


'''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 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase = 8    ,       UpperCAmelCase = DEFAULT_DEVICE    ,       UpperCAmelCase=False    ,       UpperCAmelCase="summarization"    ,       UpperCAmelCase=None    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	Optional[int]          =					Path(UpperCAmelCase     ).open("""w"""    ,       encoding="""utf-8"""     )
       snake_case__					:	Dict          =					str(UpperCAmelCase     )
       snake_case__					:	Tuple          =					AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase     ).to(UpperCAmelCase     )
       if fpaa:
              snake_case__					:	Optional[int]          =					model.half()

       snake_case__					:	Any          =					AutoTokenizer.from_pretrained(UpperCAmelCase     )
       logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

       snake_case__					:	Dict          =					time.time()
       # update config with task specific params
       use_task_specific_params(UpperCAmelCase    ,       UpperCAmelCase     )
       if prefix is None:
              snake_case__					:	List[str]          =					prefix or getattr(model.config    ,       """prefix"""    ,       """"""     ) or """"""
       for examples_chunk in tqdm(list(chunks(UpperCAmelCase    ,       UpperCAmelCase     )     )     ):
              snake_case__					:	int          =					[prefix + text for text in examples_chunk]
              snake_case__					:	str          =					tokenizer(UpperCAmelCase    ,       return_tensors="""pt"""    ,       truncation=UpperCAmelCase    ,       padding="""longest"""     ).to(UpperCAmelCase     )
              snake_case__					:	Any          =					model.generate(
                  input_ids=batch.input_ids    ,       attention_mask=batch.attention_mask    ,       **UpperCAmelCase    ,       )
              snake_case__					:	Optional[Any]          =					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()
       snake_case__					:	Dict          =					int(time.time() - start_time     )  # seconds
       snake_case__					:	Dict          =					len(UpperCAmelCase     )
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,       4     )}



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





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



def 				lowerCAmelCase__	(       UpperCAmelCase=True     ):






       """simple docstring"""





       snake_case__					:	Optional[Any]          =					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
       snake_case__       ,   snake_case__					:	int          =					parser.parse_known_args()
       snake_case__					:	Tuple          =					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:
              snake_case__					:	List[Any]          =					[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              snake_case__					:	Union[str, Any]          =					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."""     )
       snake_case__					:	List[str]          =					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
       snake_case__					:	Union[str, Any]          =					calculate_bleu if """translation""" in args.task else calculate_rouge
       snake_case__					:	List[str]          =					[x.rstrip() for x in open(args.save_path     ).readlines()]
       snake_case__					:	List[Any]          =					[x.rstrip() for x in open(args.reference_path     ).readlines()][: len(UpperCAmelCase     )]
       snake_case__					:	dict          =					score_fn(UpperCAmelCase    ,       UpperCAmelCase     )
       scores.update(UpperCAmelCase     )

       if args.dump_args:
              scores.update(UpperCAmelCase     )
       if args.info:
              snake_case__					:	Optional[int]          =					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 subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


lowerCAmelCase__  =     '1.5.0'

lowerCAmelCase__  =     ['vision-encoder-decoder']



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       if is_optimum_available():
              from optimum.version import __version__ as optimum_version

              snake_case__					:	Dict          =					version.parse(UpperCAmelCase     )
              if parsed_optimum_version < version.parse(UpperCAmelCase     ):
                     raise RuntimeError(
                         f"""transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You """
                         """can upgrade optimum by running: pip install -U optimum[exporters]"""     )
       else:
              raise RuntimeError(
                  """transformers.onnx requires optimum to run, you can install the library by running: pip install """
                  """optimum[exporters]"""     )
       snake_case__					:	Union[str, Any]          =					[
           sys.executable,
           """-m""",
           """optimum.exporters.onnx""",
           f"""--model {args.model}""",
           f"""--task {args.feature}""",
           f"""--framework {args.framework}""" if args.framework is not None else """""",
           f"""{args.output}""",
       ]
       snake_case__					:	Union[str, Any]          =					subprocess.Popen(""" """.join(UpperCAmelCase     )    ,       stdout=subprocess.PIPE    ,       shell=UpperCAmelCase     )
       proc.wait()

       logger.info(
           """The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as """
           """transformers.onnx is deprecated, and will be removed in v5. You can find more information here: """
           """https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""     )



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	str          =					args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""     )
       if not args.output.parent.exists():
              args.output.parent.mkdir(parents=UpperCAmelCase     )

       # Allocate the model
       snake_case__					:	Dict          =					FeaturesManager.get_model_from_feature(
           args.feature    ,       args.model    ,       framework=args.framework    ,       cache_dir=args.cache_dir     )

       snake_case__       ,   snake_case__					:	List[Any]          =					FeaturesManager.check_supported_model_or_raise(UpperCAmelCase    ,       feature=args.feature     )
       snake_case__					:	Any          =					model_onnx_config(model.config     )

       if model_kind in ENCODER_DECODER_MODELS:
              snake_case__					:	Optional[int]          =					model.get_encoder()
              snake_case__					:	Union[str, Any]          =					model.get_decoder()

              snake_case__					:	Optional[Any]          =					onnx_config.get_encoder_config(encoder_model.config     )
              snake_case__					:	Any          =					onnx_config.get_decoder_config(
                  encoder_model.config    ,       decoder_model.config    ,       feature=args.feature     )

              if args.opset is None:
                     snake_case__					:	List[Any]          =					max(encoder_onnx_config.default_onnx_opset    ,       decoder_onnx_config.default_onnx_opset     )

              if args.opset < min(encoder_onnx_config.default_onnx_opset    ,       decoder_onnx_config.default_onnx_opset     ):
                     raise ValueError(
                         f"""Opset {args.opset} is not sufficient to export {model_kind}. At least """
                         f""" {min(encoder_onnx_config.default_onnx_opset    ,       decoder_onnx_config.default_onnx_opset     )} is required."""     )

              snake_case__					:	Dict          =					AutoFeatureExtractor.from_pretrained(args.model     )

              snake_case__       ,   snake_case__					:	List[str]          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output.parent.joinpath("""encoder_model.onnx"""     )    ,       )

              validate_model_outputs(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.output.parent.joinpath("""encoder_model.onnx"""     )    ,       UpperCAmelCase    ,       args.atol if args.atol else encoder_onnx_config.atol_for_validation    ,       )

              snake_case__					:	List[Any]          =					AutoTokenizer.from_pretrained(args.model     )

              snake_case__       ,   snake_case__					:	Dict          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output.parent.joinpath("""decoder_model.onnx"""     )    ,       )

              validate_model_outputs(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.output.parent.joinpath("""decoder_model.onnx"""     )    ,       UpperCAmelCase    ,       args.atol if args.atol else decoder_onnx_config.atol_for_validation    ,       )
              logger.info(
                  f"""All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"     ).as_posix()},"""
                  f""" {args.output.parent.joinpath("decoder_model.onnx"     ).as_posix()}"""     )

       else:
              # Instantiate the appropriate preprocessor
              if args.preprocessor == "auto":
                     snake_case__					:	int          =					get_preprocessor(args.model     )
              elif args.preprocessor == "tokenizer":
                     snake_case__					:	List[str]          =					AutoTokenizer.from_pretrained(args.model     )
              elif args.preprocessor == "image_processor":
                     snake_case__					:	Optional[int]          =					AutoImageProcessor.from_pretrained(args.model     )
              elif args.preprocessor == "feature_extractor":
                     snake_case__					:	Tuple          =					AutoFeatureExtractor.from_pretrained(args.model     )
              elif args.preprocessor == "processor":
                     snake_case__					:	int          =					AutoProcessor.from_pretrained(args.model     )
              else:
                     raise ValueError(f"""Unknown preprocessor type '{args.preprocessor}'"""     )

              # Ensure the requested opset is sufficient
              if args.opset is None:
                     snake_case__					:	Dict          =					onnx_config.default_onnx_opset

              if args.opset < onnx_config.default_onnx_opset:
                     raise ValueError(
                         f"""Opset {args.opset} is not sufficient to export {model_kind}. """
                         f"""At least  {onnx_config.default_onnx_opset} is required."""     )

              snake_case__       ,   snake_case__					:	Dict          =					export(
                  UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.opset    ,       args.output    ,       )

              if args.atol is None:
                     snake_case__					:	int          =					onnx_config.atol_for_validation

              validate_model_outputs(UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       args.output    ,       UpperCAmelCase    ,       args.atol     )
              logger.info(f"""All good, model saved at: {args.output.as_posix()}"""     )
              warnings.warn(
                  """The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"""
                  """ using optimum.exporters.onnx in future. You can find more information here:"""
                  """ https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""    ,       UpperCAmelCase    ,       )



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	List[str]          =					ArgumentParser("""Hugging Face Transformers ONNX exporter"""     )
       parser.add_argument(
           """-m"""    ,       """--model"""    ,       type=UpperCAmelCase    ,       required=UpperCAmelCase    ,       help="""Model ID on huggingface.co or path on disk to load model from."""     )
       parser.add_argument(
           """--feature"""    ,       default="""default"""    ,       help="""The type of features to export the model with."""    ,       )
       parser.add_argument("""--opset"""    ,       type=UpperCAmelCase    ,       default=UpperCAmelCase    ,       help="""ONNX opset version to export the model with."""     )
       parser.add_argument(
           """--atol"""    ,       type=UpperCAmelCase    ,       default=UpperCAmelCase    ,       help="""Absolute difference tolerance when validating the model."""     )
       parser.add_argument(
           """--framework"""    ,       type=UpperCAmelCase    ,       choices=["""pt""", """tf"""]    ,       default=UpperCAmelCase    ,       help=(
               """The framework to use for the ONNX export."""
               """ If not provided, will attempt to use the local checkpoint's original framework"""
               """ or what is available in the environment."""
           )    ,       )
       parser.add_argument("""output"""    ,       type=UpperCAmelCase    ,       help="""Path indicating where to store generated ONNX model."""     )
       parser.add_argument("""--cache_dir"""    ,       type=UpperCAmelCase    ,       default=UpperCAmelCase    ,       help="""Path indicating where to store cache."""     )
       parser.add_argument(
           """--preprocessor"""    ,       type=UpperCAmelCase    ,       choices=["""auto""", """tokenizer""", """feature_extractor""", """image_processor""", """processor"""]    ,       default="""auto"""    ,       help="""Which type of preprocessor to use. 'auto' tries to automatically detect it."""    ,       )
       parser.add_argument(
           """--export_with_transformers"""    ,       action="""store_true"""    ,       help=(
               """Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be """
               """useful when exporting a model supported in transformers but not in optimum, otherwise it is not """
               """recommended."""
           )    ,       )

       snake_case__					:	str          =					parser.parse_args()
       if args.export_with_transformers or not is_optimum_available():
              export_with_transformers(UpperCAmelCase     )
       else:
              export_with_optimum(UpperCAmelCase     )


if __name__ == "__main__":
     lowerCAmelCase__  =     logging.get_logger('transformers.onnx')  # pylint: disable=invalid-name
     logger.setLevel(logging.INFO)
     main()




