'''simple docstring'''

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=True		,					UpperCAmelCase="pt"					):
 lowercase__    :   str						= {'''add_prefix_space''': True} if isinstance(UpperCAmelCase		,					UpperCAmelCase					) and not line.startswith(''' '''					) else {}
 lowercase__    :   Any						= padding_side
 return tokenizer(
     [line]		,					max_length=UpperCAmelCase		,					padding='''max_length''' if pad_to_max_length else None		,					truncation=UpperCAmelCase		,					return_tensors=UpperCAmelCase		,					add_special_tokens=UpperCAmelCase		,					**UpperCAmelCase		,					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=None		,					):
 lowercase__    :   List[str]						= input_ids.ne(UpperCAmelCase					).any(dim=0					)
 if attention_mask is None:
  return input_ids[:, keep_column_mask]
 else:
  return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase="train"     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=""     ,       )  ->  List[str]:
        super().__init__()
        lowercase__    :   Optional[int]						= Path(__lowerCAmelCase			).joinpath(type_path + '''.source'''			)
        lowercase__    :   List[Any]						= Path(__lowerCAmelCase			).joinpath(type_path + '''.target'''			)
        lowercase__    :   str						= self.get_char_lens(self.src_file			)
        lowercase__    :   List[str]						= max_source_length
        lowercase__    :   Optional[Any]						= max_target_length
        assert min(self.src_lens			) > 0, F"""found empty line in {self.src_file}"""
        lowercase__    :   Any						= tokenizer
        lowercase__    :   Union[str, Any]						= prefix
        if n_obs is not None:
         lowercase__    :   Any						= self.src_lens[:n_obs]
        lowercase__    :   int						= src_lang
        lowercase__    :   Tuple						= tgt_lang

       def __len__(						self			)  ->  Optional[int]:
        return len(self.src_lens			)

       def __getitem__(						self     ,       __lowerCAmelCase			)  ->  Dict[str, torch.Tensor]:
        lowercase__    :   Dict						= index + 1  # linecache starts at 1
        lowercase__    :   int						= self.prefix + linecache.getline(str(self.src_file			)     ,       __lowerCAmelCase			).rstrip('''\n'''			)
        lowercase__    :   List[str]						= linecache.getline(str(self.tgt_file			)     ,       __lowerCAmelCase			).rstrip('''\n'''			)
        assert source_line, F"""empty source line for index {index}"""
        assert tgt_line, F"""empty tgt line for index {index}"""

        # Need to add eos token manually for T5
        if isinstance(self.tokenizer     ,       __lowerCAmelCase			):
         source_line += self.tokenizer.eos_token
         tgt_line += self.tokenizer.eos_token

        # Pad source and target to the right
        lowercase__    :   List[str]						= (
            self.tokenizer.question_encoder if isinstance(self.tokenizer     ,       __lowerCAmelCase			) else self.tokenizer
        )
        lowercase__    :   Dict						= self.tokenizer.generator if isinstance(self.tokenizer     ,       __lowerCAmelCase			) else self.tokenizer

        lowercase__    :   Union[str, Any]						= encode_line(__lowerCAmelCase     ,       __lowerCAmelCase     ,       self.max_source_length     ,       '''right'''			)
        lowercase__    :   Dict						= encode_line(__lowerCAmelCase     ,       __lowerCAmelCase     ,       self.max_target_length     ,       '''right'''			)

        lowercase__    :   Optional[int]						= source_inputs['''input_ids'''].squeeze()
        lowercase__    :   int						= target_inputs['''input_ids'''].squeeze()
        lowercase__    :   str						= source_inputs['''attention_mask'''].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }

       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase			)  ->  str:
        return [len(__lowerCAmelCase			) for x in Path(__lowerCAmelCase			).open().readlines()]


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Dict[str, torch.Tensor]:
        lowercase__    :   Union[str, Any]						= torch.stack([x['''input_ids'''] for x in batch]			)
        lowercase__    :   Optional[Any]						= torch.stack([x['''attention_mask'''] for x in batch]			)
        lowercase__    :   Dict						= torch.stack([x['''decoder_input_ids'''] for x in batch]			)
        lowercase__    :   str						= (
            self.tokenizer.generator.pad_token_id
            if isinstance(self.tokenizer     ,       __lowerCAmelCase			)
            else self.tokenizer.pad_token_id
        )
        lowercase__    :   Dict						= (
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer     ,       __lowerCAmelCase			)
            else self.tokenizer.pad_token_id
        )
        lowercase__    :   Optional[Any]						= trim_batch(__lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__			,							lowercase__    :   Dict						= trim_batch(__lowerCAmelCase     ,       __lowerCAmelCase     ,       attention_mask=__lowerCAmelCase			)
        lowercase__    :   str						= {
            '''input_ids''': source_ids,
            '''attention_mask''': source_mask,
            '''decoder_input_ids''': y,
        }
        return batch


__a:			str	       =       getLogger(__name__)
def       __UpperCamelCase	(       UpperCAmelCase					):
 return list(itertools.chain.from_iterable(UpperCAmelCase					)					)
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   str						= get_git_info()
 save_json(UpperCAmelCase		,					os.path.join(UpperCAmelCase		,					'''git_log.json'''					)					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=4		,					**UpperCAmelCase					):
 with open(UpperCAmelCase		,					'''w'''					) as f:
  json.dump(UpperCAmelCase		,					UpperCAmelCase		,					indent=UpperCAmelCase		,					**UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase					):
 with open(UpperCAmelCase					) as f:
  return json.load(UpperCAmelCase					)
def       __UpperCamelCase	(       ):
 lowercase__    :   List[Any]						= git.Repo(search_parent_directories=UpperCAmelCase					)
 lowercase__    :   Union[str, Any]						= {
     '''repo_id''': str(UpperCAmelCase					),
     '''repo_sha''': str(repo.head.object.hexsha					),
     '''repo_branch''': str(repo.active_branch					),
     '''hostname''': str(socket.gethostname()					),
 }
 return repo_infos
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 return list(map(UpperCAmelCase		,					UpperCAmelCase					)					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 with open(UpperCAmelCase		,					'''wb'''					) as f:
  return pickle.dump(UpperCAmelCase		,					UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase					):

 def remove_articles(UpperCAmelCase					):
  return re.sub(r'''\b(a|an|the)\b'''		,					''' '''		,					UpperCAmelCase					)

 def white_space_fix(UpperCAmelCase					):
  return " ".join(text.split()					)

 def remove_punc(UpperCAmelCase					):
  lowercase__    :   int						= set(string.punctuation					)
  return "".join(ch for ch in text if ch not in exclude					)

 def lower(UpperCAmelCase					):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase					)					)					)					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Optional[int]						= normalize_answer(UpperCAmelCase					).split()
 lowercase__    :   Optional[Any]						= normalize_answer(UpperCAmelCase					).split()
 lowercase__    :   Tuple						= Counter(UpperCAmelCase					) & Counter(UpperCAmelCase					)
 lowercase__    :   Optional[Any]						= sum(common.values()					)
 if num_same == 0:
  return 0
 lowercase__    :   Any						= 1.0 * num_same / len(UpperCAmelCase					)
 lowercase__    :   Optional[Any]						= 1.0 * num_same / len(UpperCAmelCase					)
 lowercase__    :   Union[str, Any]						= (2 * precision * recall) / (precision + recall)
 return fa
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 return normalize_answer(UpperCAmelCase					) == normalize_answer(UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 assert len(UpperCAmelCase					) == len(UpperCAmelCase					)
 lowercase__    :   Tuple						= 0
 for hypo, pred in zip(UpperCAmelCase		,					UpperCAmelCase					):
  em += exact_match_score(UpperCAmelCase		,					UpperCAmelCase					)
 if len(UpperCAmelCase					) > 0:
  em /= len(UpperCAmelCase					)
 return {"em": em}
def       __UpperCamelCase	(       UpperCAmelCase					):
 return model_prefix.startswith('''rag'''					)





def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Tuple						= {p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 lowercase__    :   Union[str, Any]						= '''dropout_rate'''
 for p in extra_params:
  if getattr(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
   if not hasattr(UpperCAmelCase		,					UpperCAmelCase					) and not hasattr(UpperCAmelCase		,					equivalent_param[p]					):
    logger.info('''config doesn\'t have a `{}` attribute'''.format(UpperCAmelCase					)					)
    delattr(UpperCAmelCase		,					UpperCAmelCase					)
    continue
   lowercase__    :   Dict						= p if hasattr(UpperCAmelCase		,					UpperCAmelCase					) else equivalent_param[p]
   setattr(UpperCAmelCase		,					UpperCAmelCase		,					getattr(UpperCAmelCase		,					UpperCAmelCase					)					)
   delattr(UpperCAmelCase		,					UpperCAmelCase					)
 return hparams, config


'''simple docstring'''

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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   None


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=0.9_9_9		,					UpperCAmelCase="cosine"		,					):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.exp(t * -1_2.0					)

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

 lowercase__    :   str						= []
 for i in range(UpperCAmelCase					):
  lowercase__    :   int						= i / num_diffusion_timesteps
  lowercase__    :   Tuple						= (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       UpperCAmelCase  (						a__   ,						a__ ):





       '''simple docstring'''



       @register_to_config
       def __init__(						self     ,       __lowerCAmelCase = 1000     ,       __lowerCAmelCase = "fixed_small_log"     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = "epsilon"     ,       __lowerCAmelCase = "squaredcos_cap_v2"     ,       )  ->  Optional[int]:
        if beta_schedule != "squaredcos_cap_v2":
         raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''			)

        lowercase__    :   Union[str, Any]						= betas_for_alpha_bar(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= 1.0 - self.betas
        lowercase__    :   int						= torch.cumprod(self.alphas     ,       dim=0			)
        lowercase__    :   str						= torch.tensor(1.0			)

        # standard deviation of the initial noise distribution
        lowercase__    :   Optional[Any]						= 1.0

        # setable values
        lowercase__    :   Optional[Any]						= None
        lowercase__    :   List[Any]						= torch.from_numpy(np.arange(0     ,       __lowerCAmelCase			)[::-1].copy()			)

        lowercase__    :   Tuple						= variance_type

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  torch.FloatTensor:
        return sample

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Optional[int]:
        lowercase__    :   List[str]						= num_inference_steps
        lowercase__    :   List[str]						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
        lowercase__    :   List[str]						= (np.arange(0     ,       __lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
        lowercase__    :   str						= torch.from_numpy(__lowerCAmelCase			).to(__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None			)  ->  Tuple:
        if prev_timestep is None:
         lowercase__    :   Any						= t - 1

        lowercase__    :   Any						= self.alphas_cumprod[t]
        lowercase__    :   List[Any]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   str						= 1 - alpha_prod_t
        lowercase__    :   int						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Tuple						= self.betas[t]
        else:
         lowercase__    :   Dict						= 1 - alpha_prod_t / alpha_prod_t_prev

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        lowercase__    :   Any						= beta_prod_t_prev / beta_prod_t * beta

        if variance_type is None:
         lowercase__    :   Union[str, Any]						= self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small_log":
         lowercase__    :   int						= torch.log(torch.clamp(__lowerCAmelCase     ,       min=1E-20			)			)
         lowercase__    :   Dict						= torch.exp(0.5 * variance			)
        elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         lowercase__    :   Union[str, Any]						= variance.log()
         lowercase__    :   Optional[int]						= beta.log()

         lowercase__    :   Tuple						= (predicted_variance + 1) / 2
         lowercase__    :   Dict						= frac * max_log + (1 - frac) * min_log

        return variance

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase = True     ,       )  ->  Union[UnCLIPSchedulerOutput, Tuple]:
        lowercase__    :   Tuple						= timestep

        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         lowercase__			,							lowercase__    :   str						= torch.split(__lowerCAmelCase     ,       sample.shape[1]     ,       dim=1			)
        else:
         lowercase__    :   Dict						= None

        # 1. compute alphas, betas
        if prev_timestep is None:
         lowercase__    :   int						= t - 1

        lowercase__    :   Optional[int]						= self.alphas_cumprod[t]
        lowercase__    :   Optional[int]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   Optional[int]						= 1 - alpha_prod_t
        lowercase__    :   List[Any]						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Optional[int]						= self.betas[t]
         lowercase__    :   Optional[Any]						= self.alphas[t]
        else:
         lowercase__    :   Any						= 1 - alpha_prod_t / alpha_prod_t_prev
         lowercase__    :   Optional[int]						= 1 - beta

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
         lowercase__    :   int						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
         lowercase__    :   Dict						= model_output
        else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
             ''' for the UnCLIPScheduler.'''			)

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         lowercase__    :   List[Any]						= torch.clamp(
             __lowerCAmelCase     ,       -self.config.clip_sample_range     ,       self.config.clip_sample_range			)

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   Optional[Any]						= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
        lowercase__    :   Tuple						= alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   List[Any]						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        lowercase__    :   List[Any]						= 0
        if t > 0:
         lowercase__    :   Dict						= randn_tensor(
             model_output.shape     ,       dtype=model_output.dtype     ,       generator=__lowerCAmelCase     ,       device=model_output.device			)

         lowercase__    :   Union[str, Any]						= self._get_variance(
             __lowerCAmelCase     ,       predicted_variance=__lowerCAmelCase     ,       prev_timestep=__lowerCAmelCase     ,       )

         if self.variance_type == "fixed_small_log":
          lowercase__    :   List[Any]						= variance
         elif self.variance_type == "learned_range":
          lowercase__    :   int						= (0.5 * variance).exp()
         else:
          raise ValueError(
              F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
              ''' for the UnCLIPScheduler.'''			)

         lowercase__    :   List[str]						= variance * variance_noise

        lowercase__    :   Union[str, Any]						= pred_prev_sample + variance

        if not return_dict:
         return (pred_prev_sample,)

        return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase     ,       pred_original_sample=__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  torch.FloatTensor:
        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
        lowercase__    :   Tuple						= self.alphas_cumprod.to(device=original_samples.device     ,       dtype=original_samples.dtype			)
        lowercase__    :   str						= timesteps.to(original_samples.device			)

        lowercase__    :   Union[str, Any]						= alphas_cumprod[timesteps] ** 0.5
        lowercase__    :   List[str]						= sqrt_alpha_prod.flatten()
        while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   List[str]						= sqrt_alpha_prod.unsqueeze(-1			)

        lowercase__    :   int						= (1 - alphas_cumprod[timesteps]) ** 0.5
        lowercase__    :   List[Any]						= sqrt_one_minus_alpha_prod.flatten()
        while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   int						= sqrt_one_minus_alpha_prod.unsqueeze(-1			)

        lowercase__    :   Optional[int]						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        return noisy_samples

