


'''simple docstring'''
from typing import Callable, Optional, Union

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


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

lowerCAmelCase   : int             =				{
    """microsoft/xprophetnet-large-wiki100-cased""": (
        """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
    ),
}




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				_snake_case						       =				'''xlm-prophetnet'''
				_snake_case						       =				['''past_key_values''']
				_snake_case						       =				{
				    '''num_attention_heads''': '''num_encoder_attention_heads''',
				}

				def __init__(     self							,      a_ = 0.1							,      a_ = "gelu"							,      a_ = 3_0_5_2_2							,      a_ = 1_0_2_4							,      a_ = 4_0_9_6							,      a_ = 1_2							,      a_ = 1_6							,      a_ = 4_0_9_6							,      a_ = 1_2							,      a_ = 1_6							,      a_ = 0.1							,      a_ = 0.1							,      a_ = 5_1_2							,      a_ = 0.02							,      a_ = True							,      a_ = True							,      a_ = 0							,      a_ = 2							,      a_ = 3_2							,      a_ = 1_2_8							,      a_ = False							,      a_ = 0.0							,      a_ = True							,      a_ = 0							,      a_ = 1							,      a_ = 2							,      **a_							,      )    ->  List[str]:
					lowercase						:		int   					=		vocab_size
					lowercase						:		Any   					=		hidden_size
					lowercase						:		Optional[Any]   					=		encoder_ffn_dim
					lowercase						:		List[Any]   					=		num_encoder_layers
					lowercase						:		int   					=		num_encoder_attention_heads
					lowercase						:		Tuple   					=		decoder_ffn_dim
					lowercase						:		Optional[int]   					=		num_decoder_layers
					lowercase						:		List[Any]   					=		num_decoder_attention_heads
					lowercase						:		List[Any]   					=		max_position_embeddings
					lowercase						:		Dict   					=		init_std  # Normal(0, this parameter)
					lowercase						:		List[str]   					=		activation_function

					# parameters for xlmprophetnet
					lowercase						:		Optional[Any]   					=		ngram
					lowercase						:		Any   					=		num_buckets
					lowercase						:		List[Any]   					=		relative_max_distance
					lowercase						:		Optional[int]   					=		disable_ngram_loss
					lowercase						:		Tuple   					=		eps

					# 3 Types of Dropout
					lowercase						:		int   					=		attention_dropout
					lowercase						:		Any   					=		activation_dropout
					lowercase						:		str   					=		dropout

					lowercase						:		str   					=		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 	a__		(     self							)    ->  int:
					return self.num_encoder_layers + self.num_decoder_layers


				@num_hidden_layers.setter
				def 	a__		(     self							,      a_							)    ->  Optional[int]:
					raise NotImplementedError(
					    "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
					    " `num_decoder_layers`."							)






'''simple docstring'''
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase   : Dict             =				{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : int             =				[
					    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """VanForImageClassification""",
					    """VanModel""",
					    """VanPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase   : Tuple             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)


