



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     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  42
			UpperCamelCase           =  None





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Any=0.999							,     _UpperCAmelCase       :     int="cosine"							,     )       ->				str:







			'''simple docstring'''
			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(_UpperCAmelCase       :     Tuple					):
									return math.exp(t * -12.0					)

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

			_UpperCAmelCase	 =     []
			for i in range(_UpperCAmelCase					):
						_UpperCAmelCase	 =     i / num_diffusion_timesteps
						_UpperCAmelCase	 =     (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					(    A       ,		A		):



			@register_to_config
			def __init__(		self     :						int				,	A     :						int = 10_00				,	A     :						str = "fixed_small_log"				,	A     :						bool = True				,	A     :						Optional[float] = 1.0				,	A     :						str = "epsilon"				,	A     :						str = "squaredcos_cap_v2"				,	)     ->					Any:



						"""simple docstring"""






						if beta_schedule != "squaredcos_cap_v2":
									raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

						_UpperCAmelCase	 =     betas_for_alpha_bar(A)

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

						# standard deviation of the initial noise distribution
						_UpperCAmelCase	 =     1.0

						# setable values
						_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     torch.from_numpy(np.arange(0				,	A)[::-1].copy())

						_UpperCAmelCase	 =     variance_type



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



						"""simple docstring"""






						return sample



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						int				,	A     :						Union[str, torch.device] = None)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     num_inference_steps
						_UpperCAmelCase	 =     (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
						_UpperCAmelCase	 =     (np.arange(0				,	A) * step_ratio).round()[::-1].copy().astype(np.intaa)
						_UpperCAmelCase	 =     torch.from_numpy(A).to(A)



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						List[str]				,	A     :						Dict=None				,	A     :						Optional[Any]=None				,	A     :						Union[str, Any]=None)     ->					List[str]:



						"""simple docstring"""






						if prev_timestep is None:
									_UpperCAmelCase	 =     t - 1

						_UpperCAmelCase	 =     self.alphas_cumprod[t]
						_UpperCAmelCase	 =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
						_UpperCAmelCase	 =     1 - alpha_prod_t
						_UpperCAmelCase	 =     1 - alpha_prod_t_prev

						if prev_timestep == t - 1:
									_UpperCAmelCase	 =     self.betas[t]
						else:
									_UpperCAmelCase	 =     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
						_UpperCAmelCase	 =     beta_prod_t_prev / beta_prod_t * beta

						if variance_type is None:
									_UpperCAmelCase	 =     self.config.variance_type

						# hacks - were probably added for training stability
						if variance_type == "fixed_small_log":
									_UpperCAmelCase	 =     torch.log(torch.clamp(A				,	min=1E-20))
									_UpperCAmelCase	 =     torch.exp(0.5 * variance)
						elif variance_type == "learned_range":
									# NOTE difference with DDPM scheduler
									_UpperCAmelCase	 =     variance.log()
									_UpperCAmelCase	 =     beta.log()

									_UpperCAmelCase	 =     (predicted_variance + 1) / 2
									_UpperCAmelCase	 =     frac * max_log + (1 - frac) * min_log

						return variance



			def 					_lowerCamelCase (		self     :						int				,	A     :						torch.FloatTensor				,	A     :						int				,	A     :						torch.FloatTensor				,	A     :						Optional[int] = None				,	A     :						Any=None				,	A     :						bool = True				,	)     ->					Union[UnCLIPSchedulerOutput, Tuple]:



						"""simple docstring"""






						_UpperCAmelCase	 =     timestep

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

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

						_UpperCAmelCase	 =     self.alphas_cumprod[t]
						_UpperCAmelCase	 =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
						_UpperCAmelCase	 =     1 - alpha_prod_t
						_UpperCAmelCase	 =     1 - alpha_prod_t_prev

						if prev_timestep == t - 1:
									_UpperCAmelCase	 =     self.betas[t]
									_UpperCAmelCase	 =     self.alphas[t]
						else:
									_UpperCAmelCase	 =     1 - alpha_prod_t / alpha_prod_t_prev
									_UpperCAmelCase	 =     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":
									_UpperCAmelCase	 =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif self.config.prediction_type == "sample":
									_UpperCAmelCase	 =     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:
									_UpperCAmelCase	 =     torch.clamp(
									    A				,	-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
						_UpperCAmelCase	 =     (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
						_UpperCAmelCase	 =     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
						_UpperCAmelCase	 =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						_UpperCAmelCase	 =     0
						if t > 0:
									_UpperCAmelCase	 =     randn_tensor(
									    model_output.shape				,	dtype=model_output.dtype				,	generator=A				,	device=model_output.device)

									_UpperCAmelCase	 =     self._get_variance(
									    A				,	predicted_variance=A				,	prev_timestep=A				,	)

									if self.variance_type == "fixed_small_log":
												_UpperCAmelCase	 =     variance
									elif self.variance_type == "learned_range":
												_UpperCAmelCase	 =     (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.')

									_UpperCAmelCase	 =     variance * variance_noise

						_UpperCAmelCase	 =     pred_prev_sample + variance

						if not return_dict:
									return (pred_prev_sample,)

						return UnCLIPSchedulerOutput(prev_sample=A				,	pred_original_sample=A)



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						torch.FloatTensor				,	A     :						torch.FloatTensor				,	A     :						torch.IntTensor				,	)     ->					torch.FloatTensor:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.alphas_cumprod.to(device=original_samples.device				,	dtype=original_samples.dtype)
						_UpperCAmelCase	 =     timesteps.to(original_samples.device)

						_UpperCAmelCase	 =     alphas_cumprod[timesteps] ** 0.5
						_UpperCAmelCase	 =     sqrt_alpha_prod.flatten()
						while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
									_UpperCAmelCase	 =     sqrt_alpha_prod.unsqueeze(-1)

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

						_UpperCAmelCase	 =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples





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 							A			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Dict=True							,     _UpperCAmelCase       :     Tuple="pt"					)       ->				List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {'add_prefix_space': True} if isinstance(_UpperCAmelCase							,     _UpperCAmelCase					) and not line.startswith(' '					) else {}
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Dict=None							,     )       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     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     __lowerCAmelCase					(    A		):



			def __init__(		self     :						Union[str, Any]				,	A     :						Union[str, Any]				,	A     :						Optional[int]				,	A     :						str				,	A     :						Union[str, Any]				,	A     :						int="train"				,	A     :						List[Any]=None				,	A     :						int=None				,	A     :						Tuple=None				,	A     :						str=""				,	)     ->					List[Any]:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     Path(A).joinpath(type_path + '.source')
						_UpperCAmelCase	 =     Path(A).joinpath(type_path + '.target')
						_UpperCAmelCase	 =     self.get_char_lens(self.src_file)
						_UpperCAmelCase	 =     max_source_length
						_UpperCAmelCase	 =     max_target_length
						assert min(self.src_lens) > 0, F"found empty line in {self.src_file}"
						_UpperCAmelCase	 =     tokenizer
						_UpperCAmelCase	 =     prefix
						if n_obs is not None:
									_UpperCAmelCase	 =     self.src_lens[:n_obs]
						_UpperCAmelCase	 =     src_lang
						_UpperCAmelCase	 =     tgt_lang



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



						"""simple docstring"""






						return len(self.src_lens)



			def __getitem__(		self     :						Any				,	A     :						Dict)     ->					Dict[str, torch.Tensor]:



						"""simple docstring"""






						_UpperCAmelCase	 =     index + 1  # linecache starts at 1
						_UpperCAmelCase	 =     self.prefix + linecache.getline(str(self.src_file)				,	A).rstrip('\n')
						_UpperCAmelCase	 =     linecache.getline(str(self.tgt_file)				,	A).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				,	A):
									source_line += self.tokenizer.eos_token
									tgt_line += self.tokenizer.eos_token

						# Pad source and target to the right
						_UpperCAmelCase	 =     (
						    self.tokenizer.question_encoder if isinstance(self.tokenizer				,	A) else self.tokenizer
						)
						_UpperCAmelCase	 =     self.tokenizer.generator if isinstance(self.tokenizer				,	A) else self.tokenizer

						_UpperCAmelCase	 =     encode_line(A				,	A				,	self.max_source_length				,	'right')
						_UpperCAmelCase	 =     encode_line(A				,	A				,	self.max_target_length				,	'right')

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



			@staticmethod
			def 					_lowerCamelCase (		A     :						str)     ->					Tuple:



						"""simple docstring"""






						return [len(A) for x in Path(A).open().readlines()]



			def 					_lowerCamelCase (		self     :						int				,	A     :						int)     ->					Dict[str, torch.Tensor]:



						"""simple docstring"""






						_UpperCAmelCase	 =     torch.stack([x['input_ids'] for x in batch])
						_UpperCAmelCase	 =     torch.stack([x['attention_mask'] for x in batch])
						_UpperCAmelCase	 =     torch.stack([x['decoder_input_ids'] for x in batch])
						_UpperCAmelCase	 =     (
						    self.tokenizer.generator.pad_token_id
						    if isinstance(self.tokenizer				,	A)
						    else self.tokenizer.pad_token_id
						)
						_UpperCAmelCase	 =     (
						    self.tokenizer.question_encoder.pad_token_id
						    if isinstance(self.tokenizer				,	A)
						    else self.tokenizer.pad_token_id
						)
						_UpperCAmelCase	 =     trim_batch(A				,	A)
						_UpperCAmelCase ,  _UpperCAmelCase	 =     trim_batch(A				,	A				,	attention_mask=A)
						_UpperCAmelCase	 =     {
						    'input_ids': source_ids,
						    'attention_mask': source_mask,
						    'decoder_input_ids': y,
						}
						return batch





UpperCAmelCase__     = getLogger(__name__)





def 							A			(  _UpperCAmelCase       :     List[List]					)       ->				Union[str, Any]:







			'''simple docstring'''
			return list(itertools.chain.from_iterable(_UpperCAmelCase					)					)





def 							A			(  _UpperCAmelCase       :     str					)       ->				None:







			'''simple docstring'''
			_UpperCAmelCase	 =     get_git_info()
			save_json(_UpperCAmelCase							,     os.path.join(_UpperCAmelCase							,     'git_log.json'					)					)





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Optional[int]=4							,     **_UpperCAmelCase       :     Optional[Any]					)       ->				Dict:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'w'					) as f:
						json.dump(_UpperCAmelCase							,     _UpperCAmelCase							,     indent=_UpperCAmelCase							,     **_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     List[str]					)       ->				Optional[Any]:







			'''simple docstring'''
			with open(_UpperCAmelCase					) as f:
						return json.load(_UpperCAmelCase					)





def 							A			(  )       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     git.Repo(search_parent_directories=_UpperCAmelCase					)
			_UpperCAmelCase	 =     {
			    '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 							A			(  _UpperCAmelCase       :     Callable							,     _UpperCAmelCase       :     Iterable					)       ->				List:







			'''simple docstring'''
			return list(map(_UpperCAmelCase							,     _UpperCAmelCase					)					)





def 							A			(  _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Union[str, Any]:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'wb'					) as f:
						return pickle.dump(_UpperCAmelCase							,     _UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     int					)       ->				str:







			'''simple docstring'''

			def remove_articles(_UpperCAmelCase       :     Optional[int]					):
						return re.sub(R'\b(a|an|the)\b'							,     ' '							,     _UpperCAmelCase					)

			def white_space_fix(_UpperCAmelCase       :     Optional[int]					):
						return " ".join(text.split()					)

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

			def lower(_UpperCAmelCase       :     str					):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase					)					)					)					)





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Any					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     normalize_answer(_UpperCAmelCase					).split()
			_UpperCAmelCase	 =     normalize_answer(_UpperCAmelCase					).split()
			_UpperCAmelCase	 =     Counter(_UpperCAmelCase					) & Counter(_UpperCAmelCase					)
			_UpperCAmelCase	 =     sum(common.values()					)
			if num_same == 0:
						return 0
			_UpperCAmelCase	 =     1.0 * num_same / len(_UpperCAmelCase					)
			_UpperCAmelCase	 =     1.0 * num_same / len(_UpperCAmelCase					)
			_UpperCAmelCase	 =     (2 * precision * recall) / (precision + recall)
			return fa





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     str					)       ->				List[Any]:







			'''simple docstring'''
			return normalize_answer(_UpperCAmelCase					) == normalize_answer(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     List[str]					)       ->				Dict:







			'''simple docstring'''
			assert len(_UpperCAmelCase					) == len(_UpperCAmelCase					)
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Union[str, Any]					)       ->				int:







			'''simple docstring'''
			return model_prefix.startswith('rag'					)





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     str					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			_UpperCAmelCase	 =     '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
									_UpperCAmelCase	 =     p if hasattr(_UpperCAmelCase							,     _UpperCAmelCase					) else equivalent_param[p]
									setattr(_UpperCAmelCase							,     _UpperCAmelCase							,     getattr(_UpperCAmelCase							,     _UpperCAmelCase					)					)
									delattr(_UpperCAmelCase							,     _UpperCAmelCase					)
			return hparams, config
