



'''simple docstring'''
from typing import List, Union

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


__SCREAMING_SNAKE_CASE :      int             =logging.get_logger(__name__)

__SCREAMING_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   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Dict						=				'''led'''
	A__    :    Optional[int]						=				{
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''hidden_size''': '''d_model''',
	    '''attention_probs_dropout_prob''': '''attention_dropout''',
	    '''initializer_range''': '''init_std''',
	}


	def __init__(		self					,   A=5_02_65					,   A=1_63_84					,   A=10_24					,   A=12					,   A=40_96					,   A=16					,   A=12					,   A=40_96					,   A=16					,   A=0.0					,   A=0.0					,   A=True					,   A=True					,   A="gelu"					,   A=10_24					,   A=0.1					,   A=0.0					,   A=0.0					,   A=0.02					,   A=2					,   A=0.0					,   A=1					,   A=0					,   A=2					,   A = 5_12					,   **A					,   ) ->							List[str]:
				A:      Tuple														=   vocab_size
				A:      Any														=   max_encoder_position_embeddings
				A:      str														=   max_decoder_position_embeddings
				A:      List[str]														=   d_model
				A:      Optional[Any]														=   encoder_ffn_dim
				A:      Dict														=   encoder_layers
				A:      List[Any]														=   encoder_attention_heads
				A:      Union[str, Any]														=   decoder_ffn_dim
				A:      List[Any]														=   decoder_layers
				A:      Optional[Any]														=   decoder_attention_heads
				A:      Dict														=   dropout
				A:      str														=   attention_dropout
				A:      Optional[int]														=   activation_dropout
				A:      int														=   activation_function
				A:      Optional[int]														=   init_std
				A:      Tuple														=   encoder_layerdrop
				A:      Any														=   decoder_layerdrop
				A:      str														=   classifier_dropout
				A:      int														=   use_cache
				A:      Dict														=   encoder_layers
				A:      List[Any]														=   attention_window

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










'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__SCREAMING_SNAKE_CASE :      int             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :      Optional[int]             ={
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}


class   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			snake_case_					):
	"""simple docstring"""


	A__    :    List[str]						=				'''convnext'''


	def __init__(		self					,   A=3					,   A=4					,   A=4					,   A=None					,   A=None					,   A="gelu"					,   A=0.02					,   A=1e-12					,   A=1e-6					,   A=0.0					,   A=2_24					,   A=None					,   A=None					,   **A					,   ) ->							Dict:
				super().__init__(**A			)

				A:      List[str]														=   num_channels
				A:      Optional[Any]														=   patch_size
				A:      List[str]														=   num_stages
				A:      Tuple														=   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
				A:      List[Any]														=   [3, 3, 9, 3] if depths is None else depths
				A:      int														=   hidden_act
				A:      int														=   initializer_range
				A:      Optional[int]														=   layer_norm_eps
				A:      Tuple														=   layer_scale_init_value
				A:      int														=   drop_path_rate
				A:      Tuple														=   image_size
				A:      Union[str, Any]														=   ["""stem"""] + [f'stage{idx}' for idx in range(1					,   len(self.depths			) + 1			)]
				A							,		A:      List[str]														=   get_aligned_output_features_output_indices(
				    out_features=A					,   out_indices=A					,   stage_names=self.stage_names			)







class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Any						=				version.parse('''1.11'''					)


	@property
	def      a__ (		self			) ->							Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
				    ]			)





	@property
	def      a__ (		self			) ->							float:
				return 1e-5





