

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							None




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=0.999   ,      SCREAMING_SNAKE_CASE__      :     List[str]="cosine"   ,      )							->				Dict:





       '''simple docstring'''






       if alpha_transform_type == "cosine":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__      :     Tuple				):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__      :     int				):
                     return math.exp(t * -12.0				)

       else:
              raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

       A__   				=       []
       for i in range(SCREAMING_SNAKE_CASE__				):
              A__   				=       i / num_diffusion_timesteps
              A__   				=       (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__				) / alpha_bar_fn(SCREAMING_SNAKE_CASE__				)   ,      SCREAMING_SNAKE_CASE__				)				)
       return torch.tensor(SCREAMING_SNAKE_CASE__   ,      dtype=torch.floataa				)


class 			A						(							_UpperCAmelCase   ,							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							1


    @register_to_config
    def __init__(				self			:	Tuple,lowercase_			:	int = 1_0_0_0,lowercase_			:	float = 0.0_001,lowercase_			:	float = 0.02,lowercase_			:	str = "linear",lowercase_			:	Optional[Union[np.ndarray, List[float]]] = None,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	int = 0,lowercase_			:	str = "epsilon",lowercase_			:	float = 1.0,**lowercase_			:	Dict,)->   Dict:





           '''simple docstring'''







           if kwargs.get('set_alpha_to_one',lowercase_		) is not None:
                  A__   				=       (
                      'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
                  )
                  deprecate('set_alpha_to_one','1.0.0',lowercase_,standard_warn=lowercase_		)
                  A__   				=       kwargs['set_alpha_to_one']
           if trained_betas is not None:
                  A__   				=       torch.tensor(lowercase_,dtype=torch.floataa		)
           elif beta_schedule == "linear":
                  A__   				=       torch.linspace(lowercase_,lowercase_,lowercase_,dtype=torch.floataa		)
           elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  A__   				=       (
                      torch.linspace(beta_start**0.5,beta_end**0.5,lowercase_,dtype=torch.floataa		) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  A__   				=       betas_for_alpha_bar(lowercase_		)
           else:
                  raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'		)

           A__   				=       1.0 - self.betas
           A__   				=       torch.cumprod(self.alphas,dim=0		)

           # At every step in inverted ddim, we are looking into the next alphas_cumprod
           # For the final step, there is no next alphas_cumprod, and the index is out of bounds
           # `set_alpha_to_zero` decides whether we set this parameter simply to zero
           # in this case, self.step() just output the predicted noise
           # or whether we use the final alpha of the "non-previous" one.
           A__   				=       torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

           # standard deviation of the initial noise distribution
           A__   				=       1.0

           # setable values
           A__   				=       None
           A__   				=       torch.from_numpy(np.arange(0,lowercase_		).copy().astype(np.intaa		)		)


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	torch.FloatTensor,lowercase_			:	Optional[int] = None		)->   torch.FloatTensor:





           '''simple docstring'''







           return sample


    def 						snake_case__					(				self			:	Dict,lowercase_			:	int,lowercase_			:	Union[str, torch.device] = None		)->   str:





           '''simple docstring'''







           if num_inference_steps > self.config.num_train_timesteps:
                  raise ValueError(
                      F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                      F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                      F' maximal {self.config.num_train_timesteps} timesteps.'		)

           A__   				=       num_inference_steps
           A__   				=       self.config.num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           A__   				=       (np.arange(0,lowercase_		) * step_ratio).round().copy().astype(np.intaa		)
           A__   				=       torch.from_numpy(lowercase_		).to(lowercase_		)
           self.timesteps += self.config.steps_offset


    def 						snake_case__					(				self			:	Dict,lowercase_			:	torch.FloatTensor,lowercase_			:	int,lowercase_			:	torch.FloatTensor,lowercase_			:	float = 0.0,lowercase_			:	bool = False,lowercase_			:	Optional[torch.FloatTensor] = None,lowercase_			:	bool = True,)->   Union[DDIMSchedulerOutput, Tuple]:





           '''simple docstring'''







           A__   				=       timestep + self.config.num_train_timesteps // self.num_inference_steps

           # 2. compute alphas, betas
           # change original implementation to exactly match noise levels for analogous forward process
           A__   				=       self.alphas_cumprod[timestep]
           A__   				=       (
               self.alphas_cumprod[prev_timestep]
               if prev_timestep < self.config.num_train_timesteps
               else self.final_alpha_cumprod
           )

           A__   				=       1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
                  A__   				=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                  A__   				=       model_output
           elif self.config.prediction_type == "sample":
                  A__   				=       model_output
                  A__   				=       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
                  A__   				=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                  A__   				=       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
                  raise ValueError(
                      F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                      ' `v_prediction`'		)

           # 4. Clip or threshold "predicted x_0"
           if self.config.clip_sample:
                  A__   				=       pred_original_sample.clamp(
                      -self.config.clip_sample_range,self.config.clip_sample_range		)

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           A__   				=       (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           A__   				=       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
                  return (prev_sample, pred_original_sample)
           return DDIMSchedulerOutput(prev_sample=lowercase_,pred_original_sample=lowercase_		)


    def __len__(				self			:	Optional[int]		)->   Tuple:





           '''simple docstring'''







           return self.config.num_train_timesteps




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)

