




"""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


a								=							getLogger(__name__)


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





def 							lowercase   (snake_case__				:	Optional[int] ,    snake_case__				:	Dict ,    snake_case__				:	List[Any] ,    snake_case__				:	Optional[int] = 8 ,    snake_case__				:	str = DEFAULT_DEVICE ,    snake_case__				:	Dict=False ,    snake_case__				:	int="summarization" ,    snake_case__				:	Optional[int]=None ,    **snake_case__				:	Optional[Any] ,    )       ->					Tuple:


				'''simple docstring'''






				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   ()       ->					Dict:


				'''simple docstring'''






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





def 							lowercase   (snake_case__				:	Tuple=True	)       ->					List[str]:


				'''simple docstring'''






				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		=			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)











"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


a								=							logging.get_logger(__name__)

a								=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a								=							{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

a								=							{
    'allenai/longformer-base-4096': 4_0_9_6,
    'allenai/longformer-large-4096': 4_0_9_6,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  VOCAB_FILES_NAMES
						_a        =  PRETRAINED_VOCAB_FILES_MAP
						_a        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a        =  ['input_ids', 'attention_mask']
						_a        =  LongformerTokenizer


						def __init__(							self			:     Optional[Any]  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     Any=None  ,  lowerCAmelCase			:     List[Any]=None  ,  lowerCAmelCase			:     Optional[Any]="replace"  ,  lowerCAmelCase			:     Union[str, Any]="<s>"  ,  lowerCAmelCase			:     int="</s>"  ,  lowerCAmelCase			:     Any="</s>"  ,  lowerCAmelCase			:     Union[str, Any]="<s>"  ,  lowerCAmelCase			:     List[str]="<unk>"  ,  lowerCAmelCase			:     Optional[Any]="<pad>"  ,  lowerCAmelCase			:     List[str]="<mask>"  ,  lowerCAmelCase			:     Tuple=False  ,  lowerCAmelCase			:     List[str]=True  ,  **lowerCAmelCase			:     Optional[int]  ,  ):
										super().__init__(
										    lowerCAmelCase  ,  lowerCAmelCase  ,  tokenizer_file=lowerCAmelCase  ,  errors=lowerCAmelCase  ,  bos_token=lowerCAmelCase  ,  eos_token=lowerCAmelCase  ,  sep_token=lowerCAmelCase  ,  cls_token=lowerCAmelCase  ,  unk_token=lowerCAmelCase  ,  pad_token=lowerCAmelCase  ,  mask_token=lowerCAmelCase  ,  add_prefix_space=lowerCAmelCase  ,  trim_offsets=lowerCAmelCase  ,  **lowerCAmelCase  ,  )

										lowerCAmelCase		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
										if pre_tok_state.get("""add_prefix_space"""  ,  lowerCAmelCase   ) != add_prefix_space:
														lowerCAmelCase		=			getattr(lowerCAmelCase  ,  pre_tok_state.pop("""type"""   )   )
														lowerCAmelCase		=			add_prefix_space
														lowerCAmelCase		=			pre_tok_class(**lowerCAmelCase   )

										lowerCAmelCase		=			add_prefix_space

										lowerCAmelCase		=			"""post_processor"""
										lowerCAmelCase		=			getattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )
										if tokenizer_component_instance:
														lowerCAmelCase		=			json.loads(tokenizer_component_instance.__getstate__()   )

														# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
														if "sep" in state:
																		lowerCAmelCase		=			tuple(state["""sep"""]   )
														if "cls" in state:
																		lowerCAmelCase		=			tuple(state["""cls"""]   )

														lowerCAmelCase		=			False

														if state.get("""add_prefix_space"""  ,  lowerCAmelCase   ) != add_prefix_space:
																		lowerCAmelCase		=			add_prefix_space
																		lowerCAmelCase		=			True

														if state.get("""trim_offsets"""  ,  lowerCAmelCase   ) != trim_offsets:
																		lowerCAmelCase		=			trim_offsets
																		lowerCAmelCase		=			True

														if changes_to_apply:
																		lowerCAmelCase		=			getattr(lowerCAmelCase  ,  state.pop("""type"""   )   )
																		lowerCAmelCase		=			component_class(**lowerCAmelCase   )
																		setattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										if self._mask_token is None:
														if self.verbose:
																		logger.error("""Using mask_token, but it is not set yet."""   )
														return None
										return str(self._mask_token   )


						@mask_token.setter
						def 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase		=			AddedToken(lowerCAmelCase  ,  lstrip=lowerCAmelCase  ,  rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase  ,  lowerCAmelCase   ) else value
										lowerCAmelCase		=			value


						def 					__lowercase	(							self			:     Tuple  ,  *lowerCAmelCase			:     Optional[int]  ,  **lowerCAmelCase			:     Optional[Any]   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )
										assert self.add_prefix_space or not is_split_into_words, (
										    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     str  ,  **lowerCAmelCase			:     str   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )

										assert self.add_prefix_space or not is_split_into_words, (
										    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     Optional[str] = None   ):
										lowerCAmelCase		=			self._tokenizer.model.save(lowerCAmelCase  ,  name=lowerCAmelCase   )
										return tuple(lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Optional[int]=None   ):
										lowerCAmelCase		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
														return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     List[int]  ,  lowerCAmelCase			:     Optional[List[int]] = 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 + sep + token_ids_a + sep   ) * [0]





