



'''simple docstring'''

from typing import List, Union

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


__lowerCAmelCase		    =     logging.get_logger(__name__)

__lowerCAmelCase		    =     {
    '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
    # See all LED models at https://huggingface.co/models?filter=led
}

class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					List[str]            =      'led'
							lowerCAmelCase :					Any            =      {
							    'num_attention_heads': 'encoder_attention_heads',
							    'hidden_size': 'd_model',
							    'attention_probs_dropout_prob': 'attention_dropout',
							    'initializer_range': 'init_std',
							}







							def __init__(							self      : int			,_UpperCAmelCase      : str=50265			,_UpperCAmelCase      : Dict=16384			,_UpperCAmelCase      : Union[str, Any]=1024			,_UpperCAmelCase      : Tuple=12			,_UpperCAmelCase      : Any=4096			,_UpperCAmelCase      : Union[str, Any]=16			,_UpperCAmelCase      : Tuple=12			,_UpperCAmelCase      : Dict=4096			,_UpperCAmelCase      : str=16			,_UpperCAmelCase      : Any=0.0			,_UpperCAmelCase      : str=0.0			,_UpperCAmelCase      : int=True			,_UpperCAmelCase      : Tuple=True			,_UpperCAmelCase      : str="gelu"			,_UpperCAmelCase      : Any=1024			,_UpperCAmelCase      : Union[str, Any]=0.1			,_UpperCAmelCase      : Dict=0.0			,_UpperCAmelCase      : Dict=0.0			,_UpperCAmelCase      : List[Any]=0.02			,_UpperCAmelCase      : Optional[int]=2			,_UpperCAmelCase      : List[Any]=0.0			,_UpperCAmelCase      : Optional[int]=1			,_UpperCAmelCase      : str=0			,_UpperCAmelCase      : str=2			,_UpperCAmelCase      : Union[List[int], int] = 512			,**_UpperCAmelCase      : Dict			,):
									_a  :   Optional[Any]				 =							vocab_size
									_a  :   List[Any]				 =							max_encoder_position_embeddings
									_a  :   int				 =							max_decoder_position_embeddings
									_a  :   Dict				 =							d_model
									_a  :   List[str]				 =							encoder_ffn_dim
									_a  :   List[Any]				 =							encoder_layers
									_a  :   Tuple				 =							encoder_attention_heads
									_a  :   List[str]				 =							decoder_ffn_dim
									_a  :   str				 =							decoder_layers
									_a  :   List[str]				 =							decoder_attention_heads
									_a  :   Dict				 =							dropout
									_a  :   Optional[Any]				 =							attention_dropout
									_a  :   Dict				 =							activation_dropout
									_a  :   Union[str, Any]				 =							activation_function
									_a  :   List[Any]				 =							init_std
									_a  :   Tuple				 =							encoder_layerdrop
									_a  :   List[str]				 =							decoder_layerdrop
									_a  :   str				 =							classifier_dropout
									_a  :   Optional[int]				 =							use_cache
									_a  :   Union[str, Any]				 =							encoder_layers
									_a  :   Optional[Any]				 =							attention_window

									super().__init__(
									    pad_token_id=_UpperCAmelCase			,bos_token_id=_UpperCAmelCase			,eos_token_id=_UpperCAmelCase			,is_encoder_decoder=_UpperCAmelCase			,decoder_start_token_id=_UpperCAmelCase			,**_UpperCAmelCase			,)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase		    =     {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5Tokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5TokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''T5EncoderModel''',
							    '''T5ForConditionalGeneration''',
							    '''T5Model''',
							    '''T5PreTrainedModel''',
							    '''load_tf_weights_in_t5''',
							    '''T5ForQuestionAnswering''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFT5EncoderModel''',
							    '''TFT5ForConditionalGeneration''',
							    '''TFT5Model''',
							    '''TFT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''FlaxT5EncoderModel''',
							    '''FlaxT5ForConditionalGeneration''',
							    '''FlaxT5Model''',
							    '''FlaxT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta import TaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta_fast import TaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ta import (
														    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TaEncoderModel,
														    TaForConditionalGeneration,
														    TaForQuestionAnswering,
														    TaModel,
														    TaPreTrainedModel,
														    load_tf_weights_in_ta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ta import (
														    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTaEncoderModel,
														    TFTaForConditionalGeneration,
														    TFTaModel,
														    TFTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_ta import (
														    FlaxTaEncoderModel,
														    FlaxTaForConditionalGeneration,
														    FlaxTaModel,
														    FlaxTaPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase		    =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




