


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__lowerCamelCase   	=  100000000000
__lowerCamelCase   	=  10000

__lowerCamelCase	,							__lowerCamelCase   	=  os.path.split(__file__)
__lowerCamelCase   	=  os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Dict				      =     pa.Table.from_pydict({"""col""": [0] * chunk_size}			)
     a_     :  Tuple				      =     pa.concat_tables([table] * (num_examples // chunk_size)			)
     return datasets.Dataset(__UpperCamelCase	,	fingerprint="""table_100B"""			)


@dataclass
class 	a__						:
       lowerCamelCase__:  int
       lowerCamelCase__:  int
       lowerCamelCase__:  int
       lowerCamelCase__:  int

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Any				      =     np.random.default_rng(self.seed     )
            a_     :  List[str]				      =     rng.integers(low=self.low   ,				high=self.high   ,				size=self.size     ).tolist()

       def __iter__( self   :  Union[str, Any]     ):
            return iter(self._sampled_values     )

       def __len__( self   :  Optional[Any]     ):
            return self.size



@get_duration
def 			_a      (    __UpperCamelCase			):
     a_     :  int				      =     dataset[0]



@get_duration
def 			_a      (    __UpperCamelCase			):
     a_     :  Optional[int]				      =     dataset[-1]



@get_duration
def 			_a      (    __UpperCamelCase			):
     a_     :  Optional[Any]				      =     dataset[range(len(__UpperCamelCase			) // 2	,	len(__UpperCamelCase			) // 2 + 1_0_2_4			)]



@get_duration
def 			_a      (    __UpperCamelCase			):
     a_     :  Tuple				      =     dataset[RandIter(0	,	len(__UpperCamelCase			)	,	1_0_2_4	,	seed=4_2			)]



def 			_a      (    ):
     a_     :  Optional[Any]				      =     {"""num examples""": SPEED_TEST_N_EXAMPLES}
     a_     :  int				      =     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
     print("""generating dataset"""			)
     a_     :  List[str]				      =     generate_100B_dataset(num_examples=__UpperCamelCase	,	chunk_size=__UpperCamelCase			)
     print("""Functions"""			)
     for func in functions:
          print(func.__name__			)
          a_     :  Any				      =     func(__UpperCamelCase			)

     with open(__UpperCamelCase	,	"""wb"""			) as f:
          f.write(json.dumps(__UpperCamelCase			).encode("""utf-8"""			)			)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_table_100B()








import copy

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


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {}







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						"fsmt_decoder"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Tuple=0  ,lowerCamelCase_:     List[str]=0							)	->      Any:
       super().__init__()
       UpperCAmelCase_     :       Optional[Any]  		=					vocab_size
       UpperCAmelCase_     :       Union[str, Any]  		=					bos_token_id







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						"fsmt"
   A__      : int       =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(    self:     int  ,lowerCamelCase_:     Optional[Any]=["en", "de"]  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     List[str]="relu"  ,lowerCamelCase_:     Any=1024  ,lowerCamelCase_:     Optional[int]=200  ,lowerCamelCase_:     str=1024  ,lowerCamelCase_:     Any=4096  ,lowerCamelCase_:     Dict=12  ,lowerCamelCase_:     Tuple=16  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     str=4096  ,lowerCamelCase_:     Union[str, Any]=12  ,lowerCamelCase_:     Any=16  ,lowerCamelCase_:     List[str]=0.0  ,lowerCamelCase_:     str=0.0  ,lowerCamelCase_:     Optional[int]=0.1  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     Tuple=0.0_2  ,lowerCamelCase_:     str=2  ,lowerCamelCase_:     List[str]=True  ,lowerCamelCase_:     Dict=True  ,lowerCamelCase_:     Any=False  ,lowerCamelCase_:     Any=5  ,lowerCamelCase_:     Optional[int]=1.0  ,lowerCamelCase_:     List[str]=False  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     List[str]=1  ,lowerCamelCase_:     int=0  ,lowerCamelCase_:     Optional[Any]=2  ,lowerCamelCase_:     Any=2  ,**lowerCamelCase_:     Optional[Any]  ,)	->      int:
       UpperCAmelCase_     :       Dict  		=					langs
       UpperCAmelCase_     :       Tuple  		=					src_vocab_size
       UpperCAmelCase_     :       Optional[int]  		=					tgt_vocab_size
       UpperCAmelCase_     :       Optional[Any]  		=					d_model  # encoder_embed_dim and decoder_embed_dim

       UpperCAmelCase_     :       List[str]  		=					encoder_ffn_dim
       UpperCAmelCase_     :       List[Any]  		=					encoder_layers
       UpperCAmelCase_     :       List[Any]  		=					encoder_attention_heads
       UpperCAmelCase_     :       Union[str, Any]  		=					encoder_layerdrop
       UpperCAmelCase_     :       List[Any]  		=					decoder_layerdrop
       UpperCAmelCase_     :       str  		=					decoder_ffn_dim
       UpperCAmelCase_     :       Optional[int]  		=					decoder_layers
       UpperCAmelCase_     :       int  		=					decoder_attention_heads
       UpperCAmelCase_     :       List[Any]  		=					max_position_embeddings
       UpperCAmelCase_     :       Dict  		=					init_std  # Normal(0, this parameter)
       UpperCAmelCase_     :       Dict  		=					activation_function

       UpperCAmelCase_     :       List[str]  		=					DecoderConfig(vocab_size=lowerCamelCase_  ,bos_token_id=lowerCamelCase_							)
       if "decoder" in common_kwargs:
           del common_kwargs["decoder"]

       UpperCAmelCase_     :       Any  		=					scale_embedding  # scale factor will be sqrt(d_model) if True

       # 3 Types of Dropout
       UpperCAmelCase_     :       int  		=					attention_dropout
       UpperCAmelCase_     :       int  		=					activation_dropout
       UpperCAmelCase_     :       Any  		=					dropout

       UpperCAmelCase_     :       Any  		=					use_cache
       super().__init__(
           pad_token_id=lowerCamelCase_  ,bos_token_id=lowerCamelCase_  ,eos_token_id=lowerCamelCase_  ,decoder_start_token_id=lowerCamelCase_  ,is_encoder_decoder=lowerCamelCase_  ,tie_word_embeddings=lowerCamelCase_  ,forced_eos_token_id=lowerCamelCase_  ,max_length=lowerCamelCase_  ,num_beams=lowerCamelCase_  ,length_penalty=lowerCamelCase_  ,early_stopping=lowerCamelCase_  ,**lowerCamelCase_  ,)






   def       A__    (    self:     List[str]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       str  		=					copy.deepcopy(self.__dict__							)
       UpperCAmelCase_     :       int  		=					self.decoder.to_dict()
       UpperCAmelCase_     :       Dict  		=					self.__class__.model_type
       return output
