



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 		a__			(		_UpperCamelCase			: Optional[int] ,_UpperCamelCase			: Optional[int]=0.999 ,_UpperCamelCase			: List[Any]="cosine" ,):
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(_UpperCamelCase			: Optional[int]							):
         return math.exp(t * -12.0							)

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""							)

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





class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  [e.name for e in KarrasDiffusionSchedulers]
 lowerCAmelCase__       					=  2




 @register_to_config
 def __init__(						self	,				__UpperCAmelCase = 1000	,				__UpperCAmelCase = 0.00_085	,				__UpperCAmelCase = 0.012	,				__UpperCAmelCase = "linear"	,				__UpperCAmelCase = None	,				__UpperCAmelCase = "epsilon"	,				__UpperCAmelCase = "linspace"	,				__UpperCAmelCase = 0	,				):



    '''simple docstring'''
    if trained_betas is not None:
       __lowerCamelCase													=    torch.tensor(__UpperCAmelCase	,				dtype=torch.floataa				)
    elif beta_schedule == "linear":
       __lowerCamelCase													=    torch.linspace(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				dtype=torch.floataa				)
    elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __lowerCamelCase													=    (
           torch.linspace(beta_start**0.5	,				beta_end**0.5	,				__UpperCAmelCase	,				dtype=torch.floataa				) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __lowerCamelCase													=    betas_for_alpha_bar(__UpperCAmelCase				)
    else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""				)

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

    #  set all values
    self.set_timesteps(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=None				):



    '''simple docstring'''
    if schedule_timesteps is None:
       __lowerCamelCase													=    self.timesteps

    __lowerCamelCase													=    (schedule_timesteps == timestep).nonzero()

    # The sigma index that is taken for the **very** first `step`
    # is always the second index (or the last index if there is only 1)
    # This way we can ensure we don't accidentally skip a sigma in
    # case we start in the middle of the denoising schedule (e.g. for image-to-image)
    if len(self._index_counter				) == 0:
       __lowerCamelCase													=    1 if len(__UpperCAmelCase				) > 1 else 0
    else:
       __lowerCamelCase													=    timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase				) else timestep
       __lowerCamelCase													=    self._index_counter[timestep_int]

    return indices[pos].item()




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # standard deviation of the initial noise distribution
    if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    self.index_for_timestep(__UpperCAmelCase				)

    if self.state_in_first_order:
       __lowerCamelCase													=    self.sigmas[step_index]
    else:
       __lowerCamelCase													=    self.sigmas_interpol[step_index]

    __lowerCamelCase													=    sample / ((sigma**2 + 1) ** 0.5)
    return sample




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				):



    '''simple docstring'''
    __lowerCamelCase													=    num_inference_steps

    __lowerCamelCase													=    num_train_timesteps or self.config.num_train_timesteps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
       __lowerCamelCase													=    np.linspace(0	,				num_train_timesteps - 1	,				__UpperCAmelCase	,				dtype=__UpperCAmelCase				)[::-1].copy()
    elif self.config.timestep_spacing == "leading":
       __lowerCamelCase													=    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
       __lowerCamelCase													=    (np.arange(0	,				__UpperCAmelCase				) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase				)
       timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
       __lowerCamelCase													=    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
       __lowerCamelCase													=    (np.arange(__UpperCAmelCase	,				0	,				-step_ratio				)).round().copy().astype(__UpperCAmelCase				)
       timesteps -= 1
    else:
       raise ValueError(
           F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""				)

    __lowerCamelCase													=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
    __lowerCamelCase													=    torch.from_numpy(np.log(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

    __lowerCamelCase													=    np.interp(__UpperCAmelCase	,				np.arange(0	,				len(__UpperCAmelCase				)				)	,				__UpperCAmelCase				)
    __lowerCamelCase													=    np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
    __lowerCamelCase													=    torch.from_numpy(__UpperCAmelCase				).to(device=__UpperCAmelCase				)

    # interpolate sigmas
    __lowerCamelCase													=    sigmas.log().lerp(sigmas.roll(1				).log()	,				0.5				).exp()

    __lowerCamelCase													=    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
    __lowerCamelCase													=    torch.cat(
        [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)

    if str(__UpperCAmelCase				).startswith('''mps'''				):
       # mps does not support float64
       __lowerCamelCase													=    torch.from_numpy(__UpperCAmelCase				).to(__UpperCAmelCase	,				dtype=torch.floataa				)
    else:
       __lowerCamelCase													=    torch.from_numpy(__UpperCAmelCase				).to(__UpperCAmelCase				)

    # interpolate timesteps
    __lowerCamelCase													=    self.sigma_to_t(__UpperCAmelCase				).to(__UpperCAmelCase	,				dtype=timesteps.dtype				)
    __lowerCamelCase													=    torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])	,				dim=-1				).flatten()

    __lowerCamelCase													=    torch.cat([timesteps[:1], interleaved_timesteps]				)

    __lowerCamelCase													=    None

    # for exp beta schedules, such as the one for `pipeline_shap_e.py`
    # we need an index counter
    __lowerCamelCase													=    defaultdict(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    # get log sigma
    __lowerCamelCase													=    sigma.log()

    # get distribution
    __lowerCamelCase													=    log_sigma - self.log_sigmas[:, None]

    # get sigmas range
    __lowerCamelCase													=    dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
    __lowerCamelCase													=    low_idx + 1

    __lowerCamelCase													=    self.log_sigmas[low_idx]
    __lowerCamelCase													=    self.log_sigmas[high_idx]

    # interpolate sigmas
    __lowerCamelCase													=    (low - log_sigma) / (low - high)
    __lowerCamelCase													=    w.clamp(0	,				1				)

    # transform interpolation to time range
    __lowerCamelCase													=    (1 - w) * low_idx + w * high_idx
    __lowerCamelCase													=    t.view(sigma.shape				)
    return t




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self.sample is None




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = True	,				):



    '''simple docstring'''
    __lowerCamelCase													=    self.index_for_timestep(__UpperCAmelCase				)

    # advance index counter by 1
    __lowerCamelCase													=    timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase				) else timestep
    self._index_counter[timestep_int] += 1

    if self.state_in_first_order:
       __lowerCamelCase													=    self.sigmas[step_index]
       __lowerCamelCase													=    self.sigmas_interpol[step_index + 1]
       __lowerCamelCase													=    self.sigmas[step_index + 1]
    else:
       # 2nd order / KDPM2's method
       __lowerCamelCase													=    self.sigmas[step_index - 1]
       __lowerCamelCase													=    self.sigmas_interpol[step_index]
       __lowerCamelCase													=    self.sigmas[step_index]

    # currently only gamma=0 is supported. This usually works best anyways.
    # We can support gamma in the future but then need to scale the timestep before
    # passing it to the model which requires a change in API
    __lowerCamelCase													=    0
    __lowerCamelCase													=    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
       __lowerCamelCase													=    sigma_hat if self.state_in_first_order else sigma_interpol
       __lowerCamelCase													=    sample - sigma_input * model_output
    elif self.config.prediction_type == "v_prediction":
       __lowerCamelCase													=    sigma_hat if self.state_in_first_order else sigma_interpol
       __lowerCamelCase													=    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
           sample / (sigma_input**2 + 1)
       )
    elif self.config.prediction_type == "sample":
       raise NotImplementedError('''prediction_type not implemented yet: sample'''				)
    else:
       raise ValueError(
           F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

    if self.state_in_first_order:
       # 2. Convert to an ODE derivative for 1st order
       __lowerCamelCase													=    (sample - pred_original_sample) / sigma_hat
       # 3. delta timestep
       __lowerCamelCase													=    sigma_interpol - sigma_hat

       # store for 2nd order step
       __lowerCamelCase													=    sample
    else:
       # DPM-Solver-2
       # 2. Convert to an ODE derivative for 2nd order
       __lowerCamelCase													=    (sample - pred_original_sample) / sigma_interpol

       # 3. delta timestep
       __lowerCamelCase													=    sigma_next - sigma_hat

       __lowerCamelCase													=    self.sample
       __lowerCamelCase													=    None

    __lowerCamelCase													=    sample + derivative * dt

    if not return_dict:
       return (prev_sample,)

    return SchedulerOutput(prev_sample=__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    __lowerCamelCase													=    self.sigmas.to(device=original_samples.device	,				dtype=original_samples.dtype				)
    if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase				):
       # mps does not support float64
       __lowerCamelCase													=    self.timesteps.to(original_samples.device	,				dtype=torch.floataa				)
       __lowerCamelCase													=    timesteps.to(original_samples.device	,				dtype=torch.floataa				)
    else:
       __lowerCamelCase													=    self.timesteps.to(original_samples.device				)
       __lowerCamelCase													=    timesteps.to(original_samples.device				)

    __lowerCamelCase													=    [self.index_for_timestep(__UpperCAmelCase	,				__UpperCAmelCase				) for t in timesteps]

    __lowerCamelCase													=    sigmas[step_indices].flatten()
    while len(sigma.shape				) < len(original_samples.shape				):
       __lowerCamelCase													=    sigma.unsqueeze(-1				)

    __lowerCamelCase													=    original_samples + noise * sigma
    return noisy_samples




 def __len__(						self				):



    '''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


a_			=					getLogger(__name__)


a_			=					"""cuda""" if torch.cuda.is_available() else """cpu"""





def 		a__			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: str ,_UpperCamelCase			: str ,_UpperCamelCase			: int = 8 ,_UpperCamelCase			: str = DEFAULT_DEVICE ,_UpperCamelCase			: Dict=False ,_UpperCamelCase			: Dict="summarization" ,_UpperCamelCase			: Optional[int]=None ,**_UpperCamelCase			: Dict ,):
   __lowerCamelCase													=    Path(_UpperCamelCase							).open('''w''' ,encoding='''utf-8'''							)
   __lowerCamelCase													=    str(_UpperCamelCase							)
   __lowerCamelCase													=    AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase							).to(_UpperCamelCase							)
   if fpaa:
      __lowerCamelCase													=    model.half()

   __lowerCamelCase													=    AutoTokenizer.from_pretrained(_UpperCamelCase							)
   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(_UpperCamelCase ,_UpperCamelCase							)
   if prefix is None:
      __lowerCamelCase													=    prefix or getattr(model.config ,'''prefix''' ,''''''							) or ''''''
   for examples_chunk in tqdm(list(chunks(_UpperCamelCase ,_UpperCamelCase							)							)							):
      __lowerCamelCase													=    [prefix + text for text in examples_chunk]
      __lowerCamelCase													=    tokenizer(_UpperCamelCase ,return_tensors='''pt''' ,truncation=_UpperCamelCase ,padding='''longest'''							).to(_UpperCamelCase							)
      __lowerCamelCase													=    model.generate(
          input_ids=batch.input_ids ,attention_mask=batch.attention_mask ,**_UpperCamelCase ,)
      __lowerCamelCase													=    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()
   __lowerCamelCase													=    int(time.time() - start_time							)  # seconds
   __lowerCamelCase													=    len(_UpperCamelCase							)
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,4							)}





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





def 		a__			(		_UpperCamelCase			: Union[str, Any]=True							):
   __lowerCamelCase													=    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
   __lowerCamelCase							,__lowerCamelCase													=    parser.parse_known_args()
   __lowerCamelCase													=    parse_numeric_n_bool_cl_kwargs(_UpperCamelCase							)
   if parsed_args and verbose:
      print(F"""parsed the following generate kwargs: {parsed_args}"""							)
   __lowerCamelCase													=    [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path							).readlines()]
   if args.n_obs > 0:
      __lowerCamelCase													=    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."""							)

   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'''							)

   __lowerCamelCase													=    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
   __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(_UpperCamelCase							)]
   __lowerCamelCase													=    score_fn(_UpperCamelCase ,_UpperCamelCase							)
   scores.update(_UpperCamelCase							)

   if args.dump_args:
      scores.update(_UpperCamelCase							)
   if args.info:
      __lowerCamelCase													=    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)






