

"""simple docstring"""






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


_lowerCAmelCase						=		getLogger(__name__)


_lowerCAmelCase						=		"""cuda""" if torch.cuda.is_available() else """cpu"""

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase = 8 ,   _lowerCamelCase = DEFAULT_DEVICE ,   _lowerCamelCase=False ,   _lowerCamelCase="summarization" ,   _lowerCamelCase=None ,   **_lowerCamelCase ,   ):

    '''simple docstring'''



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

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

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

def 		lowerCamelCase__		(					):

    '''simple docstring'''



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

def 		lowerCamelCase__		(					_lowerCamelCase=True   ):

    '''simple docstring'''



    _lowerCAmelCase       :   str  =    argparse.ArgumentParser()
    parser.add_argument('model_name' ,   type=_lowerCamelCase ,   help='like facebook/bart-large-cnn,t5-base, etc.'   )
    parser.add_argument('input_path' ,   type=_lowerCamelCase ,   help='like cnn_dm/test.source'   )
    parser.add_argument('save_path' ,   type=_lowerCamelCase ,   help='where to save summaries'   )
    parser.add_argument('--reference_path' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   help='like cnn_dm/test.target'   )
    parser.add_argument('--score_path' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   default='metrics.json' ,   help='where to save metrics'   )
    parser.add_argument('--device' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   default=_lowerCamelCase ,   help='cuda, cuda:1, cpu etc.'   )
    parser.add_argument(
        '--prefix' ,   type=_lowerCamelCase ,   required=_lowerCamelCase ,   default=_lowerCamelCase ,   help='will be added to the begininng of src examples'   )
    parser.add_argument('--task' ,   type=_lowerCamelCase ,   default='summarization' ,   help='used for task_specific_params + metrics'   )
    parser.add_argument('--bs' ,   type=_lowerCamelCase ,   default=8 ,   required=_lowerCamelCase ,   help='batch size'   )
    parser.add_argument(
        '--n_obs' ,   type=_lowerCamelCase ,   default=-1 ,   required=_lowerCamelCase ,   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=_lowerCamelCase ,   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       :   Dict  =    parser.parse_known_args()
    _lowerCAmelCase       :   Union[str, Any]  =    parse_numeric_n_bool_cl_kwargs(_lowerCamelCase   )
    if parsed_args and verbose:
        print(f"""parsed the following generate kwargs: {parsed_args}"""   )
    _lowerCAmelCase       :   Tuple  =    [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path   ).readlines()]
    if args.n_obs > 0:
        _lowerCAmelCase       :   List[Any]  =    examples[: args.n_obs]
    Path(args.save_path   ).parent.mkdir(exist_ok=_lowerCamelCase   )

    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."""   )

    if args.device == "cpu" and args.fpaa:
        # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
        raise ValueError('Can\'t mix --fp16 and --device cpu'   )

    _lowerCAmelCase       :   List[str]  =    generate_summaries_or_translations(
        _lowerCamelCase ,   args.save_path ,   args.model_name ,   batch_size=args.bs ,   device=args.device ,   fpaa=args.fpaa ,   task=args.task ,   prefix=args.prefix ,   **_lowerCamelCase ,   )

    if args.reference_path is None:
        return {}

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

    if args.dump_args:
        scores.update(_lowerCamelCase   )
    if args.info:
        _lowerCAmelCase       :   List[str]  =    args.info

    if verbose:
        print(_lowerCamelCase   )

    if args.score_path is not None:
        json.dump(_lowerCamelCase ,   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)








"""simple docstring"""






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import jax
 import jaxlib

_lowerCAmelCase						=		get_logger()

_lowerCAmelCase						=		None







class 	__UpperCamelCase    (    TensorFormatter[Mapping, "jax.Array", Mapping]						):

  def __init__(		self    ,_A=None    ,_A=None    ,**_A      ):
      '''simple docstring'''

      super().__init__(features=_A      )
      import jax
      from jaxlib.xla_client import Device

      if isinstance(_A    ,_A      ):
          raise ValueError(
              F"""Expected {device} to be a `str` not {type(_A      )}, as `jaxlib.xla_extension.Device` """
              'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
              'the device with `str()` to get its string identifier that will be internally mapped '
              'to the actual `jaxlib.xla_extension.Device`.'      )
      _lowerCAmelCase       :   int  =    device if isinstance(_A    ,_A      ) else str(jax.devices()[0]      )
      # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
      # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
          _lowerCAmelCase       :   Any  =    self._map_devices_to_str()
      if self.device not in list(DEVICE_MAPPING.keys()      ):
          logger.warning(
              F"""Device with string identifier {self.device} not listed among the available """
              F"""devices: {list(DEVICE_MAPPING.keys()      )}, so falling back to the default """
              F"""device: {str(jax.devices()[0]      )}."""      )
          _lowerCAmelCase       :   List[str]  =    str(jax.devices()[0]      )
      _lowerCAmelCase       :   int  =    jnp_array_kwargs

  @staticmethod
  def __lowerCamelCase		(		):
      '''simple docstring'''

      import jax

      return {str(_A      ): device for device in jax.devices()}

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      import jax
      import jax.numpy as jnp

      if isinstance(_A    ,_A      ) and column:
          if all(
              isinstance(_A    ,jax.Array      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
              return jnp.stack(_A    ,axis=0      )
      return column

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      import jax
      import jax.numpy as jnp

      if isinstance(_A    ,(str, bytes, type(_A      ))      ):
          return value
      elif isinstance(_A    ,(np.character, np.ndarray)      ) and np.issubdtype(value.dtype    ,np.character      ):
          return value.tolist()

      _lowerCAmelCase       :   Optional[Any]  =    {}

      if isinstance(_A    ,(np.number, np.ndarray)      ) and np.issubdtype(value.dtype    ,np.integer      ):
          # the default int precision depends on the jax config
          # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
          if jax.config.jax_enable_xaa:
              _lowerCAmelCase       :   List[str]  =    {'dtype': jnp.intaa}
          else:
              _lowerCAmelCase       :   Tuple  =    {'dtype': jnp.intaa}
      elif isinstance(_A    ,(np.number, np.ndarray)      ) and np.issubdtype(value.dtype    ,np.floating      ):
          _lowerCAmelCase       :   Any  =    {'dtype': jnp.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(_A    ,PIL.Image.Image      ):
              _lowerCAmelCase       :   int  =    np.asarray(_A      )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
          _lowerCAmelCase       :   Optional[Any]  =    self._map_devices_to_str()

      with jax.default_device(DEVICE_MAPPING[self.device]      ):
          # calling jnp.array on a np.ndarray does copy the data
          # see https://github.com/google/jax/issues/4486
          return jnp.array(_A    ,**{**default_dtype, **self.jnp_array_kwargs}      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      import jax

      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
          import torch

          if isinstance(_A    ,torch.Tensor      ):
              return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
      if hasattr(_A    ,'__array__'      ) and not isinstance(_A    ,jax.Array      ):
          _lowerCAmelCase       :   Optional[Any]  =    data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_A    ,np.ndarray      ):
          if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
              return self._consolidate([self.recursive_tensorize(_A      ) for substruct in data_struct]      )
      elif isinstance(_A    ,(list, tuple)      ):
          return self._consolidate([self.recursive_tensorize(_A      ) for substruct in data_struct]      )
      return self._tensorize(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return map_nested(self._recursive_tensorize    ,_A    ,map_list=_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.numpy_arrow_extractor().extract_row(_A      )
      _lowerCAmelCase       :   int  =    self.python_features_decoder.decode_row(_A      )
      return self.recursive_tensorize(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.numpy_arrow_extractor().extract_column(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.python_features_decoder.decode_column(_A    ,pa_table.column_names[0]      )
      _lowerCAmelCase       :   Optional[Any]  =    self.recursive_tensorize(_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self._consolidate(_A      )
      return column







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.numpy_arrow_extractor().extract_batch(_A      )
      _lowerCAmelCase       :   Any  =    self.python_features_decoder.decode_batch(_A      )
      _lowerCAmelCase       :   str  =    self.recursive_tensorize(_A      )
      for column_name in batch:
          _lowerCAmelCase       :   Optional[Any]  =    self._consolidate(batch[column_name]      )
      return batch





