
import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
		_lowercase											=			[]

		_lowercase											=			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
		_lowercase											=			glob.glob(pattern)
		# TODO: deal with TF/Flax too
		_lowercase											=			[
		    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
		]

		for test_file in test_files:
				_lowercase											=			get_tester_classes(test_file)
				for tester_class in tester_classes:
						# A few tester classes don't have `parent` parameter in `__init__`.
						# TODO: deal this better
						try:
								_lowercase											=			tester_class(parent=None)
						except Exception:
								continue
						if hasattr(tester, '''get_config'''):
								_lowercase											=			tester.get_config()
								for k, v in config.to_dict().items():
										if isinstance(v, int):
												_lowercase											=			None
												if k in ["vocab_size"]:
														_lowercase											=			1_00
												elif k in ["max_position_embeddings"]:
														_lowercase											=			1_28
												elif k in ["hidden_size", "d_model"]:
														_lowercase											=			40
												elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
														_lowercase											=			5
												if target is not None and v > target:
														failures.append(
														    F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
														    F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
														    F""" than {target}."""
														)

		if len(failures) > 0:
				raise Exception(F"""There were {len(failures)} failures:\n""" + '''\n'''.join(failures))

"""simple docstring"""


from typing import Callable, Optional, Union

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


_lowercase											=			logging.get_logger(__name__)

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







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      int =      '''prophetnet'''
						_lowerCamelCase:      Optional[Any] =      ['''past_key_values''']
						_lowerCamelCase:      Dict =      {
						    '''num_attention_heads''': '''num_encoder_attention_heads''',
						}


						def __init__(	self       : Tuple					,A_       : Optional[float] = 0.1					,A_       : Optional[Union[str, Callable]] = "gelu"					,A_       : Optional[int] = 3_0522					,A_       : Optional[int] = 1024					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[float] = 0.1					,A_       : Optional[float] = 0.1					,A_       : Optional[int] = 512					,A_       : Optional[float] = 0.02					,A_       : Optional[bool] = True					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 2					,A_       : Optional[int] = 32					,A_       : Optional[int] = 128					,A_       : Optional[bool] = False					,A_       : Optional[float] = 0.0					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 1					,A_       : Optional[int] = 2					,**A_       : Tuple					,)       ->				Any:
							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   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							return self.num_encoder_layers + self.num_decoder_layers




						@num_hidden_layers.setter
						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Any )       ->				int:
							raise NotImplementedError(
							    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
							    ' `num_decoder_layers`.' )