
'''simple docstring'''


from typing import Callable, Optional, Union

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


__lowercase							:				Optional[int]					=       logging.get_logger(__name__)

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

class 		__lowercase			(					_lowercase      ):
			lowerCamelCase     :						Tuple		 =     "prophetnet"
			lowerCamelCase     :						List[str]		 =     ["past_key_values"]
			lowerCamelCase     :						Tuple		 =     {
			    "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     ,				):
								lowerCamelCase_   : int       =						vocab_size
								lowerCamelCase_   : str       =						hidden_size
								lowerCamelCase_   : Optional[int]       =						encoder_ffn_dim
								lowerCamelCase_   : Dict       =						num_encoder_layers
								lowerCamelCase_   : Optional[int]       =						num_encoder_attention_heads
								lowerCamelCase_   : List[Any]       =						decoder_ffn_dim
								lowerCamelCase_   : str       =						num_decoder_layers
								lowerCamelCase_   : Dict       =						num_decoder_attention_heads
								lowerCamelCase_   : Union[str, Any]       =						max_position_embeddings
								lowerCamelCase_   : Union[str, Any]       =						init_std  # Normal(0, this parameter)
								lowerCamelCase_   : List[str]       =						activation_function

								# parameters for prophetnet
								lowerCamelCase_   : Optional[Any]       =						ngram
								lowerCamelCase_   : str       =						num_buckets
								lowerCamelCase_   : Any       =						relative_max_distance
								lowerCamelCase_   : int       =						disable_ngram_loss
								lowerCamelCase_   : str       =						eps

								# 3 Types of Dropout
								lowerCamelCase_   : Optional[Any]       =						attention_dropout
								lowerCamelCase_   : Tuple       =						activation_dropout
								lowerCamelCase_   : Optional[Any]       =						dropout

								lowerCamelCase_   : Any       =						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 ):
								return self.num_encoder_layers + self.num_decoder_layers






			@num_hidden_layers.setter
			def     UpperCAmelCase__	(self     ,				A ):
								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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					__lowercase							:				Union[str, Any]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


