"""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__    :    Dict			=      getLogger(__name__)


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



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase = 8   ,     lowerCamelCase = DEFAULT_DEVICE   ,     lowerCamelCase=False   ,     lowerCamelCase="summarization"   ,     lowerCamelCase=None   ,     **lowerCamelCase   ,     ):
				UpperCAmelCase__			 =				Path(A__			).open('w'   ,     encoding='utf-8'			)
				UpperCAmelCase__			 =				str(A__			)
				UpperCAmelCase__			 =				AutoModelForSeqaSeqLM.from_pretrained(A__			).to(A__			)
				if fpaa:
								UpperCAmelCase__			 =				model.half()

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



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



def     a_		(   lowerCamelCase=True			):
				UpperCAmelCase__			 =				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__			 =				parser.parse_known_args()
				UpperCAmelCase__			 =				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:
								UpperCAmelCase__			 =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
				if args.n_obs > 0:
								UpperCAmelCase__			 =				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.'''			)
				UpperCAmelCase__			 =				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__			 =				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(A__			)]
				UpperCAmelCase__			 =				score_fn(A__   ,     A__			)
				scores.update(A__			)

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A       =					logging.get_logger(__name__)

_A       =					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[Any] 	=						"cpmant"





     def __init__(self :       List[Any]							,       _A :       int = 3_0_7_2_0							,       _A :       int = 4_0_9_6							,       _A :       int = 3_2							,       _A :       int = 1_2_8							,       _A :       int = 1_0_2_4_0							,       _A :       int = 4_8							,       _A :       int = 0.0							,       _A :       int = 5_1_2							,       _A :       int = 2_0_4_8							,       _A :       int = 1E-6							,       _A :       float = 1.0							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       bool = True							,       **_A :       Dict							,       )		->		Dict:
         super().__init__(**_A  )
         snake_case						=   prompt_types
         snake_case						=   prompt_length
         snake_case						=   segment_types
         snake_case						=   hidden_size
         snake_case						=   num_attention_heads
         snake_case						=   dim_head
         snake_case						=   dim_ff
         snake_case						=   num_hidden_layers
         snake_case						=   position_bias_num_buckets
         snake_case						=   position_bias_max_distance
         snake_case						=   dropout_p
         snake_case						=   eps
         snake_case						=   use_cache
         snake_case						=   vocab_size
         snake_case						=   init_std

