
'''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


__magic_name__            =  getLogger(__name__)


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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase = 8    ,		__lowerCAmelCase = DEFAULT_DEVICE    ,		__lowerCAmelCase=False    ,		__lowerCAmelCase="summarization"    ,		__lowerCAmelCase=None    ,		**__lowerCAmelCase    ,		):
    snake_case__						=				Path(__lowerCAmelCase							).open("w"    ,		encoding="utf-8"							)
    snake_case__						=				str(__lowerCAmelCase							)
    snake_case__						=				AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase							).to(__lowerCAmelCase							)
    if fpaa:
        snake_case__						=				model.half()

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





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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase=True							):
    snake_case__						=				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
    snake_case__						,					snake_case__						=				parser.parse_known_args()
    snake_case__						=				parse_numeric_n_bool_cl_kwargs(__lowerCAmelCase							)
    if parsed_args and verbose:
        print(F"""parsed the following generate kwargs: {parsed_args}"""							)
    snake_case__						=				[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path							).readlines()]
    if args.n_obs > 0:
        snake_case__						=				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"							)

    snake_case__						=				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
    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(__lowerCAmelCase							)]
    snake_case__						=				score_fn(__lowerCAmelCase    ,		__lowerCAmelCase							)
    scores.update(__lowerCAmelCase							)

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





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     __magic_name__            =  None


__magic_name__            =  logging.get_logger(__name__)


__magic_name__            =  '''▁'''

__magic_name__            =  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__            =  {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

__magic_name__            =  {
    '''google/pegasus-xsum''': 512,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		int    	=     VOCAB_FILES_NAMES
      _A						:		Dict    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A						:		Tuple    	=     PegasusTokenizer
      _A						:		str    	=     ['input_ids', 'attention_mask']
      def __init__(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase="<pad>"		,	lowerCamelCase="</s>"		,	lowerCamelCase="<unk>"		,	lowerCamelCase="<mask_2>"		,	lowerCamelCase="<mask_1>"		,	lowerCamelCase=None		,	lowerCamelCase=1_03		,	**lowerCamelCase		,	):
          snake_case__						=				offset

          if additional_special_tokens is not None:
              if not isinstance(lowerCamelCase		,	lowerCamelCase ):
                  raise TypeError(
                      F"""additional_special_tokens should be of type {type(lowerCamelCase )}, but is"""
                      F""" {type(lowerCamelCase )}""" )

              snake_case__						=				(
                  ([mask_token_sent] + additional_special_tokens)
                  if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                  else additional_special_tokens
              )
              # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
              additional_special_tokens_extended += [
                  F"""<unk_{i}>""" for i in range(len(lowerCamelCase )		,	self.offset - 1 )
              ]

              if len(set(lowerCamelCase ) ) != len(lowerCamelCase ):
                  raise ValueError(
                      "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                      F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
              snake_case__						=				additional_special_tokens_extended
          else:
              snake_case__						=				[mask_token_sent] if mask_token_sent is not None else []
              additional_special_tokens += [F"""<unk_{i}>""" for i in range(2		,	self.offset )]

          super().__init__(
              lowerCamelCase		,	tokenizer_file=lowerCamelCase		,	pad_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	mask_token_sent=lowerCamelCase		,	offset=lowerCamelCase		,	additional_special_tokens=lowerCamelCase		,	**lowerCamelCase		,	)
          snake_case__						=				vocab_file
          snake_case__						=				False if not self.vocab_file else True
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				set(self.all_special_ids )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
              raise ValueError(
                  "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                  F""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )

          return [1 if x in all_special_ids else 0 for x in seq]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):
          if already_has_special_tokens:
              return self._special_token_mask(lowerCamelCase )
          elif token_ids_a is None:
              return self._special_token_mask(lowerCamelCase ) + [1]
          else:
              return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=None ):
          if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer." )

          if not os.path.isdir(lowerCamelCase ):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)



