
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class  _lowerCamelCase			:
     lowercase__				  =			42

     # setable values
     lowercase__				  =			42
     lowercase__				  =			42
     lowercase__				  =			42
     lowercase__				  =			None

     # running values
     lowercase__				  =			None

     @classmethod
     def 						__lowerCamelCase				(  cls							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            return cls(common=SCREAMING_SNAKE_CASE_							,       init_noise_sigma=SCREAMING_SNAKE_CASE_							,       timesteps=SCREAMING_SNAKE_CASE_							,       sigmas=SCREAMING_SNAKE_CASE_    )







@dataclass
class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			42







class  _lowerCamelCase			(lowerCamelCase							,					lowerCamelCase					):
     lowercase__				  =			[e.name for e in FlaxKarrasDiffusionSchedulers]

     lowercase__				  =			42

     @property
     def 						__lowerCamelCase				(  self    ):
            return True

     @register_to_config
     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = 1_000							,       SCREAMING_SNAKE_CASE_ = 0.0_0_0_1							,       SCREAMING_SNAKE_CASE_ = 0.0_2							,       SCREAMING_SNAKE_CASE_ = "linear"							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = "epsilon"							,       SCREAMING_SNAKE_CASE_ = jnp.floataa							,       ):
            __snake_case						       =   dtype

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_ = None    ):
            if common is None:
                   __snake_case						       =   CommonSchedulerState.create(self    )

            __snake_case						       =   jnp.arange(0							,       self.config.num_train_timesteps    ).round()[::-1]
            __snake_case						       =   ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

            # standard deviation of the initial noise distribution
            __snake_case						       =   sigmas.max()

            return LMSDiscreteSchedulerState.create(
                common=SCREAMING_SNAKE_CASE_							,       init_noise_sigma=SCREAMING_SNAKE_CASE_							,       timesteps=SCREAMING_SNAKE_CASE_							,       sigmas=SCREAMING_SNAKE_CASE_							,       )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            ((__snake_case)       ,       )						       =   jnp.where(state.timesteps == timestep							,       size=1    )
            __snake_case						       =   step_index[0]

            __snake_case						       =   state.sigmas[step_index]
            __snake_case						       =   sample / ((sigma**2 + 1) ** 0.5)
            return sample

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):

            def lms_derivative(SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   1.0
                   for k in range(SCREAMING_SNAKE_CASE_    ):
                          if current_order == k:
                                 continue
                          prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
                   return prod

            __snake_case						       =   integrate.quad(SCREAMING_SNAKE_CASE_							,       state.sigmas[t]							,       state.sigmas[t + 1]							,       epsrel=1E-4    )[0]

            return integrated_coeff

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = ()    ):
            __snake_case						       =   jnp.linspace(self.config.num_train_timesteps - 1							,       0							,       SCREAMING_SNAKE_CASE_							,       dtype=self.dtype    )

            __snake_case						       =   jnp.floor(SCREAMING_SNAKE_CASE_    ).astype(jnp.intaa    )
            __snake_case						       =   jnp.ceil(SCREAMING_SNAKE_CASE_    ).astype(jnp.intaa    )

            __snake_case						       =   jnp.mod(SCREAMING_SNAKE_CASE_							,       1.0    )

            __snake_case						       =   ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
            __snake_case						       =   (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
            __snake_case						       =   jnp.concatenate([sigmas, jnp.array([0.0]							,       dtype=self.dtype    )]    )

            __snake_case						       =   timesteps.astype(jnp.intaa    )

            # initial running values
            __snake_case						       =   jnp.zeros((0,) + shape							,       dtype=self.dtype    )

            return state.replace(
                timesteps=SCREAMING_SNAKE_CASE_							,       sigmas=SCREAMING_SNAKE_CASE_							,       num_inference_steps=SCREAMING_SNAKE_CASE_							,       derivatives=SCREAMING_SNAKE_CASE_							,       )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = 4							,       SCREAMING_SNAKE_CASE_ = True							,       ):
            if state.num_inference_steps is None:
                   raise ValueError(
                       'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'    )

            __snake_case						       =   state.sigmas[timestep]

            # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
            if self.config.prediction_type == "epsilon":
                   __snake_case						       =   sample - sigma * model_output
            elif self.config.prediction_type == "v_prediction":
                   # * c_out + input * c_skip
                   __snake_case						       =   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
            else:
                   raise ValueError(
                       f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

            # 2. Convert to an ODE derivative
            __snake_case						       =   (sample - pred_original_sample) / sigma
            __snake_case						       =   state.replace(derivatives=jnp.append(state.derivatives							,       SCREAMING_SNAKE_CASE_    )    )
            if len(state.derivatives    ) > order:
                   __snake_case						       =   state.replace(derivatives=jnp.delete(state.derivatives							,       0    )    )

            # 3. Compute linear multistep coefficients
            __snake_case						       =   min(timestep + 1							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   [self.get_lms_coefficient(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ) for curr_order in range(SCREAMING_SNAKE_CASE_    )]

            # 4. Compute previous sample based on the derivatives path
            __snake_case						       =   sample + sum(
                coeff * derivative for coeff, derivative in zip(SCREAMING_SNAKE_CASE_							,       reversed(state.derivatives    )    )    )

            if not return_dict:
                   return (prev_sample, state)

            return FlaxLMSSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_							,       state=SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   state.sigmas[timesteps].flatten()
            __snake_case						       =   broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE_							,       noise.shape    )

            __snake_case						       =   original_samples + noise * sigma

            return noisy_samples

     def __len__(  self    ):
            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


lowerCamelCase_				:							List[str]       =    getLogger(__name__)


lowerCamelCase_				:							Dict       =    "cuda" if torch.cuda.is_available() else "cpu"
def        __lowercase(							__snake_case				:		List[str]    ,__snake_case				:		str    ,__snake_case				:		str    ,__snake_case				:		int = 8    ,__snake_case				:		str = DEFAULT_DEVICE    ,__snake_case				:		str=False    ,__snake_case				:		Tuple="summarization"    ,__snake_case				:		List[str]=None    ,**__snake_case				:		Dict    ,)       ->   Dict:
       __snake_case						       =   Path(__snake_case					).open('w'    ,encoding='utf-8'					)
       __snake_case						       =   str(__snake_case					)
       __snake_case						       =   AutoModelForSeqaSeqLM.from_pretrained(__snake_case					).to(__snake_case					)
       if fpaa:
              __snake_case						       =   model.half()

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

       __snake_case						       =   time.time()
       # update config with task specific params
       use_task_specific_params(__snake_case    ,__snake_case					)
       if prefix is None:
              __snake_case						       =   prefix or getattr(model.config    ,'prefix'    ,''					) or ''
       for examples_chunk in tqdm(list(chunks(__snake_case    ,__snake_case					)					)					):
              __snake_case						       =   [prefix + text for text in examples_chunk]
              __snake_case						       =   tokenizer(__snake_case    ,return_tensors='pt'    ,truncation=__snake_case    ,padding='longest'					).to(__snake_case					)
              __snake_case						       =   model.generate(
                  input_ids=batch.input_ids    ,attention_mask=batch.attention_mask    ,**__snake_case    ,)
              __snake_case						       =   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()
       __snake_case						       =   int(time.time() - start_time					)  # seconds
       __snake_case						       =   len(__snake_case					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,4					)}
def        __lowercase(							)       ->   Optional[int]:
       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'					)
def        __lowercase(							__snake_case				:		Optional[int]=True					)       ->   Dict:
       __snake_case						       =   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
       __snake_case       ,       __snake_case						       =   parser.parse_known_args()
       __snake_case						       =   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:
              __snake_case						       =   [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              __snake_case						       =   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.'''					)
       __snake_case						       =   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
       __snake_case						       =   calculate_bleu if 'translation' in args.task else calculate_rouge
       __snake_case						       =   [x.rstrip() for x in open(args.save_path					).readlines()]
       __snake_case						       =   [x.rstrip() for x in open(args.reference_path					).readlines()][: len(__snake_case					)]
       __snake_case						       =   score_fn(__snake_case    ,__snake_case					)
       scores.update(__snake_case					)

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