
from typing import List, Union

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


snake_case_      :					str				      =     logging.get_logger(__name__)

snake_case_      :					List[Any]				      =     {
    '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 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Union[str, Any]      =							'''led'''
							lowerCamelCase__:				str      =							{
							    '''num_attention_heads''': '''encoder_attention_heads''',
							    '''hidden_size''': '''d_model''',
							    '''attention_probs_dropout_prob''': '''attention_dropout''',
							    '''initializer_range''': '''init_std''',
							}



							def __init__(     self	,				__snake_case=5_0_2_6_5	,				__snake_case=1_6_3_8_4	,				__snake_case=1_0_2_4	,				__snake_case=1_2	,				__snake_case=4_0_9_6	,				__snake_case=1_6	,				__snake_case=1_2	,				__snake_case=4_0_9_6	,				__snake_case=1_6	,				__snake_case=0.0	,				__snake_case=0.0	,				__snake_case=True	,				__snake_case=True	,				__snake_case="gelu"	,				__snake_case=1_0_2_4	,				__snake_case=0.1	,				__snake_case=0.0	,				__snake_case=0.0	,				__snake_case=0.02	,				__snake_case=2	,				__snake_case=0.0	,				__snake_case=1	,				__snake_case=0	,				__snake_case=2	,				__snake_case = 5_1_2	,				**__snake_case	,				)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     vocab_size
								UpperCAmelCase:					Union[str, Any] 						=     max_encoder_position_embeddings
								UpperCAmelCase:					str 						=     max_decoder_position_embeddings
								UpperCAmelCase:					List[str] 						=     d_model
								UpperCAmelCase:					Union[str, Any] 						=     encoder_ffn_dim
								UpperCAmelCase:					Union[str, Any] 						=     encoder_layers
								UpperCAmelCase:					List[str] 						=     encoder_attention_heads
								UpperCAmelCase:					Dict 						=     decoder_ffn_dim
								UpperCAmelCase:					List[str] 						=     decoder_layers
								UpperCAmelCase:					Optional[int] 						=     decoder_attention_heads
								UpperCAmelCase:					Tuple 						=     dropout
								UpperCAmelCase:					Optional[int] 						=     attention_dropout
								UpperCAmelCase:					Dict 						=     activation_dropout
								UpperCAmelCase:					Optional[int] 						=     activation_function
								UpperCAmelCase:					Tuple 						=     init_std
								UpperCAmelCase:					Union[str, Any] 						=     encoder_layerdrop
								UpperCAmelCase:					List[Any] 						=     decoder_layerdrop
								UpperCAmelCase:					List[str] 						=     classifier_dropout
								UpperCAmelCase:					Optional[Any] 						=     use_cache
								UpperCAmelCase:					Union[str, Any] 						=     encoder_layers
								UpperCAmelCase:					Tuple 						=     attention_window

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


from typing import TYPE_CHECKING

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


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Dict				      =     ['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
