





"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast






@dataclass
class 					_SCREAMING_SNAKE_CASE(   datasets.BuilderConfig  ):
       SCREAMING_SNAKE_CASE_   :      List[str]							=  None






class 					_SCREAMING_SNAKE_CASE(   datasets.ArrowBasedBuilder  ):
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  PandasConfig




       def     _UpperCamelCase     (						self							)			->      Optional[Any]:





             """simple docstring"""






             return datasets.DatasetInfo(features=self.config.features							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      List[Any]:





             """simple docstring"""






             if not self.config.data_files:
                   raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  dl_manager.download_and_extract(self.config.data_files							)
             if isinstance(UpperCamelCase__		,(str, list, tuple)							):
                   __SCREAMING_SNAKE_CASE   :int						=  data_files
                   if isinstance(UpperCamelCase__		,UpperCamelCase__							):
                         __SCREAMING_SNAKE_CASE   :List[str]						=  [files]
                   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                   __SCREAMING_SNAKE_CASE   :Dict						=  [dl_manager.iter_files(UpperCamelCase__							) for file in files]
                   return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,gen_kwargs={'''files''': files}							)]
             __SCREAMING_SNAKE_CASE   :List[Any]						=  []
             for split_name, files in data_files.items():
                   if isinstance(UpperCamelCase__		,UpperCamelCase__							):
                         __SCREAMING_SNAKE_CASE   :List[str]						=  [files]
                   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [dl_manager.iter_files(UpperCamelCase__							) for file in files]
                   splits.append(datasets.SplitGenerator(name=UpperCamelCase__		,gen_kwargs={'''files''': files}							)							)
             return splits




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Optional[int]:





             """simple docstring"""






             if self.config.features is not None:
                   # more expensive cast to support nested features with keys in a different order
                   # allows str <-> int/float or str to Audio for example
                   __SCREAMING_SNAKE_CASE   :int						=  table_cast(UpperCamelCase__		,self.config.features.arrow_schema							)
             return pa_table




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__							)			->      Optional[Any]:





             """simple docstring"""






             for i, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__							)							):
                   with open(UpperCamelCase__		,'''rb'''							) as f:
                         __SCREAMING_SNAKE_CASE   :List[str]						=  pa.Table.from_pandas(pd.read_pickle(UpperCamelCase__							)							)
                         yield i, self._cast_table(UpperCamelCase__							)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase		:       Any									=     logging.get_logger(__name__)

__UpperCamelCase		:       Optional[int]									=     {
    'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		"""nllb-moe"""
						UpperCamelCase_     =		["""past_key_values"""]
						UpperCamelCase_     =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


						def __init__(       self	:				List[str]    ,		UpperCamelCase__	:				List[str]=12_8112    ,		UpperCamelCase__	:				str=1024    ,		UpperCamelCase__	:				Optional[int]=12    ,		UpperCamelCase__	:				Union[str, Any]=4096    ,		UpperCamelCase__	:				Optional[Any]=16    ,		UpperCamelCase__	:				Optional[Any]=12    ,		UpperCamelCase__	:				str=4096    ,		UpperCamelCase__	:				Dict=16    ,		UpperCamelCase__	:				Any=0.05    ,		UpperCamelCase__	:				Any=0.05    ,		UpperCamelCase__	:				Optional[Any]=True    ,		UpperCamelCase__	:				Optional[Any]=True    ,		UpperCamelCase__	:				List[Any]="relu"    ,		UpperCamelCase__	:				Union[str, Any]=1024    ,		UpperCamelCase__	:				Optional[int]=0.1    ,		UpperCamelCase__	:				Any=0.1    ,		UpperCamelCase__	:				Optional[Any]=0.0    ,		UpperCamelCase__	:				str=0.02    ,		UpperCamelCase__	:				Tuple=2    ,		UpperCamelCase__	:				List[str]=True    ,		UpperCamelCase__	:				Dict=False    ,		UpperCamelCase__	:				Any="float32"    ,		UpperCamelCase__	:				List[Any]=False    ,		UpperCamelCase__	:				Tuple=128    ,		UpperCamelCase__	:				Tuple=64    ,		UpperCamelCase__	:				List[str]=4    ,		UpperCamelCase__	:				Union[str, Any]=4    ,		UpperCamelCase__	:				Dict=0.001    ,		UpperCamelCase__	:				Optional[Any]=0.001    ,		UpperCamelCase__	:				Optional[Any]="all"    ,		UpperCamelCase__	:				Tuple=False    ,		UpperCamelCase__	:				List[Any]=False    ,		UpperCamelCase__	:				List[Any]=1.0    ,		UpperCamelCase__	:				str=0.2    ,		UpperCamelCase__	:				List[Any]=1    ,		UpperCamelCase__	:				List[Any]=0    ,		UpperCamelCase__	:				Optional[int]=2    ,		UpperCamelCase__	:				Tuple=False    ,		**UpperCamelCase__	:				Union[str, Any]    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							vocab_size
												SCREAMING_SNAKE_CASE							:    Dict							=							max_position_embeddings
												SCREAMING_SNAKE_CASE							:    int							=							d_model
												SCREAMING_SNAKE_CASE							:    Any							=							encoder_ffn_dim
												SCREAMING_SNAKE_CASE							:    List[Any]							=							encoder_layers
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							encoder_attention_heads
												SCREAMING_SNAKE_CASE							:    List[str]							=							decoder_ffn_dim
												SCREAMING_SNAKE_CASE							:    Dict							=							decoder_layers
												SCREAMING_SNAKE_CASE							:    List[Any]							=							decoder_attention_heads
												SCREAMING_SNAKE_CASE							:    List[str]							=							dropout
												SCREAMING_SNAKE_CASE							:    Any							=							attention_dropout
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							activation_dropout
												SCREAMING_SNAKE_CASE							:    List[Any]							=							activation_function
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							init_std
												SCREAMING_SNAKE_CASE							:    int							=							encoder_layerdrop
												SCREAMING_SNAKE_CASE							:    List[Any]							=							decoder_layerdrop
												SCREAMING_SNAKE_CASE							:    Any							=							use_cache
												SCREAMING_SNAKE_CASE							:    str							=							encoder_layers
												SCREAMING_SNAKE_CASE							:    List[Any]							=							scale_embedding  # scale factor will be sqrt(d_model) if True
												SCREAMING_SNAKE_CASE							:    List[str]							=							router_z_loss_coef
												SCREAMING_SNAKE_CASE							:    List[str]							=							router_aux_loss_coef
												SCREAMING_SNAKE_CASE							:    int							=							decoder_sparse_step
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							encoder_sparse_step
												SCREAMING_SNAKE_CASE							:    List[str]							=							num_experts
												SCREAMING_SNAKE_CASE							:    int							=							expert_capacity
												SCREAMING_SNAKE_CASE							:    Any							=							router_bias
												if router_dtype not in ["float32", "float16", "bfloat16"]:
																		raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""						)
												SCREAMING_SNAKE_CASE							:    str							=							router_dtype

												SCREAMING_SNAKE_CASE							:    List[Any]							=							router_ignore_padding_tokens
												SCREAMING_SNAKE_CASE							:    int							=							batch_prioritized_routing
												SCREAMING_SNAKE_CASE							:    str							=							second_expert_policy
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							normalize_router_prob_before_dropping
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							moe_eval_capacity_token_fraction
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							moe_token_dropout
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							output_router_logits
												super().__init__(
												    pad_token_id=UpperCamelCase__    ,		bos_token_id=UpperCamelCase__    ,		eos_token_id=UpperCamelCase__    ,		is_encoder_decoder=UpperCamelCase__    ,		decoder_start_token_id=UpperCamelCase__    ,		**UpperCamelCase__    ,		)





