'''simple docstring'''




import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCamelCase				:		List[Any]       			=		10_00_00_00_00_00
_UpperCamelCase				:		Optional[int]       			=		1_00_00

_UpperCamelCase			,      _UpperCamelCase				:		List[Any]       			=		os.path.split(__file__)
_UpperCamelCase				:		Optional[int]       			=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



def 						__snake_case (       lowerCAmelCase       :     int      ,  lowerCAmelCase       :     int			):
 __UpperCAmelCase         =					pa.Table.from_pydict({'col': [0] * chunk_size}			)
 __UpperCAmelCase         =					pa.concat_tables([table] * (num_examples // chunk_size)			)
 return datasets.Dataset(lowerCAmelCase      ,  fingerprint='table_100B'			)

@dataclass
class   _lowercase:



   """simple docstring"""





   __lowerCamelCase    =  42
   __lowerCamelCase    =  42
   __lowerCamelCase    =  42
   __lowerCamelCase    =  42





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					np.random.default_rng(self.seed	)
    __UpperCAmelCase         =					rng.integers(low=self.low			,high=self.high			,size=self.size	).tolist()





   def __iter__(							self:    Any	):
    return iter(self._sampled_values	)





   def __len__(							self:    Dict	):
    return self.size




@get_duration
def 						__snake_case (       lowerCAmelCase       :     datasets.Dataset			):
 __UpperCAmelCase         =					dataset[0]



@get_duration
def 						__snake_case (       lowerCAmelCase       :     datasets.Dataset			):
 __UpperCAmelCase         =					dataset[-1]



@get_duration
def 						__snake_case (       lowerCAmelCase       :     datasets.Dataset			):
 __UpperCAmelCase         =					dataset[range(len(lowerCAmelCase			) // 2      ,  len(lowerCAmelCase			) // 2 + 1024			)]



@get_duration
def 						__snake_case (       lowerCAmelCase       :     datasets.Dataset			):
 __UpperCAmelCase         =					dataset[RandIter(0      ,  len(lowerCAmelCase			)      ,  1024      ,  seed=42			)]



def 						__snake_case (       ):
 __UpperCAmelCase         =					{'num examples': SPEED_TEST_N_EXAMPLES}
 __UpperCAmelCase         =					(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
 print('generating dataset'			)
 __UpperCAmelCase         =					generate_100B_dataset(num_examples=lowerCAmelCase      ,  chunk_size=lowerCAmelCase			)
 print('Functions'			)
 for func in functions:
  print(func.__name__			)
  __UpperCAmelCase         =					func(lowerCAmelCase			)

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


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




'''simple docstring'''




from typing import TYPE_CHECKING

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


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



