





"""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__        =      getLogger(__name__)


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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE = 8	,						_SCREAMING_SNAKE_CASE = DEFAULT_DEVICE	,						_SCREAMING_SNAKE_CASE=False	,						_SCREAMING_SNAKE_CASE="summarization"	,						_SCREAMING_SNAKE_CASE=None	,						**_SCREAMING_SNAKE_CASE	,						):




		"""simple docstring"""


		UpperCamelCase =						Path(_SCREAMING_SNAKE_CASE     ).open("w"	,						encoding="utf-8"     )
		UpperCamelCase =						str(_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE     ).to(_SCREAMING_SNAKE_CASE     )
		if fpaa:
				UpperCamelCase =						model.half()

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


def    a__       (       ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE=True     ):




		"""simple docstring"""


		UpperCamelCase =						argparse.ArgumentParser()
		parser.add_argument("model_name"	,						type=_SCREAMING_SNAKE_CASE	,						help="like facebook/bart-large-cnn,t5-base, etc."     )
		parser.add_argument("input_path"	,						type=_SCREAMING_SNAKE_CASE	,						help="like cnn_dm/test.source"     )
		parser.add_argument("save_path"	,						type=_SCREAMING_SNAKE_CASE	,						help="where to save summaries"     )
		parser.add_argument("--reference_path"	,						type=_SCREAMING_SNAKE_CASE	,						required=_SCREAMING_SNAKE_CASE	,						help="like cnn_dm/test.target"     )
		parser.add_argument("--score_path"	,						type=_SCREAMING_SNAKE_CASE	,						required=_SCREAMING_SNAKE_CASE	,						default="metrics.json"	,						help="where to save metrics"     )
		parser.add_argument("--device"	,						type=_SCREAMING_SNAKE_CASE	,						required=_SCREAMING_SNAKE_CASE	,						default=_SCREAMING_SNAKE_CASE	,						help="cuda, cuda:1, cpu etc."     )
		parser.add_argument(
		    "--prefix"	,						type=_SCREAMING_SNAKE_CASE	,						required=_SCREAMING_SNAKE_CASE	,						default=_SCREAMING_SNAKE_CASE	,						help="will be added to the begininng of src examples"     )
		parser.add_argument("--task"	,						type=_SCREAMING_SNAKE_CASE	,						default="summarization"	,						help="used for task_specific_params + metrics"     )
		parser.add_argument("--bs"	,						type=_SCREAMING_SNAKE_CASE	,						default=8	,						required=_SCREAMING_SNAKE_CASE	,						help="batch size"     )
		parser.add_argument(
		    "--n_obs"	,						type=_SCREAMING_SNAKE_CASE	,						default=-1	,						required=_SCREAMING_SNAKE_CASE	,						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=_SCREAMING_SNAKE_CASE	,						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(_SCREAMING_SNAKE_CASE     )
		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=_SCREAMING_SNAKE_CASE     )
		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(
		    _SCREAMING_SNAKE_CASE	,						args.save_path	,						args.model_name	,						batch_size=args.bs	,						device=args.device	,						fpaa=args.fpaa	,						task=args.task	,						prefix=args.prefix	,						**_SCREAMING_SNAKE_CASE	,						)

		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(_SCREAMING_SNAKE_CASE     )]
		UpperCamelCase =						score_fn(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )
		scores.update(_SCREAMING_SNAKE_CASE     )

		if args.dump_args:
				scores.update(_SCREAMING_SNAKE_CASE     )
		if args.info:
				UpperCamelCase =						args.info

		if verbose:
				print(_SCREAMING_SNAKE_CASE     )

		if args.score_path is not None:
				json.dump(_SCREAMING_SNAKE_CASE	,						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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__        =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''DebertaV2TokenizerFast''']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDebertaV2ForMaskedLM''',
						    '''TFDebertaV2ForQuestionAnswering''',
						    '''TFDebertaV2ForSequenceClassification''',
						    '''TFDebertaV2ForTokenClassification''',
						    '''TFDebertaV2Model''',
						    '''TFDebertaV2PreTrainedModel''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DebertaV2ForMaskedLM''',
						    '''DebertaV2ForMultipleChoice''',
						    '''DebertaV2ForQuestionAnswering''',
						    '''DebertaV2ForSequenceClassification''',
						    '''DebertaV2ForTokenClassification''',
						    '''DebertaV2Model''',
						    '''DebertaV2PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






