

'''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				: int         =  1_0_0_0_0_0_0_0_0_0_0_0
UpperCAmelCase				: Dict         =  1_0_0_0_0

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







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







@dataclass
class 		lowerCAmelCase__     :



 """simple docstring"""



 lowerCAmelCase__						      =   42
 lowerCAmelCase__						      =   42
 lowerCAmelCase__						      =   42
 lowerCAmelCase__						      =   42
 def    UpperCAmelCase__	(	self      : str    )						->				Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   np.random.default_rng(self.seed    )
      __SCREAMING_SNAKE_CASE						     =   rng.integers(low=self.low      ,							high=self.high      ,							size=self.size    ).tolist()
 def __iter__(	self      : List[Any]    )						->				int:


      """simple docstring"""




      return iter(self._sampled_values    )


 def __len__(	self      : Any    )						->				Any:


      """simple docstring"""




      return self.size







@get_duration
def      a__ (						a__		):
     __SCREAMING_SNAKE_CASE						     =   dataset[0]







@get_duration
def      a__ (						a__		):
     __SCREAMING_SNAKE_CASE						     =   dataset[-1]







@get_duration
def      a__ (						a__		):
     __SCREAMING_SNAKE_CASE						     =   dataset[range(len(a__		) // 2   ,					len(a__		) // 2 + 10_24		)]







@get_duration
def      a__ (						a__		):
     __SCREAMING_SNAKE_CASE						     =   dataset[RandIter(0   ,					len(a__		)   ,					10_24   ,					seed=42		)]







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

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


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









'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "mt5"
 lowerCAmelCase__						      =   ["past_key_values"]
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=250_112      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=512      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=64      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1_024      ,							__SCREAMING_SNAKE_CASE      : List[str]=8      ,							__SCREAMING_SNAKE_CASE      : Any=None      ,							__SCREAMING_SNAKE_CASE      : List[str]=6      ,							__SCREAMING_SNAKE_CASE      : Any=32      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=128      ,							__SCREAMING_SNAKE_CASE      : List[str]=0.1      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=1E-6      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=1.0      ,							__SCREAMING_SNAKE_CASE      : Tuple="gated-gelu"      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple="T5Tokenizer"      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=False      ,							__SCREAMING_SNAKE_CASE      : Tuple=0      ,							__SCREAMING_SNAKE_CASE      : str=1      ,							__SCREAMING_SNAKE_CASE      : Dict=0      ,							**__SCREAMING_SNAKE_CASE      : str      ,							)						->				List[Any]:


      """simple docstring"""




      super().__init__(
          is_encoder_decoder=__SCREAMING_SNAKE_CASE      ,							tokenizer_class=__SCREAMING_SNAKE_CASE      ,							tie_word_embeddings=__SCREAMING_SNAKE_CASE      ,							pad_token_id=__SCREAMING_SNAKE_CASE      ,							eos_token_id=__SCREAMING_SNAKE_CASE      ,							decoder_start_token_id=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE      ,							)
      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   d_model
      __SCREAMING_SNAKE_CASE						     =   d_kv
      __SCREAMING_SNAKE_CASE						     =   d_ff
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   relative_attention_num_buckets
      __SCREAMING_SNAKE_CASE						     =   relative_attention_max_distance
      __SCREAMING_SNAKE_CASE						     =   dropout_rate
      __SCREAMING_SNAKE_CASE						     =   layer_norm_epsilon
      __SCREAMING_SNAKE_CASE						     =   initializer_factor
      __SCREAMING_SNAKE_CASE						     =   feed_forward_proj
      __SCREAMING_SNAKE_CASE						     =   use_cache

      __SCREAMING_SNAKE_CASE						     =   self.feed_forward_proj.split("""-"""    )
      __SCREAMING_SNAKE_CASE						     =   act_info[-1]
      __SCREAMING_SNAKE_CASE						     =   act_info[0] == """gated"""

      if len(__SCREAMING_SNAKE_CASE    ) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE    ) > 2:
           raise ValueError(
               f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
               """'gated-gelu' or 'relu'"""    )

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           __SCREAMING_SNAKE_CASE						     =   """gelu_new"""
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.d_model
 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				int:


      """simple docstring"""




      return self.num_heads


 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.num_layers







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
          """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
      }
      if self.use_past:
           __SCREAMING_SNAKE_CASE						     =   """past_encoder_sequence + sequence"""
           __SCREAMING_SNAKE_CASE						     =   {0: """batch"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
           self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE      ,							direction="""inputs"""    )

      return common_inputs
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				int:


      """simple docstring"""




      return 13


 @property
 def    UpperCAmelCase__	(	self      : Tuple    )						->				float:


      """simple docstring"""




      return 5E-4






