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


lowerCamelCase_				:		List[str]       						=       logging.get_logger(__name__)

lowerCamelCase_				:		str       						=       {
    """google/pegasus-x-base""": """https://huggingface.co/google/pegasus-x-base/resolve/main/config.json""",
    """google/pegasus-x-large""": """https://huggingface.co/google/pegasus-x-large/resolve/main/config.json""",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}


class 						a__							(   __snake_case		):
			A__				:   List[Any]								=			'pegasus_x'
			A__				:   Optional[int]								=			['past_key_values']
			A__				:   Tuple								=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



			def __init__(						self      ,						UpperCAmelCase=9_6_1_0_3      ,						UpperCAmelCase=1_6_3_8_4      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=4_0_9_6      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=4_0_9_6      ,						UpperCAmelCase=1_6      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=True      ,						UpperCAmelCase=True      ,						UpperCAmelCase="gelu"      ,						UpperCAmelCase=1_0_2_4      ,						UpperCAmelCase=0.1      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.02      ,						UpperCAmelCase=0      ,						UpperCAmelCase=True      ,						UpperCAmelCase=0      ,						UpperCAmelCase=1      ,						UpperCAmelCase=1      ,						UpperCAmelCase=3_2      ,						UpperCAmelCase=5_1_2      ,						UpperCAmelCase=True      ,						**UpperCAmelCase      ,						) ->    int:
								__a							=   vocab_size
								__a							=   max_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							=   use_cache
								__a							=   encoder_layers
								__a							=   scale_embedding  # scale factor will be sqrt(d_model) if True

								__a							=   num_global_tokens
								__a							=   block_size
								__a							=   stagger_local_blocks

								super().__init__(
								    pad_token_id=UpperCAmelCase      ,						eos_token_id=UpperCAmelCase      ,						is_encoder_decoder=UpperCAmelCase      ,						decoder_start_token_id=UpperCAmelCase      ,						forced_eos_token_id=UpperCAmelCase      ,						**UpperCAmelCase      ,						)



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								return self.encoder_attention_heads







			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								return self.d_model





from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


lowerCamelCase_				:		List[Any]       						=       TypeVar("""DatasetType""", Dataset, IterableDataset)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					if isinstance(__lowerCamelCase      ,				__lowerCamelCase						):
										return _split_by_node_map_style_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)
					else:
										return _split_by_node_iterable_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)




