





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a   	=    {}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    ["NllbTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    ["NllbTokenizerFast"]


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb import NllbTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb_fast import NllbTokenizerFast

else:
    import sys

    a   	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













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


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Optional[Any]								={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   "pegasus"
		__snake_case      :       List[str]     =   ["past_key_values"]
		__snake_case      :       Union[str, Any]     =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(					self       ,A__=50265       ,A__=1024       ,A__=12       ,A__=4096       ,A__=16       ,A__=12       ,A__=4096       ,A__=16       ,A__=0.0       ,A__=0.0       ,A__=True       ,A__=True       ,A__="gelu"       ,A__=1024       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=0       ,A__=False       ,A__=0       ,A__=1       ,A__=1       ,**A__       ,):
						_A	:				List[str]   =      vocab_size
						_A	:				List[Any]   =      max_position_embeddings
						_A	:				Union[str, Any]   =      d_model
						_A	:				Union[str, Any]   =      encoder_ffn_dim
						_A	:				Union[str, Any]   =      encoder_layers
						_A	:				int   =      encoder_attention_heads
						_A	:				List[Any]   =      decoder_ffn_dim
						_A	:				Optional[int]   =      decoder_layers
						_A	:				Tuple   =      decoder_attention_heads
						_A	:				List[str]   =      dropout
						_A	:				Tuple   =      attention_dropout
						_A	:				Union[str, Any]   =      activation_dropout
						_A	:				int   =      activation_function
						_A	:				Union[str, Any]   =      init_std
						_A	:				Any   =      encoder_layerdrop
						_A	:				int   =      decoder_layerdrop
						_A	:				Tuple   =      use_cache
						_A	:				Dict   =      encoder_layers
						_A	:				List[str]   =      scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=A__       ,eos_token_id=A__       ,is_encoder_decoder=A__       ,decoder_start_token_id=A__       ,forced_eos_token_id=A__       ,**A__       ,)







		@property
		def  A__      (					self		):
						return self.encoder_attention_heads







		@property
		def  A__      (					self		):
						return self.d_model



