



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						      =       getLogger(__name__)


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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = 8						,      lowerCAmelCase_ = DEFAULT_DEVICE						,      lowerCAmelCase_=False						,      lowerCAmelCase_="summarization"						,      lowerCAmelCase_=None						,      **lowerCAmelCase_						,      )      ->				Dict:
		_a					:				Union[str, Any]				=							Path(lowerCAmelCase_  ).open('w'						,      encoding='utf-8'  )
		_a					:				Union[str, Any]				=							str(lowerCAmelCase_  )
		_a					:				Union[str, Any]				=							AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_  ).to(lowerCAmelCase_  )
		if fpaa:
				_a					:				Tuple				=							model.half()

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

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



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



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

		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'  )

		_a					:				Any				=							generate_summaries_or_translations(
		    lowerCAmelCase_						,      args.save_path						,      args.model_name						,      batch_size=args.bs						,      device=args.device						,      fpaa=args.fpaa						,      task=args.task						,      prefix=args.prefix						,      **lowerCAmelCase_						,      )

		if args.reference_path is None:
				return {}

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

		if args.dump_args:
				scores.update(lowerCAmelCase_  )
		if args.info:
				_a					:				Optional[int]				=							args.info

		if verbose:
				print(lowerCAmelCase_  )

		if args.score_path is not None:
				json.dump(lowerCAmelCase_						,      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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Optional[int]           = 'distilbert'
							lowerCAmelCase	:   Optional[int]           = {
							    'hidden_size': 'dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							}







							def __init__(							self :	str     ,_UpperCAmelCase :	Optional[Any]=30522     ,_UpperCAmelCase :	int=512     ,_UpperCAmelCase :	int=False     ,_UpperCAmelCase :	str=6     ,_UpperCAmelCase :	int=12     ,_UpperCAmelCase :	Union[str, Any]=768     ,_UpperCAmelCase :	Optional[Any]=4 * 768     ,_UpperCAmelCase :	int=0.1     ,_UpperCAmelCase :	Union[str, Any]=0.1     ,_UpperCAmelCase :	Tuple="gelu"     ,_UpperCAmelCase :	Any=0.02     ,_UpperCAmelCase :	List[Any]=0.1     ,_UpperCAmelCase :	Dict=0.2     ,_UpperCAmelCase :	Union[str, Any]=0     ,**_UpperCAmelCase :	List[Any]     ,):
									_a					:				Optional[Any]				=							vocab_size
									_a					:				Dict				=							max_position_embeddings
									_a					:				Optional[int]				=							sinusoidal_pos_embds
									_a					:				List[Any]				=							n_layers
									_a					:				Any				=							n_heads
									_a					:				int				=							dim
									_a					:				Optional[Any]				=							hidden_dim
									_a					:				str				=							dropout
									_a					:				Optional[Any]				=							attention_dropout
									_a					:				Any				=							activation
									_a					:				Tuple				=							initializer_range
									_a					:				Union[str, Any]				=							qa_dropout
									_a					:				List[Any]				=							seq_classif_dropout
									super().__init__(**_UpperCAmelCase     ,pad_token_id=_UpperCAmelCase		)

class      __magic_name__						(   _UpperCamelCase       ):







							@property
							def 							__lowercase      (							self :	Union[str, Any]		):
									if self.task == "multiple-choice":
											_a					:				Union[str, Any]				=							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_a					:				List[str]				=							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)




