





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


UpperCAmelCase__    :      Any       					=getLogger(__name__)


UpperCAmelCase__    :      Any       					='''cuda''' if torch.cuda.is_available() else '''cpu'''





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase = 8      ,   _UpperCAmelCase = DEFAULT_DEVICE      ,   _UpperCAmelCase=False      ,   _UpperCAmelCase="summarization"      ,   _UpperCAmelCase=None      ,   **_UpperCAmelCase      ,   )    ->	Dict:
				lowerCamelCase									=Path(_UpperCAmelCase					).open("""w"""      ,   encoding="""utf-8"""					)
				lowerCamelCase									=str(_UpperCAmelCase					)
				lowerCamelCase									=AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase					).to(_UpperCAmelCase					)
				if fpaa:
								lowerCamelCase									=model.half()

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

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





def   _lowercase       (    )    ->	List[Any]:
				return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""					)





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

				if args.reference_path is None:
								return {}

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

				if args.dump_args:
								scores.update(_UpperCAmelCase					)
				if args.info:
								lowerCamelCase									=args.info

				if verbose:
								print(_UpperCAmelCase					)

				if args.score_path is not None:
								json.dump(_UpperCAmelCase      ,   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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase__    :      int       					=logging.get_logger(__name__)

UpperCAmelCase__    :      Optional[Any]       					={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__    :      List[str]       					={
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__    :      Dict       					={
    '''microsoft/layoutlm-base-uncased''': 5_12,
    '''microsoft/layoutlm-large-uncased''': 5_12,
}

UpperCAmelCase__    :      List[Any]       					={
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}

class    __A  (    a     ):
				__A  =					VOCAB_FILES_NAMES
				__A  =					PRETRAINED_VOCAB_FILES_MAP
				__A  =					PRETRAINED_INIT_CONFIGURATION
				__A  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A  =					LayoutLMTokenizer


				def __init__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=True ,			UpperCAmelCase_="[UNK]" ,			UpperCAmelCase_="[SEP]" ,			UpperCAmelCase_="[PAD]" ,			UpperCAmelCase_="[CLS]" ,			UpperCAmelCase_="[MASK]" ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None ,			**UpperCAmelCase_ ,			):
								super().__init__(
								    UpperCAmelCase_ ,			tokenizer_file=UpperCAmelCase_ ,			do_lower_case=UpperCAmelCase_ ,			unk_token=UpperCAmelCase_ ,			sep_token=UpperCAmelCase_ ,			pad_token=UpperCAmelCase_ ,			cls_token=UpperCAmelCase_ ,			mask_token=UpperCAmelCase_ ,			tokenize_chinese_chars=UpperCAmelCase_ ,			strip_accents=UpperCAmelCase_ ,			**UpperCAmelCase_ ,			)

								lowerCamelCase									=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase""" ,			UpperCAmelCase_				) != do_lower_case
								    or normalizer_state.get("""strip_accents""" ,			UpperCAmelCase_				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars""" ,			UpperCAmelCase_				) != tokenize_chinese_chars
								):
												lowerCamelCase									=getattr(UpperCAmelCase_ ,			normalizer_state.pop("""type"""				)				)
												lowerCamelCase									=do_lower_case
												lowerCamelCase									=strip_accents
												lowerCamelCase									=tokenize_chinese_chars
												lowerCamelCase									=normalizer_class(**UpperCAmelCase_				)

								lowerCamelCase									=do_lower_case


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=[self.sep_token_id]
								lowerCamelCase									=[self.cls_token_id]
								if token_ids_a is None:
												return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=self._tokenizer.model.save(UpperCAmelCase_ ,			name=UpperCAmelCase_				)
								return tuple(UpperCAmelCase_				)





