





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any			,							snake_case__  :Union[str, Any]			,							snake_case__  :List[Any]			,							snake_case__  :Any			,							snake_case__  :Tuple=True			,							snake_case__  :List[Any]="pt"     )      ->						Tuple:
  _lowercase							= {'add_prefix_space': True} if isinstance(snake_case__			,							snake_case__     ) and not line.startswith(' '     ) else {}
  _lowercase							= padding_side
  return tokenizer(
      [line]			,							max_length=snake_case__			,							padding='max_length' if pad_to_max_length else None			,							truncation=snake_case__			,							return_tensors=snake_case__			,							add_special_tokens=snake_case__			,							**snake_case__			,							)

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[int]			,							snake_case__  :Dict			,							snake_case__  :Any=None			,							)      ->						Union[str, Any]:
  _lowercase							= input_ids.ne(snake_case__     ).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 				A_				(      lowercase_	):







       """simple docstring"""





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







       def __len__(    self	:	List[Any]  )						->					List[str]:
         return len(self.src_lens  )







       def __getitem__(    self	:	Optional[int]						,__A	:	Union[str, Any]  )						->					str:
         _lowercase							= index + 1  # linecache starts at 1
         _lowercase							= self.prefix + linecache.getline(str(self.src_file  )						,UpperCamelCase__  ).rstrip('\n'  )
         _lowercase							= linecache.getline(str(self.tgt_file  )						,UpperCamelCase__  ).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						,UpperCamelCase__  ):
           source_line += self.tokenizer.eos_token
           tgt_line += self.tokenizer.eos_token

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

         _lowercase							= encode_line(UpperCamelCase__						,UpperCamelCase__						,self.max_source_length						,'right'  )
         _lowercase							= encode_line(UpperCamelCase__						,UpperCamelCase__						,self.max_target_length						,'right'  )

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







       @staticmethod
       def    __UpperCAmelCase (    __A	:	Dict  )						->					str:
         return [len(UpperCamelCase__  ) for x in Path(UpperCamelCase__  ).open().readlines()]







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Any  )						->					Union[str, Any]:
         _lowercase							= torch.stack([x['input_ids'] for x in batch]  )
         _lowercase							= torch.stack([x['attention_mask'] for x in batch]  )
         _lowercase							= torch.stack([x['decoder_input_ids'] for x in batch]  )
         _lowercase							= (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer						,UpperCamelCase__  )
             else self.tokenizer.pad_token_id
         )
         _lowercase							= (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer						,UpperCamelCase__  )
             else self.tokenizer.pad_token_id
         )
         _lowercase							= trim_batch(UpperCamelCase__						,UpperCamelCase__  )
         _lowercase						,		_lowercase							= trim_batch(UpperCamelCase__						,UpperCamelCase__						,attention_mask=UpperCamelCase__  )
         _lowercase							= {
             'input_ids': source_ids,
             'attention_mask': source_mask,
             'decoder_input_ids': y,
         }
         return batch
snake_case     :   Any 				=       getLogger(__name__)

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[List]     )      ->						Optional[Any]:
  return list(itertools.chain.from_iterable(snake_case__     )     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str     )      ->						Optional[int]:
  _lowercase							= get_git_info()
  save_json(snake_case__			,							os.path.join(snake_case__			,							'git_log.json'     )     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :List[Any]			,							snake_case__  :Optional[int]=4			,							**snake_case__  :int     )      ->						Any:
  with open(snake_case__			,							'w'     ) as f:
    json.dump(snake_case__			,							snake_case__			,							indent=snake_case__			,							**snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Union[str, Any]:
  with open(snake_case__     ) as f:
    return json.load(snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  )      ->						Tuple:
  _lowercase							= git.Repo(search_parent_directories=snake_case__     )
  _lowercase							= {
      'repo_id': str(snake_case__     ),
      'repo_sha': str(repo.head.object.hexsha     ),
      'repo_branch': str(repo.active_branch     ),
      'hostname': str(socket.gethostname()     ),
  }
  return repo_infos

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Callable			,							snake_case__  :Iterable     )      ->						Tuple:
  return list(map(snake_case__			,							snake_case__     )     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]			,							snake_case__  :Dict     )      ->						int:
  with open(snake_case__			,							'wb'     ) as f:
    return pickle.dump(snake_case__			,							snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any     )      ->						Optional[Any]:

  def remove_articles(snake_case__  :List[str]     ):
    return re.sub(R'\b(a|an|the)\b'			,							' '			,							snake_case__     )

  def white_space_fix(snake_case__  :Union[str, Any]     ):
    return " ".join(text.split()     )

  def remove_punc(snake_case__  :List[Any]     ):
    _lowercase							= set(string.punctuation     )
    return "".join(ch for ch in text if ch not in exclude     )

  def lower(snake_case__  :Optional[Any]     ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(snake_case__     )     )     )     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]			,							snake_case__  :Optional[int]     )      ->						Dict:
  _lowercase							= normalize_answer(snake_case__     ).split()
  _lowercase							= normalize_answer(snake_case__     ).split()
  _lowercase							= Counter(snake_case__     ) & Counter(snake_case__     )
  _lowercase							= sum(common.values()     )
  if num_same == 0:
    return 0
  _lowercase							= 1.0 * num_same / len(snake_case__     )
  _lowercase							= 1.0 * num_same / len(snake_case__     )
  _lowercase							= (2 * precision * recall) / (precision + recall)
  return fa

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Union[str, Any]			,							snake_case__  :Union[str, Any]     )      ->						int:
  return normalize_answer(snake_case__     ) == normalize_answer(snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]			,							snake_case__  :List[str]     )      ->						Optional[int]:
  assert len(snake_case__     ) == len(snake_case__     )
  _lowercase							= 0
  for hypo, pred in zip(snake_case__			,							snake_case__     ):
    em += exact_match_score(snake_case__			,							snake_case__     )
  if len(snake_case__     ) > 0:
    em /= len(snake_case__     )
  return {"em": em}

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple     )      ->						List[str]:
  return model_prefix.startswith('rag'     )







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






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  (DEISMultistepScheduler,)
       SCREAMING_SNAKE_CASE_      : List[Any]      =  (('''num_inference_steps''', 2_5),)







       def    __UpperCAmelCase (    self	:	List[Any]						,**__A	:	int  )						->					List[Any]:
         _lowercase							= {
             'num_train_timesteps': 1000,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'solver_order': 2,
         }

         config.update(**__A  )
         return config







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	str=0						,**__A	:	Optional[int]  )						->					Tuple:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config(**__A  )
           _lowercase							= scheduler_class(**__A  )
           scheduler.set_timesteps(__A  )
           # copy over dummy past residuals
           _lowercase							= dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(__A  )
             _lowercase							= scheduler_class.from_pretrained(__A  )
             new_scheduler.set_timesteps(__A  )
             # copy over dummy past residuals
             _lowercase							= dummy_past_residuals[: new_scheduler.config.solver_order]

           _lowercase						,		_lowercase							= sample, sample
           for t in range(__A						,time_step + scheduler.config.solver_order + 1  ):
             _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
             _lowercase							= new_scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         pass







       def    __UpperCAmelCase (    self	:	int						,__A	:	Dict=0						,**__A	:	Optional[Any]  )						->					Dict:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )
           scheduler.set_timesteps(__A  )

           # copy over dummy past residuals (must be after setting timesteps)
           _lowercase							= dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(__A  )
             _lowercase							= scheduler_class.from_pretrained(__A  )
             # copy over dummy past residuals
             new_scheduler.set_timesteps(__A  )

             # copy over dummy past residual (must be after setting timesteps)
             _lowercase							= dummy_past_residuals[: new_scheduler.config.solver_order]

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= new_scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

           assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Tuple=None						,**__A	:	Optional[Any]  )						->					Tuple:
         if scheduler is None:
           _lowercase							= self.scheduler_classes[0]
           _lowercase							= self.get_scheduler_config(**__A  )
           _lowercase							= scheduler_class(**__A  )

         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(**__A  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter
         scheduler.set_timesteps(__A  )

         for i, t in enumerate(scheduler.timesteps  ):
           _lowercase							= model(__A						,__A  )
           _lowercase							= scheduler.step(__A						,__A						,__A  ).prev_sample

         return sample







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= dict(self.forward_default_kwargs  )

         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )

           _lowercase							= self.dummy_sample
           _lowercase							= 0.1 * sample

           if num_inference_steps is not None and hasattr(__A						,'set_timesteps'  ):
             scheduler.set_timesteps(__A  )
           elif num_inference_steps is not None and not hasattr(__A						,'set_timesteps'  ):
             _lowercase							= num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]
           _lowercase							= dummy_past_residuals[: scheduler.config.solver_order]

           _lowercase							= scheduler.timesteps[5]
           _lowercase							= scheduler.timesteps[6]

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

           self.assertEqual(output_a.shape						,sample.shape  )
           self.assertEqual(output_a.shape						,output_a.shape  )







       def    __UpperCAmelCase (    self	:	int  )						->					Optional[Any]:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         _lowercase							= DEISMultistepScheduler(**self.get_scheduler_config()  )
         _lowercase							= self.full_loop(scheduler=__A  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3

         _lowercase							= DPMSolverSinglestepScheduler.from_config(scheduler.config  )
         _lowercase							= DPMSolverMultistepScheduler.from_config(scheduler.config  )
         _lowercase							= UniPCMultistepScheduler.from_config(scheduler.config  )
         _lowercase							= DEISMultistepScheduler.from_config(scheduler.config  )

         _lowercase							= self.full_loop(scheduler=__A  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[int]:
         for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=__A  )







       def    __UpperCAmelCase (    self	:	int  )						->					str:
         self.check_over_configs(thresholding=__A  )
         for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=__A						,prediction_type=__A						,sample_max_value=__A						,algorithm_type='deis'						,solver_order=__A						,solver_type=__A						,)







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Tuple:
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=__A  )







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=__A						,solver_type=__A						,prediction_type=__A						,algorithm_type=__A						,)
                 _lowercase							= self.full_loop(
                     solver_order=__A						,solver_type=__A						,prediction_type=__A						,algorithm_type=__A						,)
                 assert not torch.isnan(__A  ).any(), "Samples have nan numbers"







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         self.check_over_configs(lower_order_final=__A  )
         self.check_over_configs(lower_order_final=__A  )







       def    __UpperCAmelCase (    self	:	str  )						->					Tuple:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
           self.check_over_forward(num_inference_steps=__A						,time_step=0  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         _lowercase							= self.full_loop()
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Any  )						->					List[str]:
         _lowercase							= self.full_loop(prediction_type='v_prediction'  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.091  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[Any]:
         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(thresholding=__A						,dynamic_thresholding_ratio=0  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter.half()
         scheduler.set_timesteps(__A  )

         for i, t in enumerate(scheduler.timesteps  ):
           _lowercase							= model(__A						,__A  )
           _lowercase							= scheduler.step(__A						,__A						,__A  ).prev_sample

         assert sample.dtype == torch.floataa