


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 						_UpperCAmelCase    (      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__      : Dict=False      ,					SCREAMING_SNAKE_CASE__      : Any="summarization"      ,					SCREAMING_SNAKE_CASE__      : Any=None      ,					**SCREAMING_SNAKE_CASE__      : int      ,					):
     __UpperCamelCase   							=Path(SCREAMING_SNAKE_CASE__    ).open('w'      ,					encoding='utf-8'    )
     __UpperCamelCase   							=str(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__    ).to(SCREAMING_SNAKE_CASE__    )
     if fpaa:
          __UpperCamelCase   							=model.half()

     __UpperCamelCase   							=AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
     logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'    )  # if this is wrong, check config.model_type.

     __UpperCamelCase   							=time.time()
     # update config with task specific params
     use_task_specific_params(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     if prefix is None:
          __UpperCamelCase   							=prefix or getattr(model.config      ,					'prefix'      ,					''    ) or ''
     for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )    )    ):
          __UpperCamelCase   							=[prefix + text for text in examples_chunk]
          __UpperCamelCase   							=tokenizer(SCREAMING_SNAKE_CASE__      ,					return_tensors='pt'      ,					truncation=SCREAMING_SNAKE_CASE__      ,					padding='longest'    ).to(SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=model.generate(
              input_ids=batch.input_ids      ,					attention_mask=batch.attention_mask      ,					**SCREAMING_SNAKE_CASE__      ,					)
          __UpperCamelCase   							=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()
     __UpperCamelCase   							=int(time.time() - start_time    )  # seconds
     __UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    )
     return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs      ,					4    )}

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int=True    ):
     __UpperCamelCase   							=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
     __UpperCamelCase							,				__UpperCamelCase   							=parser.parse_known_args()
     __UpperCamelCase   							=parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__    )
     if parsed_args and verbose:
          print(F'parsed the following generate kwargs: {parsed_args}'    )
     __UpperCamelCase   							=[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path    ).readlines()]
     if args.n_obs > 0:
          __UpperCamelCase   							=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.'    )

     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'    )

     __UpperCamelCase   							=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
     __UpperCamelCase   							=calculate_bleu if 'translation' in args.task else calculate_rouge
     __UpperCamelCase   							=[x.rstrip() for x in open(args.save_path    ).readlines()]
     __UpperCamelCase   							=[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(SCREAMING_SNAKE_CASE__    )]
     __UpperCamelCase   							=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:
          __UpperCamelCase   							=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)









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


_A			=						logging.get_logger(__name__)

_A			=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_A			=						{
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

_A			=						{
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

_A			=						{
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any              =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		List[str]              =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Optional[int]              =  PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase__						:		List[str]              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		Dict              =  BertTokenizer






    def __init__(      self ,       A_=None ,       A_=None ,       A_=True ,       A_="[UNK]" ,       A_="[SEP]" ,       A_="[PAD]" ,       A_="[CLS]" ,       A_="[MASK]" ,       A_=True ,       A_=None ,       **A_ ,       )  ->	Any:
         super().__init__(
             A_ ,       tokenizer_file=A_ ,       do_lower_case=A_ ,       unk_token=A_ ,       sep_token=A_ ,       pad_token=A_ ,       cls_token=A_ ,       mask_token=A_ ,       tokenize_chinese_chars=A_ ,       strip_accents=A_ ,       **A_ ,       )

         __UpperCamelCase   							=json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
         if (
             normalizer_state.get('lowercase' ,       A_   ) != do_lower_case
             or normalizer_state.get('strip_accents' ,       A_   ) != strip_accents
             or normalizer_state.get('handle_chinese_chars' ,       A_   ) != tokenize_chinese_chars
         ):
              __UpperCamelCase   							=getattr(A_ ,       normalizer_state.pop('type'   )   )
              __UpperCamelCase   							=do_lower_case
              __UpperCamelCase   							=strip_accents
              __UpperCamelCase   							=tokenize_chinese_chars
              __UpperCamelCase   							=normalizer_class(**A_   )

         __UpperCamelCase   							=do_lower_case






    def _a (      self ,       A_ ,       A_=None   )  ->	List[str]:
         __UpperCamelCase   							=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

         return output






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:
         __UpperCamelCase   							=[self.sep_token_id]
         __UpperCamelCase   							=[self.cls_token_id]
         if token_ids_a is None:
              return len(cls + token_ids_a + sep   ) * [0]
         return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple[str]:
         __UpperCamelCase   							=self._tokenizer.model.save(A_ ,       name=A_   )
         return tuple(A_   )





