



'''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:      Tuple		 =		getLogger(__name__)


lowerCAmelCase:      Optional[int]		 =		'cuda' if torch.cuda.is_available() else 'cpu'
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A = 8				,    _A = DEFAULT_DEVICE				,    _A=False				,    _A="summarization"				,    _A=None				,    **_A				,    ):
  a		:      str       =  Path(_A       ).open('w'				,    encoding='utf-8'       )
  a		:      Any       =  str(_A       )
  a		:      List[Any]       =  AutoModelForSeqaSeqLM.from_pretrained(_A       ).to(_A       )
  if fpaa:
    a		:      Optional[Any]       =  model.half()

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

  a		:      Optional[int]       =  time.time()
  # update config with task specific params
  use_task_specific_params(_A				,    _A       )
  if prefix is None:
    a		:      Union[str, Any]       =  prefix or getattr(model.config				,    'prefix'				,    ''       ) or ''
  for examples_chunk in tqdm(list(chunks(_A				,    _A       )       )       ):
    a		:      str       =  [prefix + text for text in examples_chunk]
    a		:      Union[str, Any]       =  tokenizer(_A				,    return_tensors='pt'				,    truncation=_A				,    padding='longest'       ).to(_A       )
    a		:      str       =  model.generate(
        input_ids=batch.input_ids				,    attention_mask=batch.attention_mask				,    **_A				,    )
    a		:      Dict       =  tokenizer.batch_decode(_A				,    skip_special_tokens=_A				,    clean_up_tokenization_spaces=_A       )
    for hypothesis in dec:
      fout.write(hypothesis + '\n'       )
      fout.flush()
  fout.close()
  a		:      Optional[int]       =  int(time.time() - start_time       )  # seconds
  a		:      Tuple       =  len(_A       )
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs				,    4       )}
def    lowerCamelCase__ (		):
  return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'       )
def    lowerCamelCase__ (		_A=True       ):
  a		:      Tuple       =  argparse.ArgumentParser()
  parser.add_argument('model_name'				,    type=_A				,    help='like facebook/bart-large-cnn,t5-base, etc.'       )
  parser.add_argument('input_path'				,    type=_A				,    help='like cnn_dm/test.source'       )
  parser.add_argument('save_path'				,    type=_A				,    help='where to save summaries'       )
  parser.add_argument('--reference_path'				,    type=_A				,    required=_A				,    help='like cnn_dm/test.target'       )
  parser.add_argument('--score_path'				,    type=_A				,    required=_A				,    default='metrics.json'				,    help='where to save metrics'       )
  parser.add_argument('--device'				,    type=_A				,    required=_A				,    default=_A				,    help='cuda, cuda:1, cpu etc.'       )
  parser.add_argument(
      '--prefix'				,    type=_A				,    required=_A				,    default=_A				,    help='will be added to the begininng of src examples'       )
  parser.add_argument('--task'				,    type=_A				,    default='summarization'				,    help='used for task_specific_params + metrics'       )
  parser.add_argument('--bs'				,    type=_A				,    default=8				,    required=_A				,    help='batch size'       )
  parser.add_argument(
      '--n_obs'				,    type=_A				,    default=-1				,    required=_A				,    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=_A				,    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
  a     ,			a		:      Any       =  parser.parse_known_args()
  a		:      List[str]       =  parse_numeric_n_bool_cl_kwargs(_A       )
  if parsed_args and verbose:
    print(f"""parsed the following generate kwargs: {parsed_args}"""       )
  a		:      Any       =  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path       ).readlines()]
  if args.n_obs > 0:
    a		:      Dict       =  examples[: args.n_obs]
  Path(args.save_path       ).parent.mkdir(exist_ok=_A       )

  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'       )

  a		:      List[Any]       =  generate_summaries_or_translations(
      _A				,    args.save_path				,    args.model_name				,    batch_size=args.bs				,    device=args.device				,    fpaa=args.fpaa				,    task=args.task				,    prefix=args.prefix				,    **_A				,    )

  if args.reference_path is None:
    return {}

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

  if args.dump_args:
    scores.update(_A       )
  if args.info:
    a		:      int       =  args.info

  if verbose:
    print(_A       )

  if args.score_path is not None:
    json.dump(_A				,    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'''






import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     from transformers.models.bert.modeling_flax_bert import (
         FlaxBertForMaskedLM,
         FlaxBertForMultipleChoice,
         FlaxBertForNextSentencePrediction,
         FlaxBertForPreTraining,
         FlaxBertForQuestionAnswering,
         FlaxBertForSequenceClassification,
         FlaxBertForTokenClassification,
         FlaxBertModel,
     )

class 							a__(      unittest.TestCase			):







    def __init__(   self				:						Dict		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Any=13		,    __snake_case				:						Any=7		,    __snake_case				:						int=True		,    __snake_case				:						List[Any]=True		,    __snake_case				:						int=True		,    __snake_case				:						List[Any]=True		,    __snake_case				:						Optional[Any]=99		,    __snake_case				:						List[str]=32		,    __snake_case				:						List[Any]=5		,    __snake_case				:						int=4		,    __snake_case				:						List[str]=37		,    __snake_case				:						int="gelu"		,    __snake_case				:						Tuple=0.1		,    __snake_case				:						Union[str, Any]=0.1		,    __snake_case				:						Any=5_12		,    __snake_case				:						Tuple=16		,    __snake_case				:						Union[str, Any]=2		,    __snake_case				:						str=0.02		,    __snake_case				:						Union[str, Any]=4		,    ):
      a		:      List[Any]       =  parent
      a		:      List[str]       =  batch_size
      a		:      Dict       =  seq_length
      a		:      str       =  is_training
      a		:      Optional[int]       =  use_attention_mask
      a		:      Union[str, Any]       =  use_token_type_ids
      a		:      List[str]       =  use_labels
      a		:      Dict       =  vocab_size
      a		:      Tuple       =  hidden_size
      a		:      Optional[Any]       =  num_hidden_layers
      a		:      List[str]       =  num_attention_heads
      a		:      Tuple       =  intermediate_size
      a		:      Dict       =  hidden_act
      a		:      List[str]       =  hidden_dropout_prob
      a		:      str       =  attention_probs_dropout_prob
      a		:      str       =  max_position_embeddings
      a		:      Tuple       =  type_vocab_size
      a		:      Optional[Any]       =  type_sequence_label_size
      a		:      Union[str, Any]       =  initializer_range
      a		:      Union[str, Any]       =  num_choices







    def     lowercase_ (   self				:						Any			):
      a		:      List[Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

      a		:      List[str]       =  None
      if self.use_attention_mask:
        a		:      List[Any]       =  random_attention_mask([self.batch_size, self.seq_length]			)

      a		:      Dict       =  None
      if self.use_token_type_ids:
        a		:      List[str]       =  ids_tensor([self.batch_size, self.seq_length]		,    self.type_vocab_size			)

      a		:      Dict       =  BertConfig(
          vocab_size=self.vocab_size		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    type_vocab_size=self.type_vocab_size		,    is_decoder=__snake_case		,    initializer_range=self.initializer_range		,    )

      return config, input_ids, token_type_ids, attention_mask







    def     lowercase_ (   self				:						Tuple			):
      a		:      Optional[int]       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a     ,			a		:      Union[str, Any]       =  config_and_inputs
      a		:      str       =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict







    def     lowercase_ (   self				:						List[str]			):
      a		:      int       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a     ,			a		:      Tuple       =  config_and_inputs

      a		:      int       =  True
      a		:      int       =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      a		:      Optional[Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    vocab_size=2			)

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )

@require_flax
class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					True

    lowercase__				=					(
        (
            FlaxBertModel,
            FlaxBertForPreTraining,
            FlaxBertForMaskedLM,
            FlaxBertForMultipleChoice,
            FlaxBertForQuestionAnswering,
            FlaxBertForNextSentencePrediction,
            FlaxBertForSequenceClassification,
            FlaxBertForTokenClassification,
            FlaxBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      str       =  FlaxBertModelTester(self			)







    @slow
    def     lowercase_ (   self				:						Any			):
      # Only check this for base model, not necessary for all model classes.
      # This will also help speed-up tests.
      a		:      Optional[Any]       =  FlaxBertModel.from_pretrained('bert-base-cased'			)
      a		:      Union[str, Any]       =  model(np.ones((1, 1)			)			)
      self.assertIsNotNone(__snake_case			)