






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__     :      int             =			getLogger(__name__)


A__     :      str             =			'cuda' if torch.cuda.is_available() else 'cpu'






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 8		,       lowerCamelCase_ = DEFAULT_DEVICE		,       lowerCamelCase_=False		,       lowerCamelCase_="summarization"		,       lowerCamelCase_=None		,       **lowerCamelCase_		,       ):






				'''simple docstring'''
				lowercase__       			=      Path(lowerCamelCase_    ).open('''w'''		,       encoding='''utf-8'''    )
				lowercase__       			=      str(lowerCamelCase_    )
				lowercase__       			=      AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_    ).to(lowerCamelCase_    )
				if fpaa:
								lowercase__       			=      model.half()

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

				lowercase__       			=      time.time()
				# update config with task specific params
				use_task_specific_params(lowerCamelCase_		,       lowerCamelCase_    )
				if prefix is None:
								lowercase__       			=      prefix or getattr(model.config		,       '''prefix'''		,       ''''''    ) or ''''''
				for examples_chunk in tqdm(list(chunks(lowerCamelCase_		,       lowerCamelCase_    )    )    ):
								lowercase__       			=      [prefix + text for text in examples_chunk]
								lowercase__       			=      tokenizer(lowerCamelCase_		,       return_tensors='''pt'''		,       truncation=lowerCamelCase_		,       padding='''longest'''    ).to(lowerCamelCase_    )
								lowercase__       			=      model.generate(
								    input_ids=batch.input_ids		,       attention_mask=batch.attention_mask		,       **lowerCamelCase_		,       )
								lowercase__       			=      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()
				lowercase__       			=      int(time.time() - start_time    )  # seconds
				lowercase__       			=      len(lowerCamelCase_    )
				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      (						lowerCamelCase_=True    ):






				'''simple docstring'''
				lowercase__       			=      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
				lowercase__			,						lowercase__       			=      parser.parse_known_args()
				lowercase__       			=      parse_numeric_n_bool_cl_kwargs(lowerCamelCase_    )
				if parsed_args and verbose:
								print(F"""parsed the following generate kwargs: {parsed_args}"""    )
				with open(args.input_path    ) as f:
								lowercase__       			=      [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
				if args.n_obs > 0:
								lowercase__       			=      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."""    )
				lowercase__       			=      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
				lowercase__       			=      calculate_bleu if '''translation''' in args.task else calculate_rouge
				lowercase__       			=      [x.rstrip() for x in open(args.save_path    ).readlines()]
				lowercase__       			=      [x.rstrip() for x in open(args.reference_path    ).readlines()][: len(lowerCamelCase_    )]
				lowercase__       			=      score_fn(lowerCamelCase_		,       lowerCamelCase_    )
				scores.update(lowerCamelCase_    )

				if args.dump_args:
								scores.update(lowerCamelCase_    )
				if args.info:
								lowercase__       			=      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)












from typing import TYPE_CHECKING

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


A__     :      int             =			{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      List[Any]             =			[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      int             =			[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Tuple             =			['SamImageProcessor']


if TYPE_CHECKING:
							from .configuration_sam import (
							    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SamConfig,
							    SamMaskDecoderConfig,
							    SamPromptEncoderConfig,
							    SamVisionConfig,
							)
							from .processing_sam import SamProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_sam import SamImageProcessor

else:
							import sys

							A__     :      Dict             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




