


from typing import TYPE_CHECKING

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


lowercase_								=    {'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    [
		    'REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RegNetForImageClassification',
		    'RegNetModel',
		    'RegNetPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    [
		    'TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFRegNetForImageClassification',
		    'TFRegNetModel',
		    'TFRegNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    [
		    'FlaxRegNetForImageClassification',
		    'FlaxRegNetModel',
		    'FlaxRegNetPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_regnet import (
				    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RegNetForImageClassification,
				    RegNetModel,
				    RegNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_regnet import (
				    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRegNetForImageClassification,
				    TFRegNetModel,
				    TFRegNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_regnet import (
				    FlaxRegNetForImageClassification,
				    FlaxRegNetModel,
				    FlaxRegNetPreTrainedModel,
				)


else:
		import sys

		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure)






from typing import List, Union

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


lowercase_								=    logging.get_logger(__name__)

lowercase_								=    {
    '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 					_UpperCamelCase (      lowerCamelCase__	):







							'''simple docstring'''
							_A  				=  "led"
							_A  				=  {
							    "num_attention_heads": "encoder_attention_heads",
							    "hidden_size": "d_model",
							    "attention_probs_dropout_prob": "attention_dropout",
							    "initializer_range": "init_std",
							}







							def __init__(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]=5_0_2_6_5		,				SCREAMING_SNAKE_CASE_  :				str=1_6_3_8_4		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_2		,				SCREAMING_SNAKE_CASE_  :				Any=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=1_6		,				SCREAMING_SNAKE_CASE_  :				str=1_2		,				SCREAMING_SNAKE_CASE_  :				List[Any]=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_6		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=0.0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				List[Any]=True		,				SCREAMING_SNAKE_CASE_  :				str=True		,				SCREAMING_SNAKE_CASE_  :				List[str]="gelu"		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.1		,				SCREAMING_SNAKE_CASE_  :				Dict=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.02		,				SCREAMING_SNAKE_CASE_  :				str=2		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=1		,				SCREAMING_SNAKE_CASE_  :				str=0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=2		,				SCREAMING_SNAKE_CASE_  :				Union[List[int], int] = 5_1_2		,				**SCREAMING_SNAKE_CASE_  :				Dict		,				):
													_a     =			vocab_size
													_a     =			max_encoder_position_embeddings
													_a     =			max_decoder_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     =			classifier_dropout
													_a     =			use_cache
													_a     =			encoder_layers
													_a     =			attention_window

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


