
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


__magic_name__  :  Optional[int]     			=   getLogger(__name__)


__magic_name__  :  Optional[int]     			=   'cuda' if torch.cuda.is_available() else 'cpu'





def        lowerCAmelCase       (				snake_case__   :      Tuple				,					snake_case__   :      Tuple				,					snake_case__   :      int				,					snake_case__   :      Union[str, Any] = 8				,					snake_case__   :      Optional[int] = DEFAULT_DEVICE				,					snake_case__   :      Any=False				,					snake_case__   :      Dict="summarization"				,					snake_case__   :      Union[str, Any]=None				,					**snake_case__   :      List[Any]				,					)->			Dict:
    A_								=					Path(snake_case__ ).open("w"				,					encoding="utf-8" )
    A_								=					str(snake_case__ )
    A_								=					AutoModelForSeqaSeqLM.from_pretrained(snake_case__ ).to(snake_case__ )
    if fpaa:
        A_								=					model.half()

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

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





def        lowerCAmelCase       (				)->			Union[str, Any]:
    return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )





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

    if args.reference_path is None:
        return {}

    # Compute scores
    A_								=					calculate_bleu if 'translation' in args.task else calculate_rouge
    A_								=					[x.rstrip() for x in open(args.save_path ).readlines()]
    A_								=					[x.rstrip() for x in open(args.reference_path ).readlines()][: len(snake_case__ )]
    A_								=					score_fn(snake_case__				,					snake_case__ )
    scores.update(snake_case__ )

    if args.dump_args:
        scores.update(snake_case__ )
    if args.info:
        A_								=					args.info

    if verbose:
        print(snake_case__ )

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









'''simple docstring'''


import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
						import torch



class        lowercase    (	unittest.TestCase,       a_			):


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   int													=       load_tool('speech-to-text')
								self.tool.setup()


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   str													=       self.tool(torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')





							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[Any]													=       self.tool(audio=torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')






