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


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

__lowerCamelCase    :				Optional[int]							     =	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"gpt_neox"




   def __init__(					self    :       Any ,			_lowercase    :       str=5_04_32 ,			_lowercase    :       int=61_44 ,			_lowercase    :       Optional[int]=44 ,			_lowercase    :       Union[str, Any]=64 ,			_lowercase    :       List[str]=2_45_76 ,			_lowercase    :       Dict="gelu" ,			_lowercase    :       List[str]=0.25 ,			_lowercase    :       Optional[Any]=1_00_00 ,			_lowercase    :       Optional[Any]=0.0 ,			_lowercase    :       int=0.0 ,			_lowercase    :       List[Any]=0.1 ,			_lowercase    :       Tuple=20_48 ,			_lowercase    :       Union[str, Any]=0.02 ,			_lowercase    :       str=1E-5 ,			_lowercase    :       int=True ,			_lowercase    :       Any=0 ,			_lowercase    :       List[str]=2 ,			_lowercase    :       Optional[Any]=False ,			_lowercase    :       int=True ,			_lowercase    :       Tuple=None ,			**_lowercase    :       Any ,			):



     """simple docstring"""






     super().__init__(bos_token_id=_lowercase ,			eos_token_id=_lowercase ,			**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   vocab_size
     SCREAMING_SNAKE_CASE__    							=   max_position_embeddings
     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   rotary_pct
     SCREAMING_SNAKE_CASE__    							=   rotary_emb_base
     SCREAMING_SNAKE_CASE__    							=   attention_dropout
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout
     SCREAMING_SNAKE_CASE__    							=   classifier_dropout
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   layer_norm_eps
     SCREAMING_SNAKE_CASE__    							=   use_cache
     SCREAMING_SNAKE_CASE__    							=   tie_word_embeddings
     SCREAMING_SNAKE_CASE__    							=   use_parallel_residual
     SCREAMING_SNAKE_CASE__    							=   rope_scaling
     self._rope_scaling_validation()

     if self.hidden_size % self.num_attention_heads != 0:
       raise ValueError(
           """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""   )




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     if self.rope_scaling is None:
       return

     if not isinstance(self.rope_scaling ,			_lowercase   ) or len(self.rope_scaling   ) != 2:
       raise ValueError(
           """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
           f"""got {self.rope_scaling}"""   )
     SCREAMING_SNAKE_CASE__    							=   self.rope_scaling.get("""type""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.rope_scaling.get("""factor""" ,			_lowercase   )
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
       raise ValueError(
           f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""   )
     if rope_scaling_factor is None or not isinstance(_lowercase ,			_lowercase   ) or rope_scaling_factor <= 1.0:
       raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""   )

import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__lowerCamelCase    :				Union[str, Any]							     =	datasets.utils.logging.get_logger(__name__)







@dataclass
class __snake_case       (		datasets.BuilderConfig	):
   lowerCAmelCase_														=							1_00_00
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None







class __snake_case       (		datasets.ArrowBasedBuilder	):
   lowerCAmelCase_														=							ParquetConfig




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[str]   ):



     """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__    							=   dl_manager.download_and_extract(self.config.data_files   )
     if isinstance(_lowercase ,			(str, list, tuple)   ):
       SCREAMING_SNAKE_CASE__    							=   data_files
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,			gen_kwargs={"""files""": files}   )]
     SCREAMING_SNAKE_CASE__    							=   []
     for split_name, files in data_files.items():
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       # Infer features is they are stoed in the arrow schema
       if self.info.features is None:
         for file in itertools.chain.from_iterable(_lowercase   ):
           with open(_lowercase ,			"""rb"""   ) as f:
             SCREAMING_SNAKE_CASE__    							=   datasets.Features.from_arrow_schema(pq.read_schema(_lowercase   )   )
           break
       splits.append(datasets.SplitGenerator(name=_lowercase ,			gen_kwargs={"""files""": files}   )   )
     return splits




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       pa.Table   ):



     """simple docstring"""






     if self.info.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__    							=   table_cast(_lowercase ,			self.info.features.arrow_schema   )
     return pa_table




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
       if sorted(field.name for field in schema   ) != sorted(self.config.columns   ):
         raise ValueError(
             f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""   )
     for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase   )   ):
       with open(_lowercase ,			"""rb"""   ) as f:
         SCREAMING_SNAKE_CASE__    							=   pq.ParquetFile(_lowercase   )
         try:
           for batch_idx, record_batch in enumerate(
               parquet_file.iter_batches(batch_size=self.config.batch_size ,			columns=self.config.columns   )   ):
             SCREAMING_SNAKE_CASE__    							=   pa.Table.from_batches([record_batch]   )
             # Uncomment for debugging (will print the Arrow table size and elements)
             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
             # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
             yield f"""{file_idx}_{batch_idx}""", self._cast_table(_lowercase   )
         except ValueError as e:
           logger.error(f"""Failed to read file '{file}' with error {type(_lowercase   )}: {e}"""   )
           raise
