'''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


a=  getLogger(__name__)


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







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




    """simple docstring"""



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

    __UpperCamelCase	:	Optional[int]		    =  AutoTokenizer.from_pretrained(_a			)
    logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

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







def  _UpperCamelCase (     ):




    """simple docstring"""



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







def  _UpperCamelCase (     _a	:	List[Any]=True			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  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
    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  parser.parse_known_args()
    __UpperCamelCase	:	str		    =  parse_numeric_n_bool_cl_kwargs(_a			)
    if parsed_args and verbose:
        print(f"""parsed the following generate kwargs: {parsed_args}"""			)
    __UpperCamelCase	:	List[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:
        __UpperCamelCase	:	int		    =  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'			)

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

    if args.dump_args:
        scores.update(_a			)
    if args.info:
        __UpperCamelCase	:	Dict		    =  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'''
from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray







def  _UpperCamelCase (     _a	:	NDArray[floataa]				,     _a	:	NDArray[floataa]				,     _a	:	list[int]				,     _a	:	int				,     ):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  coefficient_matrix.shape
    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  constant_matrix.shape

    if rowsa != colsa:
        __UpperCamelCase	:	Tuple		    =  f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
        raise ValueError(_a			)

    if colsa != 1:
        __UpperCamelCase	:	List[Any]		    =  f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
        raise ValueError(_a			)

    if rowsa != rowsa:
        __UpperCamelCase	:	Optional[int]		    =  (
            'Coefficient and constant matrices dimensions must be nxn and nx1 but '
            f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
        )
        raise ValueError(_a			)

    if len(_a			) != rowsa:
        __UpperCamelCase	:	Union[str, Any]		    =  (
            'Number of initial values must be equal to number of rows in coefficient '
            f"""matrix but received {len(_a			)} and {rowsa}"""
        )
        raise ValueError(_a			)

    if iterations <= 0:
        raise ValueError('Iterations must be at least 1'			)

    __UpperCamelCase	:	NDArray[floataa]		    =  np.concatenate(
        (coefficient_matrix, constant_matrix)				,     axis=1			)

    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  table.shape

    strictly_diagonally_dominant(_a			)

    # Iterates the whole matrix for given number of times
    for _ in range(_a			):
        __UpperCamelCase	:	List[Any]		    =  []
        for row in range(_a			):
            __UpperCamelCase	:	List[Any]		    =  0
            for col in range(_a			):
                if col == row:
                    __UpperCamelCase	:	Optional[int]		    =  table[row][col]
                elif col == cols - 1:
                    __UpperCamelCase	:	str		    =  table[row][col]
                else:
                    temp += (-1) * table[row][col] * init_val[col]
            __UpperCamelCase	:	Any		    =  (temp + val) / denom
            new_val.append(_a			)
        __UpperCamelCase	:	List[Any]		    =  new_val

    return [float(_a			) for i in new_val]







def  _UpperCamelCase (     _a	:	NDArray[floataa]			):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  table.shape

    __UpperCamelCase	:	str		    =  True

    for i in range(0				,     _a			):
        __UpperCamelCase	:	Optional[Any]		    =  0
        for j in range(0				,     cols - 1			):
            if i == j:
                continue
            else:
                total += table[i][j]

        if table[i][i] <= total:
            raise ValueError('Coefficient matrix is not strictly diagonally dominant'			)

    return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
  import doctest

  doctest.testmod()



