




'''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_   :	Tuple							   = getLogger(__name__)


a_   :	Union[str, Any]							   = """cuda""" if torch.cuda.is_available() else """cpu"""





def   a_							(   __snake_case							:    List[str]     , __snake_case							:    str     , __snake_case							:    str     , __snake_case							:    int = 8     , __snake_case							:    str = DEFAULT_DEVICE     , __snake_case							:    Dict=False     , __snake_case							:    int="summarization"     , __snake_case							:    Dict=None     , **__snake_case							:    Tuple     , )							->		Dict:



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

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





def   a_							(   )							->		List[str]:



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





def   a_							(   __snake_case							:    Any=True     )							->		Tuple:



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

       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(__snake_case     )]
       lowerCamelCase_							=score_fn(__snake_case     , __snake_case     )
       scores.update(__snake_case     )

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

       if verbose:
              print(__snake_case     )

       if args.score_path is not None:
              json.dump(__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'''




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=(HeunDiscreteScheduler,)
       lowercase			:					int   		=10






       def   lowercase__  (							self,	**lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_0_0_1,
                  '''beta_end''': 0.0_2,
                  '''beta_schedule''': '''linear''',
              }

              config.update(**lowerCAmelCase						)
              return config






       def   lowercase__  (							self						):



              """simple docstring"""







              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],	[0.0_0_0_2, 0.0_0_2, 0.0_2]						):
                     self.check_over_configs(beta_start=lowerCAmelCase,	beta_end=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for schedule in ["linear", "scaled_linear", "exp"]:
                     self.check_over_configs(beta_schedule=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for clip_sample_range in [1.0, 2.0, 3.0]:
                     self.check_over_configs(clip_sample_range=lowerCAmelCase,	clip_sample=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for prediction_type in ["epsilon", "v_prediction", "sample"]:
                     self.check_over_configs(prediction_type=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config(prediction_type='''v_prediction'''						)
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 4.6_934e-07						) < 1e-2
                     assert abs(result_mean.item() - 6.1_112e-10						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 4.693_428_650_170_972e-07						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if str(lowerCAmelCase						).startswith('''cpu'''						):
                     # The following sum varies between 148 and 156 on mps. Why?
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              elif str(lowerCAmelCase						).startswith('''mps'''						):
                     # Larger tolerance on mps
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-2
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase,	use_karras_sigmas=lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              assert abs(result_sum.item() - 0.0_0_0_1_5						) < 1e-2
              assert abs(result_mean.item() - 1.9_869_554_535_034_695e-07						) < 1e-2




