,)]







def  _snake_case			(							)  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[Any]					   =		bool(os.getenv("RUN_SLOW"       ,	0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"







def  _snake_case			(							)  ->  Tuple:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		bool(os.getenv("RUN_SLOW"       ,	0     )     )
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 	snake_case_			(unittest.TestCase	):




       """simple docstring"""






       @slow
       @require_torch_gpu
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @require_torch_gpu
       @slow
       def 			A_ (  self):

             """simple docstring"""





             check_slow_torch_cuda()



       @parameterized.expand(lowercase)
       @slow
       def 			A_ (  self  ,lowercase=None):

             """simple docstring"""





             check_slow()







@slow
@require_torch_gpu
def  _snake_case			(							)  ->  str:






      '''simple docstring'''







      check_slow_torch_cuda()







@require_torch_gpu
@slow
def  _snake_case			(							)  ->  Union[str, Any]:






      '''simple docstring'''







      check_slow_torch_cuda()







@slow
@pytest.mark.parametrize("param"       ,	[1]     )
def  _snake_case			(							__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      check_slow()







@pytest.mark.parametrize("param"       ,	[1]     )
@slow
def  _snake_case			(							__snake_case     )  ->  Any:






      '''simple docstring'''







      check_slow()
