



"""simple docstring"""




from typing import List, Union

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


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    "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 	__snake_case (				A__):







							'''simple docstring'''






							UpperCamelCase__			: Dict             =   """led"""
							UpperCamelCase__			: Union[str, Any]             =   {
							    """num_attention_heads""": """encoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """attention_probs_dropout_prob""": """attention_dropout""",
							    """initializer_range""": """init_std""",
							}


							def __init__(							self					,					a_=50_265					,					a_=16_384					,					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_=2					,					a_=0.0					,					a_=1					,					a_=0					,					a_=2					,					a_ = 512					,					**a_					,					):
											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=__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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   """regnet"""
							UpperCamelCase__			: int             =   ["""x""", """y"""]


							def __init__(							self					,					a_=3					,					a_=32					,					a_=[128, 192, 512, 1_088]					,					a_=[2, 6, 12, 2]					,					a_=64					,					a_="y"					,					a_="relu"					,					**a_					,					):
											super().__init__(**a_						)
											if layer_type not in self.layer_types:
															raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types						)}'''						)
											a__			 =						num_channels
											a__			 =						embedding_size
											a__			 =						hidden_sizes
											a__			 =						depths
											a__			 =						groups_width
											a__			 =						layer_type
											a__			 =						hidden_act
											# always downsample in the first stage
											a__			 =						True

