




"""simple docstring"""

from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
		import torch

UpperCamelCase__			:     int			       =				logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
		from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):




		def    snake_case__						(							cls :		Dict		):
							return cls




def    snake_case__						(							lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Union[str, Any] = None					,       lowerCAmelCase :		Tuple = None					,       lowerCAmelCase :		List[str] = None					,       lowerCAmelCase :		Optional[int] = None					,       ):
					__snake_case        =						device
					__snake_case        =						shape[0]

					__snake_case        =						layout or torch.strided
					__snake_case        =						device or torch.device("""cpu"""		)

					if generator is not None:
										__snake_case        =						generator.device.type if not isinstance(__UpperCamelCase					,       __UpperCamelCase		) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															__snake_case        =						"""cpu"""
															if device != "mps":
																				logger.info(
																				    F'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
																				    F''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
																				    F''' slighly speed up this function by passing a generator that was created on the {device} device.'''		)
										elif gen_device_type != device.type and gen_device_type == "cuda":
															raise ValueError(F'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''		)

					if isinstance(__UpperCamelCase					,       __UpperCamelCase		):
										__snake_case        =						(1,) + shape[1:]
										__snake_case        =						[
										    torch.randn(__UpperCamelCase					,       generator=generator[i]					,       device=__UpperCamelCase					,       dtype=__UpperCamelCase					,       layout=__UpperCamelCase		)
										    for i in range(__UpperCamelCase		)
										]
										__snake_case        =						torch.cat(__UpperCamelCase					,       dim=0		).to(__UpperCamelCase		)
					else:
										__snake_case        =						torch.randn(__UpperCamelCase					,       generator=__UpperCamelCase					,       device=__UpperCamelCase					,       dtype=__UpperCamelCase					,       layout=__UpperCamelCase		).to(__UpperCamelCase		)

					return latents




def    snake_case__						(							lowerCAmelCase :		List[str]		):







					if is_torch_version("""<"""					,       """2.0.0"""		) or not hasattr(__UpperCamelCase					,       """_dynamo"""		):
										return False
					return isinstance(__UpperCamelCase					,       torch._dynamo.eval_frame.OptimizedModule		)










"""simple docstring"""


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    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					"led"
   A_		:      Any							=					{
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
       "initializer_range": "init_std",
   }


   def __init__(      self					:	Dict,			_lowerCamelCase					:	str=5_02_65,			_lowerCamelCase					:	List[Any]=1_63_84,			_lowerCamelCase					:	int=10_24,			_lowerCamelCase					:	List[Any]=12,			_lowerCamelCase					:	int=40_96,			_lowerCamelCase					:	str=16,			_lowerCamelCase					:	Dict=12,			_lowerCamelCase					:	Optional[int]=40_96,			_lowerCamelCase					:	Any=16,			_lowerCamelCase					:	int=0.0,			_lowerCamelCase					:	str=0.0,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	Tuple="gelu",			_lowerCamelCase					:	Tuple=10_24,			_lowerCamelCase					:	Dict=0.1,			_lowerCamelCase					:	Tuple=0.0,			_lowerCamelCase					:	Union[str, Any]=0.0,			_lowerCamelCase					:	Optional[int]=0.02,			_lowerCamelCase					:	List[str]=2,			_lowerCamelCase					:	Dict=0.0,			_lowerCamelCase					:	Dict=1,			_lowerCamelCase					:	str=0,			_lowerCamelCase					:	Union[str, Any]=2,			_lowerCamelCase					:	Union[List[int], int] = 5_12,			**_lowerCamelCase					:	Any,			):

     '''simple docstring'''
     __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=_lowerCamelCase,			bos_token_id=_lowerCamelCase,			eos_token_id=_lowerCamelCase,			is_encoder_decoder=_lowerCamelCase,			decoder_start_token_id=_lowerCamelCase,			**_lowerCamelCase,			)




