

import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_a			=       logging.get_logger(__name__)






@dataclass
class 							A_			:
     _lowercase      :					str
     _lowercase      :					str
     _lowercase      :					Optional[str]       =     None
     _lowercase      :					Optional[str]       =     None


     def   UpperCAmelCase  (							self      :		int )							->     int:
      return json.dumps(dataclasses.asdict(self )      ,  indent=2 ) + "\n"







@dataclass(frozen=snake_case__ )
class 							A_			:
     _lowercase      :					List[int]
     _lowercase      :					Optional[List[int]]       =     None
     _lowercase      :					Optional[List[int]]       =     None
     _lowercase      :					Optional[Union[int, float]]       =     None


     def   UpperCAmelCase  (							self      :		Tuple )							->     str:
      return json.dumps(dataclasses.asdict(self ) ) + "\n"







class 							A_			:


     def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		Optional[Any] )							->     Optional[int]:
      raise NotImplementedError()


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		Dict )							->     Optional[Any]:
      raise NotImplementedError()


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Dict )							->     Union[str, Any]:
      raise NotImplementedError()


     def   UpperCAmelCase  (							self      :		Dict      ,  UpperCAmelCase      :		Dict )							->     str:
      raise NotImplementedError()


     def   UpperCAmelCase  (							self      :		List[Any] )							->     Optional[Any]:
      raise NotImplementedError()


     def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		Dict )							->     Tuple:
      if len(self.get_labels() ) > 1:
       __lowerCAmelCase:       str            =				self.get_labels()[int(example.label )]
      return example


     @classmethod
     def   UpperCAmelCase  (							cls      :		str      ,  UpperCAmelCase      :		Dict      ,  UpperCAmelCase      :		Union[str, Any]=None )							->     Union[str, Any]:
      with open(UpperCAmelCase      ,  'r'      ,  encoding='utf-8-sig' ) as f:
       return list(csv.reader(UpperCAmelCase      ,  delimiter='\t'      ,  quotechar=UpperCAmelCase ) )







class 							A_			(		snake_case__ ):


     def __init__(							self      :		Union[str, Any]      ,  UpperCAmelCase      :		str=None      ,  UpperCAmelCase      :		int=None      ,  UpperCAmelCase      :		List[str]="classification"      ,  UpperCAmelCase      :		Tuple=False )							->     List[Any]:
      __lowerCAmelCase:       Optional[Any]            =				[] if labels is None else labels
      __lowerCAmelCase:       List[str]            =				[] if examples is None else examples
      __lowerCAmelCase:       Optional[Any]            =				mode
      __lowerCAmelCase:       Optional[int]            =				verbose


     def __len__(							self      :		Any )							->     str:
      return len(self.examples )


     def __getitem__(							self      :		Any      ,  UpperCAmelCase      :		str )							->     int:
      if isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
       return SingleSentenceClassificationProcessor(labels=self.labels      ,  examples=self.examples[idx] )
      return self.examples[idx]


     @classmethod
     def   UpperCAmelCase  (							cls      :		Any      ,  UpperCAmelCase      :		List[Any]      ,  UpperCAmelCase      :		List[Any]=""      ,  UpperCAmelCase      :		List[Any]=0      ,  UpperCAmelCase      :		List[str]=1      ,  UpperCAmelCase      :		Any=None      ,  UpperCAmelCase      :		Optional[Any]=False      ,  **UpperCAmelCase      :		List[Any] )							->     Dict:
      __lowerCAmelCase:       Dict            =				cls(**UpperCAmelCase )
      processor.add_examples_from_csv(
          UpperCAmelCase      ,  split_name=UpperCAmelCase      ,  column_label=UpperCAmelCase      ,  column_text=UpperCAmelCase      ,  column_id=UpperCAmelCase      ,  skip_first_row=UpperCAmelCase      ,  overwrite_labels=UpperCAmelCase      ,  overwrite_examples=UpperCAmelCase      ,  )
      return processor


     @classmethod
     def   UpperCAmelCase  (							cls      :		List[Any]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		List[str]=None      ,  **UpperCAmelCase      :		Dict )							->     List[str]:
      __lowerCAmelCase:       List[str]            =				cls(**UpperCAmelCase )
      processor.add_examples(UpperCAmelCase      ,  labels=UpperCAmelCase )
      return processor


     def   UpperCAmelCase  (							self      :		Any      ,  UpperCAmelCase      :		List[Any]      ,  UpperCAmelCase      :		Any=""      ,  UpperCAmelCase      :		List[Any]=0      ,  UpperCAmelCase      :		Dict=1      ,  UpperCAmelCase      :		Optional[Any]=None      ,  UpperCAmelCase      :		Dict=False      ,  UpperCAmelCase      :		List[str]=False      ,  UpperCAmelCase      :		Any=False      ,  )							->     Any:
      __lowerCAmelCase:       Union[str, Any]            =				self._read_tsv(UpperCAmelCase )
      if skip_first_row:
       __lowerCAmelCase:       Optional[Any]            =				lines[1:]
      __lowerCAmelCase:       Union[str, Any]            =				[]
      __lowerCAmelCase:       int            =				[]
      __lowerCAmelCase:       List[str]            =				[]
      for i, line in enumerate(UpperCAmelCase ):
       texts.append(line[column_text] )
       labels.append(line[column_label] )
       if column_id is not None:
        ids.append(line[column_id] )
       else:
        __lowerCAmelCase:       Any            =				F'''{split_name}-{i}''' if split_name else str(UpperCAmelCase )
        ids.append(UpperCAmelCase )

      return self.add_examples(
          UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  overwrite_labels=UpperCAmelCase      ,  overwrite_examples=UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Dict      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		Optional[Any]=None      ,  UpperCAmelCase      :		Optional[int]=None      ,  UpperCAmelCase      :		Union[str, Any]=False      ,  UpperCAmelCase      :		Optional[int]=False )							->     Union[str, Any]:
      if labels is not None and len(UpperCAmelCase ) != len(UpperCAmelCase ):
       raise ValueError(
           F'''Text and labels have mismatched lengths {len(UpperCAmelCase )} and {len(UpperCAmelCase )}''' )
      if ids is not None and len(UpperCAmelCase ) != len(UpperCAmelCase ):
       raise ValueError(F'''Text and ids have mismatched lengths {len(UpperCAmelCase )} and {len(UpperCAmelCase )}''' )
      if ids is None:
       __lowerCAmelCase:       str            =				[None] * len(UpperCAmelCase )
      if labels is None:
       __lowerCAmelCase:       Optional[int]            =				[None] * len(UpperCAmelCase )
      __lowerCAmelCase:       int            =				[]
      __lowerCAmelCase:       Optional[Any]            =				set()
      for text_or_text_and_label, label, guid in zip(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase ):
       if isinstance(UpperCAmelCase      ,  (tuple, list) ) and label is None:
        __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				text_or_text_and_label
       else:
        __lowerCAmelCase:       List[str]            =				text_or_text_and_label
       added_labels.add(UpperCAmelCase )
       examples.append(InputExample(guid=UpperCAmelCase      ,  text_a=UpperCAmelCase      ,  text_b=UpperCAmelCase      ,  label=UpperCAmelCase ) )

      # Update examples
      if overwrite_examples:
       __lowerCAmelCase:       Optional[int]            =				examples
      else:
       self.examples.extend(UpperCAmelCase )

      # Update labels
      if overwrite_labels:
       __lowerCAmelCase:       int            =				list(UpperCAmelCase )
      else:
       __lowerCAmelCase:       str            =				list(set(self.labels ).union(UpperCAmelCase ) )

      return self.examples


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		List[Any]=None      ,  UpperCAmelCase      :		str=False      ,  UpperCAmelCase      :		List[Any]=0      ,  UpperCAmelCase      :		List[Any]=True      ,  UpperCAmelCase      :		Optional[int]=None      ,  )							->     List[str]:
      if max_length is None:
       __lowerCAmelCase:       Optional[int]            =				tokenizer.max_len

      __lowerCAmelCase:       List[Any]            =				{label: i for i, label in enumerate(self.labels )}

      __lowerCAmelCase:       Dict            =				[]
      for ex_index, example in enumerate(self.examples ):
       if ex_index % 1_0_0_0_0 == 0:
        logger.info(F'''Tokenizing example {ex_index}''' )

       __lowerCAmelCase:       Tuple            =				tokenizer.encode(
           example.text_a      ,  add_special_tokens=UpperCAmelCase      ,  max_length=min(UpperCAmelCase      ,  tokenizer.max_len )      ,  )
       all_input_ids.append(UpperCAmelCase )

      __lowerCAmelCase:       Dict            =				max(len(UpperCAmelCase ) for input_ids in all_input_ids )

      __lowerCAmelCase:       Optional[int]            =				[]
      for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase      ,  self.examples ) ):
       if ex_index % 1_0_0_0_0 == 0:
        logger.info(F'''Writing example {ex_index}/{len(self.examples )}''' )
       # The mask has 1 for real tokens and 0 for padding tokens. Only real
       # tokens are attended to.
       __lowerCAmelCase:       Any            =				[1 if mask_padding_with_zero else 0] * len(UpperCAmelCase )

       # Zero-pad up to the sequence length.
       __lowerCAmelCase:       Union[str, Any]            =				batch_length - len(UpperCAmelCase )
       if pad_on_left:
        __lowerCAmelCase:       Tuple            =				([pad_token] * padding_length) + input_ids
        __lowerCAmelCase:       Tuple            =				([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
       else:
        __lowerCAmelCase:       str            =				input_ids + ([pad_token] * padding_length)
        __lowerCAmelCase:       Dict            =				attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

       if len(UpperCAmelCase ) != batch_length:
        raise ValueError(F'''Error with input length {len(UpperCAmelCase )} vs {batch_length}''' )
       if len(UpperCAmelCase ) != batch_length:
        raise ValueError(F'''Error with input length {len(UpperCAmelCase )} vs {batch_length}''' )

       if self.mode == "classification":
        __lowerCAmelCase:       int            =				label_map[example.label]
       elif self.mode == "regression":
        __lowerCAmelCase:       Dict            =				float(example.label )
       else:
        raise ValueError(self.mode )

       if ex_index < 5 and self.verbose:
        logger.info('*** Example ***' )
        logger.info(F'''guid: {example.guid}''' )
        logger.info(F'''input_ids: {' '.join([str(UpperCAmelCase ) for x in input_ids] )}''' )
        logger.info(F'''attention_mask: {' '.join([str(UpperCAmelCase ) for x in attention_mask] )}''' )
        logger.info(F'''label: {example.label} (id = {label})''' )

       features.append(InputFeatures(input_ids=UpperCAmelCase      ,  attention_mask=UpperCAmelCase      ,  label=UpperCAmelCase ) )

      if return_tensors is None:
       return features
      elif return_tensors == "tf":
       if not is_tf_available():
        raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported' )
       import tensorflow as tf

       def gen():
        for ex in features:
         yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

       __lowerCAmelCase:       List[Any]            =				tf.data.Dataset.from_generator(
           UpperCAmelCase      ,  ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)      ,  ({'input_ids': tf.TensorShape([None] ), 'attention_mask': tf.TensorShape([None] )}, tf.TensorShape([] ))      ,  )
       return dataset
      elif return_tensors == "pt":
       if not is_torch_available():
        raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported' )
       import torch
       from torch.utils.data import TensorDataset

       __lowerCAmelCase:       Tuple            =				torch.tensor([f.input_ids for f in features]      ,  dtype=torch.long )
       __lowerCAmelCase:       Dict            =				torch.tensor([f.attention_mask for f in features]      ,  dtype=torch.long )
       if self.mode == "classification":
        __lowerCAmelCase:       Optional[int]            =				torch.tensor([f.label for f in features]      ,  dtype=torch.long )
       elif self.mode == "regression":
        __lowerCAmelCase:       Optional[Any]            =				torch.tensor([f.label for f in features]      ,  dtype=torch.float )

       __lowerCAmelCase:       Optional[int]            =				TensorDataset(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
       return dataset
      else:
       raise ValueError('return_tensors should be one of \'tf\' or \'pt\'' )








import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_a			=       1_0_0_0_0_0_0_0_0_0_0_0
_a			=       1_0_0_0_0

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







def _a			(	SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int		)   ->   datasets.Dataset:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				pa.Table.from_pydict({'col': [0] * chunk_size}		)
 __lowerCAmelCase:       Dict            =				pa.concat_tables([table] * (num_examples // chunk_size)		)
 return datasets.Dataset(SCREAMING_SNAKE_CASE   , fingerprint='table_100B'		)






@dataclass
class 							A_			:
     _lowercase      :					int
     _lowercase      :					int
     _lowercase      :					int
     _lowercase      :					int


     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Dict:
      __lowerCAmelCase:       Dict            =				np.random.default_rng(self.seed )
      __lowerCAmelCase:       List[Any]            =				rng.integers(low=self.low      ,  high=self.high      ,  size=self.size ).tolist()


     def __iter__(							self      :		Any )							->     Optional[int]:
      return iter(self._sampled_values )


     def __len__(							self      :		Union[str, Any] )							->     Dict:
      return self.size







@get_duration
def _a			(	SCREAMING_SNAKE_CASE		:      datasets.Dataset		)   ->   List[str]:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				dataset[0]







@get_duration
def _a			(	SCREAMING_SNAKE_CASE		:      datasets.Dataset		)   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Dict            =				dataset[-1]







@get_duration
def _a			(	SCREAMING_SNAKE_CASE		:      datasets.Dataset		)   ->   Any:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				dataset[range(len(SCREAMING_SNAKE_CASE		) // 2   , len(SCREAMING_SNAKE_CASE		) // 2 + 10_24		)]







@get_duration
def _a			(	SCREAMING_SNAKE_CASE		:      datasets.Dataset		)   ->   Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:       int            =				dataset[RandIter(0   , len(SCREAMING_SNAKE_CASE		)   , 10_24   , seed=42		)]







def _a			(	)   ->   Tuple:

 """simple docstring"""






 __lowerCAmelCase:       str            =				{'num examples': SPEED_TEST_N_EXAMPLES}
 __lowerCAmelCase:       Tuple            =				(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
 print('generating dataset'		)
 __lowerCAmelCase:       str            =				generate_100B_dataset(num_examples=SCREAMING_SNAKE_CASE   , chunk_size=SCREAMING_SNAKE_CASE		)
 print('Functions'		)
 for func in functions:
  print(func.__name__		)
  __lowerCAmelCase:       Dict            =				func(SCREAMING_SNAKE_CASE		)

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


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





