
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 		lowercase_     (  A__					,						A__					,						A__					,						A__ = 8					,						A__ = DEFAULT_DEVICE					,						A__=False					,						A__="summarization"					,						A__=None					,						**A__					,						)				->				Dict:
    """simple docstring"""

    snake_case						=   Path(A__						).open("w"					,						encoding="utf-8"						)
    snake_case						=   str(A__						)
    snake_case						=   AutoModelForSeqaSeqLM.from_pretrained(A__						).to(A__						)
    if fpaa:
        snake_case						=   model.half()

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

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



def 		lowercase_     (  )				->				int:
    """simple docstring"""

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



def 		lowercase_     (  A__=True						)				->				List[Any]:
    """simple docstring"""

    snake_case						=   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
    snake_case	,				snake_case						=   parser.parse_known_args()
    snake_case						=   parse_numeric_n_bool_cl_kwargs(A__						)
    if parsed_args and verbose:
        print(F'parsed the following generate kwargs: {parsed_args}'						)
    with open(args.input_path						) as f:
        snake_case						=   [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
    if args.n_obs > 0:
        snake_case						=   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.'						)
    snake_case						=   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
    snake_case						=   calculate_bleu if "translation" in args.task else calculate_rouge
    snake_case						=   [x.rstrip() for x in open(args.save_path						).readlines()]
    snake_case						=   [x.rstrip() for x in open(args.reference_path						).readlines()][: len(A__						)]
    snake_case						=   score_fn(A__					,						A__						)
    scores.update(A__						)

    if args.dump_args:
        scores.update(A__						)
    if args.info:
        snake_case						=   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)



import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_A       =					[[12, 10], [3, 9]]
_A       =					[[3, 4], [7, 4]]
_A       =					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_A       =					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_A       =					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_A       =					[1]
_A       =					[2]

_A       =					logging.getLogger()
_A       =					logging.DEBUG
_A       =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				Dict:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_addition.__name__} returned integer'						)
            matop.add(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_addition.__name__} with same matrix dims'						)
        snake_case						=   (np.array(A__						) + np.array(A__						)).tolist()
        snake_case						=   matop.add(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_addition.__name__} with different matrix dims'						)
            matop.add(A__					,						A__						)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				List[Any]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_subtraction.__name__} returned integer'						)
            matop.subtract(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'						)
        snake_case						=   (np.array(A__						) - np.array(A__						)).tolist()
        snake_case						=   matop.subtract(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'						)
            assert matop.subtract(A__					,						A__						)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				Union[str, Any]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        logger.info(F'\n\t{test_multiplication.__name__} returned integer'						)
        with pytest.raises(A__						):
            matop.add(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'						)
        snake_case						=   (np.matmul(A__					,						A__						)).tolist()
        snake_case						=   matop.multiply(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(
                F'\n\t{test_multiplication.__name__} does not meet dim requirements'						)
            assert matop.subtract(A__					,						A__						)



@pytest.mark.mat_ops
def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   (3.5 * np.array(A__						)).tolist()
    snake_case						=   matop.scalar_multiply(A__					,						3.5						)
    assert theo == act



@pytest.mark.mat_ops
def 		lowercase_     (  )				->				Any:
    """simple docstring"""

    snake_case						=   (np.identity(5						)).tolist()
    snake_case						=   matop.identity(5						)
    assert theo == act





@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"					,						[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]						)
def 		lowercase_     (  A__						)				->				List[str]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_transpose.__name__} returned integer'						)
            matop.transpose(A__						)
    else:
        snake_case						=   (np.transpose(A__						)).tolist()
        snake_case						=   matop.transpose(A__					,						return_map=A__						)
        assert theo == act

