

import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
  import sqlitea

  import sqlalchemy


_a			=       datasets.utils.logging.get_logger(__name__)






@dataclass
class 							A_			(		datasets.BuilderConfig ):
     _lowercase      :					Union[str, "sqlalchemy.sql.Selectable"]       =     None
     _lowercase      :					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]       =     None
     _lowercase      :					Optional[Union[str, List[str]]]       =     None
     _lowercase      :					bool       =     True
     _lowercase      :					Optional[Union[List, Tuple, Dict]]       =     None
     _lowercase      :					Optional[Union[List, Dict]]       =     None
     _lowercase      :					Optional[List[str]]       =     None
     _lowercase      :					Optional[int]       =     1_0_0_0_0
     _lowercase      :					Optional[datasets.Features]       =     None


     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     int:
      if self.sql is None:
       raise ValueError('sql must be specified' )
      if self.con is None:
       raise ValueError('con must be specified' )


     def   UpperCAmelCase  (							self      :		Union[str, Any]      ,  UpperCAmelCase      :		dict      ,  UpperCAmelCase      :		Optional[datasets.Features] = None      ,  )							->     str:
      __lowerCAmelCase:       Optional[Any]            =				config_kwargs.copy()
      # We need to stringify the Selectable object to make its hash deterministic

      # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
      __lowerCAmelCase:       str            =				config_kwargs['sql']
      if not isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
       if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
        import sqlalchemy

        if isinstance(UpperCAmelCase      ,  sqlalchemy.sql.Selectable ):
         __lowerCAmelCase:       List[str]            =				sqlalchemy.create_engine(config_kwargs['con'].split('://' )[0] + '://' )
         __lowerCAmelCase:       Optional[Any]            =				str(sql.compile(dialect=engine.dialect ) )
         __lowerCAmelCase:       List[Any]            =				sql_str
        else:
         raise TypeError(
             F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(UpperCAmelCase )}: {sql}''' )
       else:
        raise TypeError(
            F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(UpperCAmelCase )}: {sql}''' )
      __lowerCAmelCase:       Optional[int]            =				config_kwargs['con']
      if not isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
       __lowerCAmelCase:       int            =				id(UpperCAmelCase )
       logger.info(
           F'''SQL connection \'con\' of type {type(UpperCAmelCase )} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.''' )

      return super().create_config_id(UpperCAmelCase      ,  custom_features=UpperCAmelCase )


     @property
     def   UpperCAmelCase  (							self      :		Tuple )							->     str:
      __lowerCAmelCase:       List[str]            =				{
          'index_col': self.index_col,
          'columns': self.columns,
          'params': self.params,
          'coerce_float': self.coerce_float,
          'parse_dates': self.parse_dates,
      }
      return pd_read_sql_kwargs







class 							A_			(		datasets.ArrowBasedBuilder ):
     _lowercase      :					Optional[int]       =     SqlConfig


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Optional[Any]:
      return datasets.DatasetInfo(features=self.config.features )


     def   UpperCAmelCase  (							self      :		Tuple      ,  UpperCAmelCase      :		List[Any] )							->     Dict:
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,  gen_kwargs={} )]


     def   UpperCAmelCase  (							self      :		List[Any]      ,  UpperCAmelCase      :		pa.Table )							->     pa.Table:
      if self.config.features is not None:
       __lowerCAmelCase:       List[str]            =				self.config.features.arrow_schema
       if all(not require_storage_cast(UpperCAmelCase ) for feature in self.config.features.values() ):
        # cheaper cast
        __lowerCAmelCase:       Optional[int]            =				pa.Table.from_arrays([pa_table[field.name] for field in schema]      ,  schema=UpperCAmelCase )
       else:
        # more expensive cast; allows str <-> int/float or str to Audio for example
        __lowerCAmelCase:       str            =				table_cast(UpperCAmelCase      ,  UpperCAmelCase )
      return pa_table


     def   UpperCAmelCase  (							self      :		Any )							->     Optional[int]:
      __lowerCAmelCase:       Union[str, Any]            =				self.config.chunksize
      __lowerCAmelCase:       List[Any]            =				pd.read_sql(
          self.config.sql      ,  self.config.con      ,  chunksize=UpperCAmelCase      ,  **self.config.pd_read_sql_kwargs )
      __lowerCAmelCase:       Union[str, Any]            =				[sql_reader] if chunksize is None else sql_reader
      for chunk_idx, df in enumerate(UpperCAmelCase ):
       __lowerCAmelCase:       int            =				pa.Table.from_pandas(UpperCAmelCase )
       yield chunk_idx, self._cast_table(UpperCAmelCase )








import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_a			=       getLogger(__name__)


_a			=       '''cuda''' if torch.cuda.is_available() else '''cpu'''







def _a			(	SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      int = 8   , SCREAMING_SNAKE_CASE		:      str = DEFAULT_DEVICE   , SCREAMING_SNAKE_CASE		:      Union[str, Any]=False   , SCREAMING_SNAKE_CASE		:      Tuple="summarization"   , SCREAMING_SNAKE_CASE		:      int=None   , **SCREAMING_SNAKE_CASE		:      Optional[Any]   , )   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				Path(SCREAMING_SNAKE_CASE		).open('w'   , encoding='utf-8'		)
 __lowerCAmelCase:       Tuple            =				str(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE		).to(SCREAMING_SNAKE_CASE		)
 if fpaa:
  __lowerCAmelCase:       List[Any]            =				model.half()

 __lowerCAmelCase:       Dict            =				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE		)
 logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''		)  # if this is wrong, check config.model_type.

 __lowerCAmelCase:       Any            =				time.time()
 # update config with task specific params
 use_task_specific_params(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 if prefix is None:
  __lowerCAmelCase:       Optional[Any]            =				prefix or getattr(model.config   , 'prefix'   , ''		) or ''
 for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)		)		):
  __lowerCAmelCase:       str            =				[prefix + text for text in examples_chunk]
  __lowerCAmelCase:       Optional[Any]            =				tokenizer(SCREAMING_SNAKE_CASE   , return_tensors='pt'   , truncation=SCREAMING_SNAKE_CASE   , padding='longest'		).to(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       List[str]            =				model.generate(
      input_ids=batch.input_ids   , attention_mask=batch.attention_mask   , **SCREAMING_SNAKE_CASE   , )
  __lowerCAmelCase:       Dict            =				tokenizer.batch_decode(SCREAMING_SNAKE_CASE   , skip_special_tokens=SCREAMING_SNAKE_CASE   , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE		)
  for hypothesis in dec:
   fout.write(hypothesis + '\n'		)
   fout.flush()
 fout.close()
 __lowerCAmelCase:       str            =				int(time.time() - start_time		)  # seconds
 __lowerCAmelCase:       Tuple            =				len(SCREAMING_SNAKE_CASE		)
 return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   , 4		)}







def _a			(	)   ->   Dict:

 """simple docstring"""






 return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'		)







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple=True		)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       str            =				argparse.ArgumentParser()
 parser.add_argument('model_name'   , type=SCREAMING_SNAKE_CASE   , help='like facebook/bart-large-cnn,t5-base, etc.'		)
 parser.add_argument('input_path'   , type=SCREAMING_SNAKE_CASE   , help='like cnn_dm/test.source'		)
 parser.add_argument('save_path'   , type=SCREAMING_SNAKE_CASE   , help='where to save summaries'		)
 parser.add_argument('--reference_path'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , help='like cnn_dm/test.target'		)
 parser.add_argument('--score_path'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default='metrics.json'   , help='where to save metrics'		)
 parser.add_argument('--device'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default=SCREAMING_SNAKE_CASE   , help='cuda, cuda:1, cpu etc.'		)
 parser.add_argument(
     '--prefix'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default=SCREAMING_SNAKE_CASE   , help='will be added to the begininng of src examples'		)
 parser.add_argument('--task'   , type=SCREAMING_SNAKE_CASE   , default='summarization'   , help='used for task_specific_params + metrics'		)
 parser.add_argument('--bs'   , type=SCREAMING_SNAKE_CASE   , default=8   , required=SCREAMING_SNAKE_CASE   , help='batch size'		)
 parser.add_argument(
     '--n_obs'   , type=SCREAMING_SNAKE_CASE   , default=-1   , required=SCREAMING_SNAKE_CASE   , help='How many observations. Defaults to all.'		)
 parser.add_argument('--fp16'   , action='store_true'		)
 parser.add_argument('--dump-args'   , action='store_true'   , help='print the custom hparams with the results'		)
 parser.add_argument(
     '--info'   , nargs='?'   , type=SCREAMING_SNAKE_CASE   , const=datetime_now()   , help=(
         'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
         ' lang=en-ru. If no value is passed, the current datetime string will be used.'
     )   , )
 # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
 __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				parser.parse_known_args()
 __lowerCAmelCase:       Union[str, Any]            =				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE		)
 if parsed_args and verbose:
  print(f'''parsed the following generate kwargs: {parsed_args}'''		)
 with open(args.input_path		) as f:
  __lowerCAmelCase:       Optional[int]            =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
 if args.n_obs > 0:
  __lowerCAmelCase:       Tuple            =				examples[: args.n_obs]
 Path(args.save_path		).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE		)
 if args.reference_path is None and Path(args.score_path		).exists():
  warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''		)
 __lowerCAmelCase:       Tuple            =				generate_summaries_or_translations(
     SCREAMING_SNAKE_CASE   , args.save_path   , args.model_name   , batch_size=args.bs   , device=args.device   , fpaa=args.fpaa   , task=args.task   , prefix=args.prefix   , **SCREAMING_SNAKE_CASE   , )

 if args.reference_path is None:
  return {}

 # Compute scores
 __lowerCAmelCase:       Dict            =				calculate_bleu if 'translation' in args.task else calculate_rouge
 __lowerCAmelCase:       Optional[Any]            =				[x.rstrip() for x in open(args.save_path		).readlines()]
 __lowerCAmelCase:       Union[str, Any]            =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(SCREAMING_SNAKE_CASE		)]
 __lowerCAmelCase:       dict            =				score_fn(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 scores.update(SCREAMING_SNAKE_CASE		)

 if args.dump_args:
  scores.update(SCREAMING_SNAKE_CASE		)
 if args.info:
  __lowerCAmelCase:       Any            =				args.info

 if verbose:
  print(SCREAMING_SNAKE_CASE		)

 if args.score_path is not None:
  json.dump(SCREAMING_SNAKE_CASE   , open(args.score_path   , 'w'		)		)

 return scores


if __name__ == "__main__":
  # Usage for MT:
  # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
  run_generate(verbose=True)





