



'''simple docstring'''





from typing import TYPE_CHECKING

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


lowercase_	 =						{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_	 =						[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		lowercase_	 =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''





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


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = 'pegasus'
 __lowerCamelCase					: Tuple    = ['past_key_values']
 __lowerCamelCase					: List[Any]    = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



 def __init__(self				,     A=50_265				,     A=1_024				,     A=12				,     A=4_096				,     A=16				,     A=12				,     A=4_096				,     A=16				,     A=0.0				,     A=0.0				,     A=True				,     A=True				,     A="gelu"				,     A=1_024				,     A=0.1				,     A=0.0				,     A=0.0				,     A=0.02				,     A=0				,     A=False				,     A=0				,     A=1				,     A=1				,     **A				,     )					->  str:


      """simple docstring"""



      _a											=						vocab_size
      _a											=						max_position_embeddings
      _a											=						d_model
      _a											=						encoder_ffn_dim
      _a											=						encoder_layers
      _a											=						encoder_attention_heads
      _a											=						decoder_ffn_dim
      _a											=						decoder_layers
      _a											=						decoder_attention_heads
      _a											=						dropout
      _a											=						attention_dropout
      _a											=						activation_dropout
      _a											=						activation_function
      _a											=						init_std
      _a											=						encoder_layerdrop
      _a											=						decoder_layerdrop
      _a											=						use_cache
      _a											=						encoder_layers
      _a											=						scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=A				,     eos_token_id=A				,     is_encoder_decoder=A				,     decoder_start_token_id=A				,     forced_eos_token_id=A				,     **A				,     )



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.encoder_attention_heads



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.d_model



