


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class     __lowercase    (				_lowerCAmelCase   ):



		"""simple docstring"""

		UpperCamelCase			: List[str]           =						(UnCLIPScheduler,)







		def  __A					(      self						,	**A     )   ->      Any:







					'''simple docstring'''







					lowerCamelCase  						=   {
					    "num_train_timesteps": 10_00,
					    "variance_type": "fixed_small_log",
					    "clip_sample": True,
					    "clip_sample_range": 1.0,
					    "prediction_type": "epsilon",
					}

					config.update(**A     )
					return config







		def  __A					(      self     )   ->      Any:







					'''simple docstring'''







					for timesteps in [1, 5, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=A     )







		def  __A					(      self     )   ->      List[str]:







					'''simple docstring'''







					for variance in ["fixed_small_log", "learned_range"]:
								self.check_over_configs(variance_type=A     )







		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







					for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=A     )







		def  __A					(      self     )   ->      Optional[int]:







					'''simple docstring'''







					for clip_sample_range in [1, 5, 10, 20]:
								self.check_over_configs(clip_sample_range=A     )







		def  __A					(      self     )   ->      Tuple:







					'''simple docstring'''







					for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(prediction_type=A     )







		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







					for time_step in [0, 5_00, 9_99]:
								for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
											if prev_timestep is not None and prev_timestep >= time_step:
														continue

											self.check_over_forward(time_step=A						,	prev_timestep=A     )







		def  __A					(      self     )   ->      List[str]:







					'''simple docstring'''







					lowerCamelCase  						=   self.scheduler_classes[0]
					lowerCamelCase  						=   self.get_scheduler_config(variance_type="""fixed_small_log"""     )
					lowerCamelCase  						=   scheduler_class(**A     )

					assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0549625     )     ) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9994987     )     ) < 1e-5







		def  __A					(      self     )   ->      Tuple:







					'''simple docstring'''







					lowerCamelCase  						=   self.scheduler_classes[0]
					lowerCamelCase  						=   self.get_scheduler_config(variance_type="""learned_range"""     )
					lowerCamelCase  						=   scheduler_class(**A     )

					lowerCamelCase  						=   0.5

					assert scheduler._get_variance(1						,	predicted_variance=A     ) - -10.1712790 < 1e-5
					assert scheduler._get_variance(4_87						,	predicted_variance=A     ) - -5.7998052 < 1e-5
					assert scheduler._get_variance(9_99						,	predicted_variance=A     ) - -0.0010011 < 1e-5







		def  __A					(      self     )   ->      Dict:







					'''simple docstring'''







					lowerCamelCase  						=   self.scheduler_classes[0]
					lowerCamelCase  						=   self.get_scheduler_config()
					lowerCamelCase  						=   scheduler_class(**A     )

					lowerCamelCase  						=   scheduler.timesteps

					lowerCamelCase  						=   self.dummy_model()
					lowerCamelCase  						=   self.dummy_sample_deter
					lowerCamelCase  						=   torch.manual_seed(0     )

					for i, t in enumerate(A     ):
								# 1. predict noise residual
								lowerCamelCase  						=   model(A						,	A     )

								# 2. predict previous mean of sample x_t-1
								lowerCamelCase  						=   scheduler.step(A						,	A						,	A						,	generator=A     ).prev_sample

								lowerCamelCase  						=   pred_prev_sample

					lowerCamelCase  						=   torch.sum(torch.abs(A     )     )
					lowerCamelCase  						=   torch.mean(torch.abs(A     )     )

					assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
					assert abs(result_mean.item() - 0.3284743     ) < 1e-3







		def  __A					(      self     )   ->      List[str]:







					'''simple docstring'''







					lowerCamelCase  						=   self.scheduler_classes[0]
					lowerCamelCase  						=   self.get_scheduler_config()
					lowerCamelCase  						=   scheduler_class(**A     )

					scheduler.set_timesteps(25     )

					lowerCamelCase  						=   scheduler.timesteps

					lowerCamelCase  						=   self.dummy_model()
					lowerCamelCase  						=   self.dummy_sample_deter
					lowerCamelCase  						=   torch.manual_seed(0     )

					for i, t in enumerate(A     ):
								# 1. predict noise residual
								lowerCamelCase  						=   model(A						,	A     )

								if i + 1 == timesteps.shape[0]:
											lowerCamelCase  						=   None
								else:
											lowerCamelCase  						=   timesteps[i + 1]

								# 2. predict previous mean of sample x_t-1
								lowerCamelCase  						=   scheduler.step(
								    A						,	A						,	A						,	prev_timestep=A						,	generator=A     ).prev_sample

								lowerCamelCase  						=   pred_prev_sample

					lowerCamelCase  						=   torch.sum(torch.abs(A     )     )
					lowerCamelCase  						=   torch.mean(torch.abs(A     )     )

					assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
					assert abs(result_mean.item() - 0.3362038     ) < 1e-3







		def  __A					(      self     )   ->      Tuple:







					'''simple docstring'''







					pass







		def  __A					(      self     )   ->      int:







					'''simple docstring'''







					pass








'''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_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					Dict=True	,__lowerCamelCase					:					List[Any]="pt" ):
   lowercase_			:Dict    			= {"add_prefix_space": True} if isinstance(__lowerCamelCase	,__lowerCamelCase ) and not line.startswith(" " ) else {}
   lowercase_			:str    			= padding_side
   return tokenizer(
       [line]	,max_length=__lowerCamelCase	,padding="max_length" if pad_to_max_length else None	,truncation=__lowerCamelCase	,return_tensors=__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,**__lowerCamelCase	,)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					Dict	,__lowerCamelCase					:					str=None	,):
   lowercase_			:Optional[int]    			= input_ids.ne(__lowerCamelCase ).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_   (				_lowerCAmelCase							):





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





        """simple docstring"""




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





     def __len__( self		:					Tuple						):





        """simple docstring"""




        return len(self.src_lens						)





     def __getitem__( self		:					str	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:Tuple    			= index + 1  # linecache starts at 1
        lowercase_			:Optional[Any]    			= self.prefix + linecache.getline(str(self.src_file						)	,      lowercase						).rstrip("\n"						)
        lowercase_			:List[str]    			= linecache.getline(str(self.tgt_file						)	,      lowercase						).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	,      lowercase						):
           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	,      lowercase						) else self.tokenizer
        )
        lowercase_			:int    			= self.tokenizer.generator if isinstance(self.tokenizer	,      lowercase						) else self.tokenizer

        lowercase_			:List[str]    			= encode_line(lowercase	,      lowercase	,      self.max_source_length	,      "right"						)
        lowercase_			:Any    			= encode_line(lowercase	,      lowercase	,      self.max_target_length	,      "right"						)

        lowercase_			:Dict    			= source_inputs["input_ids"].squeeze()
        lowercase_			:Tuple    			= target_inputs["input_ids"].squeeze()
        lowercase_			:Optional[int]    			= source_inputs["attention_mask"].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }





     @staticmethod
     def 							lowercase__      ( lowercase		:					Union[str, Any]						):





        """simple docstring"""




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







     def 							lowercase__      ( self		:					str	,      lowercase		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= torch.stack([x["input_ids"] for x in batch]						)
        lowercase_			:Dict    			= torch.stack([x["attention_mask"] for x in batch]						)
        lowercase_			:List[str]    			= torch.stack([x["decoder_input_ids"] for x in batch]						)
        lowercase_			:Any    			= (
            self.tokenizer.generator.pad_token_id
            if isinstance(self.tokenizer	,      lowercase						)
            else self.tokenizer.pad_token_id
        )
        lowercase_			:str    			= (
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer	,      lowercase						)
            else self.tokenizer.pad_token_id
        )
        lowercase_			:Union[str, Any]    			= trim_batch(lowercase	,      lowercase						)
        lowercase_			,							lowercase_			:Optional[Any]    			= trim_batch(lowercase	,      lowercase	,      attention_mask=lowercase						)
        lowercase_			:Tuple    			= {
            "input_ids": source_ids,
            "attention_mask": source_mask,
            "decoder_input_ids": y,
        }
        return batch







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

   def remove_articles(__lowerCamelCase					:					Optional[int] ):
      return re.sub(r"\b(a|an|the)\b"	," "	,__lowerCamelCase )

   def white_space_fix(__lowerCamelCase					:					Dict ):
      return " ".join(text.split() )

   def remove_punc(__lowerCamelCase					:					Optional[Any] ):
      lowercase_			:Any    			= set(string.punctuation )
      return "".join(ch for ch in text if ch not in exclude )

   def lower(__lowerCamelCase					:					List[Any] ):
      return text.lower()

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


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


