

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							(IPNDMScheduler,)
    lowerCamelCase		     =							(('num_inference_steps', 50),)


    def 						snake_case__					(				self			:	List[Any],**lowercase_			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       {'num_train_timesteps': 1_0_0_0}
        config.update(**lowercase_		)
        return config


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	List[str]=0,**lowercase_			:	Tuple		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       dict(self.forward_default_kwargs		)
        A__   				=       kwargs.pop('num_inference_steps',lowercase_		)
        A__   				=       self.dummy_sample
        A__   				=       0.1 * sample
        A__   				=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
            A__   				=       self.get_scheduler_config(**lowercase_		)
            A__   				=       scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)
            # copy over dummy past residuals
            A__   				=       dummy_past_residuals[:]

            if time_step is None:
                A__   				=       scheduler.timesteps[len(scheduler.timesteps		) // 2]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_		)
                A__   				=       scheduler_class.from_pretrained(lowercase_		)
                new_scheduler.set_timesteps(lowercase_		)
                # copy over dummy past residuals
                A__   				=       dummy_past_residuals[:]

            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__   				=       new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__   				=       new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


    def 						snake_case__					(				self			:	str		)->   int:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	List[str]=0,**lowercase_			:	Union[str, Any]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       dict(self.forward_default_kwargs		)
        A__   				=       kwargs.pop('num_inference_steps',lowercase_		)
        A__   				=       self.dummy_sample
        A__   				=       0.1 * sample
        A__   				=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
            A__   				=       self.get_scheduler_config()
            A__   				=       scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)

            # copy over dummy past residuals (must be after setting timesteps)
            A__   				=       dummy_past_residuals[:]

            if time_step is None:
                A__   				=       scheduler.timesteps[len(scheduler.timesteps		) // 2]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_		)
                A__   				=       scheduler_class.from_pretrained(lowercase_		)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(lowercase_		)

                # copy over dummy past residual (must be after setting timesteps)
                A__   				=       dummy_past_residuals[:]

            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__   				=       new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__   				=       new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


    def 						snake_case__					(				self			:	str,**lowercase_			:	List[Any]		)->   int:





        '''simple docstring'''







        A__   				=       self.scheduler_classes[0]
        A__   				=       self.get_scheduler_config(**lowercase_		)
        A__   				=       scheduler_class(**lowercase_		)

        A__   				=       1_0
        A__   				=       self.dummy_model()
        A__   				=       self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__   				=       model(lowercase_,lowercase_		)
            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        for i, t in enumerate(scheduler.timesteps		):
            A__   				=       model(lowercase_,lowercase_		)
            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        return sample


    def 						snake_case__					(				self			:	List[str]		)->   List[str]:





        '''simple docstring'''







        A__   				=       dict(self.forward_default_kwargs		)

        A__   				=       kwargs.pop('num_inference_steps',lowercase_		)

        for scheduler_class in self.scheduler_classes:
            A__   				=       self.get_scheduler_config()
            A__   				=       scheduler_class(**lowercase_		)

            A__   				=       self.dummy_sample
            A__   				=       0.1 * sample

            if num_inference_steps is not None and hasattr(lowercase_,'set_timesteps'		):
                scheduler.set_timesteps(lowercase_		)
            elif num_inference_steps is not None and not hasattr(lowercase_,'set_timesteps'		):
                A__   				=       num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            A__   				=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
            A__   				=       dummy_past_residuals[:]

            A__   				=       scheduler.timesteps[5]
            A__   				=       scheduler.timesteps[6]

            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

            self.assertEqual(output_a.shape,sample.shape		)
            self.assertEqual(output_a.shape,output_a.shape		)

            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__   				=       scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

            self.assertEqual(output_a.shape,sample.shape		)
            self.assertEqual(output_a.shape,output_a.shape		)


    def 						snake_case__					(				self			:	List[str]		)->   Union[str, Any]:





        '''simple docstring'''







        for timesteps in [1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_,time_step=lowercase_		)


    def 						snake_case__					(				self			:	List[Any]		)->   List[Any]:





        '''simple docstring'''







        for t, num_inference_steps in zip([1, 5, 1_0],[1_0, 5_0, 1_0_0]		):
            self.check_over_forward(num_inference_steps=lowercase_,time_step=lowercase_		)


    def 						snake_case__					(				self			:	str		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       self.full_loop()
        A__   				=       torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 2_5_4_0_5_2_9		) < 1_0




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


lowercase_			=  getLogger(__name__)


lowercase_			=  "cuda" if torch.cuda.is_available() else "cpu"
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     int = 8   ,      SCREAMING_SNAKE_CASE__      :     str = DEFAULT_DEVICE   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=False   ,      SCREAMING_SNAKE_CASE__      :     Dict="summarization"   ,      SCREAMING_SNAKE_CASE__      :     Any=None   ,      **SCREAMING_SNAKE_CASE__      :     str   ,      )							->				Dict:





    '''simple docstring'''






    A__   				=       Path(SCREAMING_SNAKE_CASE__				).open('w'   ,      encoding='utf-8'				)
    A__   				=       str(SCREAMING_SNAKE_CASE__				)
    A__   				=       AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__				).to(SCREAMING_SNAKE_CASE__				)
    if fpaa:
        A__   				=       model.half()

    A__   				=       AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__				)
    logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'				)  # if this is wrong, check config.model_type.

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





    '''simple docstring'''






    return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]=True				)							->				Dict:





    '''simple docstring'''






    A__   				=       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
    A__				,						A__   				=       parser.parse_known_args()
    A__   				=       parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__				)
    if parsed_args and verbose:
        print(f'parsed the following generate kwargs: {parsed_args}'				)
    A__   				=       [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
    if args.n_obs > 0:
        A__   				=       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.'				)

    if args.device == "cpu" and args.fpaa:
        # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
        raise ValueError('Can\'t mix --fp16 and --device cpu'				)

    A__   				=       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
    A__   				=       calculate_bleu if 'translation' in args.task else calculate_rouge
    A__   				=       [x.rstrip() for x in open(args.save_path				).readlines()]
    A__   				=       [x.rstrip() for x in open(args.reference_path				).readlines()][: len(SCREAMING_SNAKE_CASE__				)]
    A__   				=       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:
        A__   				=       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)

