



"""simple docstring"""
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class     a		(     lowerCAmelCase_				):
  UpperCamelCase		: List[Any]     =    (DPMSolverSinglestepScheduler,)
  UpperCamelCase		: Tuple     =    (('num_inference_steps', 2_5),)



  def 		lowerCamelCase__				(  self       :    Optional[int]				,						**lowerCAmelCase       :    Optional[Any]						)   ->    Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       str   	={
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0_0_0_1,
         '''beta_end''': 0.0_2,
         '''beta_schedule''': '''linear''',
         '''solver_order''': 2,
         '''prediction_type''': '''epsilon''',
         '''thresholding''': False,
         '''sample_max_value''': 1.0,
         '''algorithm_type''': '''dpmsolver++''',
         '''solver_type''': '''midpoint''',
         '''lambda_min_clipped''': -float("""inf"""						),
         '''variance_type''': None,
     }

     config.update(**lowerCAmelCase						)
     return config



  def 		lowerCamelCase__				(  self       :    Optional[Any]				,						lowerCAmelCase       :    Optional[int]=0				,						**lowerCAmelCase       :    Dict						)   ->    Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Tuple   	=dict(self.forward_default_kwargs						)
     SCREAMING_SNAKE_CASE_:       str   	=kwargs.pop("""num_inference_steps"""				,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       Any   	=self.dummy_sample
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=0.1 * sample
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

     for scheduler_class in self.scheduler_classes:
        SCREAMING_SNAKE_CASE_:       str   	=self.get_scheduler_config(**lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=scheduler_class(**lowerCAmelCase						)
        scheduler.set_timesteps(lowerCAmelCase						)
        # copy over dummy past residuals
        SCREAMING_SNAKE_CASE_:       Any   	=dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:       Optional[Any]   	=scheduler_class.from_pretrained(lowerCAmelCase						)
           new_scheduler.set_timesteps(lowerCAmelCase						)
           # copy over dummy past residuals
           SCREAMING_SNAKE_CASE_:       Tuple   	=dummy_past_residuals[: new_scheduler.config.solver_order]

        SCREAMING_SNAKE_CASE_:       Optional[int]   	=sample, sample
        for t in range(lowerCAmelCase				,						time_step + scheduler.config.solver_order + 1						):
           SCREAMING_SNAKE_CASE_:       Tuple   	=scheduler.step(lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						**lowerCAmelCase						).prev_sample
           SCREAMING_SNAKE_CASE_:       Optional[Any]   	=new_scheduler.step(lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						**lowerCAmelCase						).prev_sample

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



  def 		lowerCamelCase__				(  self       :    List[str]						)   ->    Optional[int]:


     '''simple docstring'''




     pass



  def 		lowerCamelCase__				(  self       :    Optional[Any]				,						lowerCAmelCase       :    str=0				,						**lowerCAmelCase       :    List[Any]						)   ->    List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Dict   	=dict(self.forward_default_kwargs						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=kwargs.pop("""num_inference_steps"""				,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.dummy_sample
     SCREAMING_SNAKE_CASE_:       Dict   	=0.1 * sample
     SCREAMING_SNAKE_CASE_:       Dict   	=[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

     for scheduler_class in self.scheduler_classes:
        SCREAMING_SNAKE_CASE_:       int   	=self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_:       Dict   	=scheduler_class(**lowerCAmelCase						)
        scheduler.set_timesteps(lowerCAmelCase						)

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

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:       List[str]   	=scheduler_class.from_pretrained(lowerCAmelCase						)
           # copy over dummy past residuals
           new_scheduler.set_timesteps(lowerCAmelCase						)

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

        SCREAMING_SNAKE_CASE_:       List[Any]   	=scheduler.step(lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						**lowerCAmelCase						).prev_sample
        SCREAMING_SNAKE_CASE_:       Any   	=new_scheduler.step(lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase				,						**lowerCAmelCase						).prev_sample

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



  def 		lowerCamelCase__				(  self       :    Dict				,						lowerCAmelCase       :    Optional[int]=None				,						**lowerCAmelCase       :    Dict						)   ->    int:


     '''simple docstring'''




     if scheduler is None:
        SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_:       List[str]   	=self.get_scheduler_config(**lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:       Dict   	=scheduler_class(**lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE_:       int   	=self.get_scheduler_config(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       Any   	=scheduler_class(**lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:       int   	=10
     SCREAMING_SNAKE_CASE_:       Any   	=self.dummy_model()
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.dummy_sample_deter
     scheduler.set_timesteps(lowerCAmelCase						)

     for i, t in enumerate(scheduler.timesteps						):
        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=model(lowerCAmelCase				,						lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:       str   	=scheduler.step(lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase						).prev_sample

     return sample



  def 		lowerCamelCase__				(  self       :    int						)   ->    Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Dict   	=DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=50
     SCREAMING_SNAKE_CASE_:       Dict   	=self.dummy_model()
     SCREAMING_SNAKE_CASE_:       Any   	=self.dummy_sample_deter
     scheduler.set_timesteps(lowerCAmelCase						)

     # make sure that the first t is uneven
     for i, t in enumerate(scheduler.timesteps[3:]						):
        SCREAMING_SNAKE_CASE_:       Any   	=model(lowerCAmelCase				,						lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:       List[Any]   	=scheduler.step(lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase						).prev_sample

     SCREAMING_SNAKE_CASE_:       Optional[int]   	=torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.2_5_7_4						) < 1E-3



  def 		lowerCamelCase__				(  self       :    List[Any]						)   ->    List[str]:


     '''simple docstring'''




     for timesteps in [25, 50, 100, 999, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase						)



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Tuple   	=DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
     SCREAMING_SNAKE_CASE_:       List[Any]   	=self.full_loop(scheduler=lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.2_7_9_1						) < 1E-3

     SCREAMING_SNAKE_CASE_:       List[str]   	=DEISMultistepScheduler.from_config(scheduler.config						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=DPMSolverMultistepScheduler.from_config(scheduler.config						)
     SCREAMING_SNAKE_CASE_:       List[str]   	=UniPCMultistepScheduler.from_config(scheduler.config						)
     SCREAMING_SNAKE_CASE_:       Any   	=DPMSolverSinglestepScheduler.from_config(scheduler.config						)

     SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.full_loop(scheduler=lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       Dict   	=torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.2_7_9_1						) < 1E-3



  def 		lowerCamelCase__				(  self       :    str						)   ->    Any:


     '''simple docstring'''




     self.check_over_configs(thresholding=lowerCAmelCase						)
     for order in [1, 2, 3]:
        for solver_type in ["midpoint", "heun"]:
           for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=lowerCAmelCase				,						prediction_type=lowerCAmelCase				,						sample_max_value=lowerCAmelCase				,						algorithm_type="""dpmsolver++"""				,						solver_order=lowerCAmelCase				,						solver_type=lowerCAmelCase				,						)



  def 		lowerCamelCase__				(  self       :    str						)   ->    Any:


     '''simple docstring'''




     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase						)



  def 		lowerCamelCase__				(  self       :    Tuple						)   ->    Union[str, Any]:


     '''simple docstring'''




     for algorithm_type in ["dpmsolver", "dpmsolver++"]:
        for solver_type in ["midpoint", "heun"]:
           for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=lowerCAmelCase				,						solver_type=lowerCAmelCase				,						prediction_type=lowerCAmelCase				,						algorithm_type=lowerCAmelCase				,						)
                 SCREAMING_SNAKE_CASE_:       List[Any]   	=self.full_loop(
                     solver_order=lowerCAmelCase				,						solver_type=lowerCAmelCase				,						prediction_type=lowerCAmelCase				,						algorithm_type=lowerCAmelCase				,						)
                 assert not torch.isnan(lowerCAmelCase						).any(), "Samples have nan numbers"



  def 		lowerCamelCase__				(  self       :    Dict						)   ->    Any:


     '''simple docstring'''




     self.check_over_configs(lower_order_final=lowerCAmelCase						)
     self.check_over_configs(lower_order_final=lowerCAmelCase						)



  def 		lowerCamelCase__				(  self       :    Any						)   ->    List[str]:


     '''simple docstring'''




     self.check_over_configs(lambda_min_clipped=-float("""inf"""						)						)
     self.check_over_configs(lambda_min_clipped=-5.1						)



  def 		lowerCamelCase__				(  self       :    List[Any]						)   ->    List[Any]:


     '''simple docstring'''




     self.check_over_configs(variance_type=lowerCAmelCase						)
     self.check_over_configs(variance_type="""learned_range"""						)



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


     '''simple docstring'''




     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
        self.check_over_forward(num_inference_steps=lowerCAmelCase				,						time_step=0						)



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       int   	=self.full_loop()
     SCREAMING_SNAKE_CASE_:       int   	=torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.2_7_9_1						) < 1E-3



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=self.full_loop(use_karras_sigmas=lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       List[str]   	=torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.2_2_4_8						) < 1E-3



  def 		lowerCamelCase__				(  self       :    List[str]						)   ->    Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Dict   	=self.full_loop(prediction_type="""v_prediction"""						)
     SCREAMING_SNAKE_CASE_:       List[str]   	=torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.1_4_5_3						) < 1E-3



  def 		lowerCamelCase__				(  self       :    Optional[Any]						)   ->    Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       int   	=self.full_loop(prediction_type="""v_prediction"""				,						use_karras_sigmas=lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:       List[Any]   	=torch.mean(torch.abs(lowerCAmelCase						)						)

     assert abs(result_mean.item() - 0.0_6_4_9						) < 1E-3





  def 		lowerCamelCase__				(  self       :    Any						)   ->    Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE_:       Any   	=self.get_scheduler_config(thresholding=lowerCAmelCase				,						dynamic_thresholding_ratio=0						)
     SCREAMING_SNAKE_CASE_:       int   	=scheduler_class(**lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:       Any   	=10
     SCREAMING_SNAKE_CASE_:       List[str]   	=self.dummy_model()
     SCREAMING_SNAKE_CASE_:       Dict   	=self.dummy_sample_deter.half()
     scheduler.set_timesteps(lowerCAmelCase						)

     for i, t in enumerate(scheduler.timesteps						):
        SCREAMING_SNAKE_CASE_:       int   	=model(lowerCAmelCase				,						lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:       Optional[int]   	=scheduler.step(lowerCAmelCase				,						lowerCAmelCase				,						lowerCAmelCase						).prev_sample

     assert sample.dtype == torch.floataa








"""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      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Tuple=True							,		__lowerCamelCase				:    Dict="pt"	):
			"""simple docstring"""
			lowerCamelCase__		:		str			={'''add_prefix_space''': True} if isinstance(__lowerCamelCase							,		__lowerCamelCase	) and not line.startswith(''' '''	) else {}
			lowerCamelCase__		:		int			=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      snake_case__   (		__lowerCamelCase				:    str							,		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    Optional[int]=None							,		):
			"""simple docstring"""
			lowerCamelCase__		:		Any			=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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





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



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



					def __getitem__(       self							:  List[str],  lowerCamelCase							:  Optional[int]     )->					Dict[str, torch.Tensor]:
								lowerCamelCase__		:		List[Any]			=index + 1  # linecache starts at 1
								lowerCamelCase__		:		Optional[int]			=self.prefix + linecache.getline(str(self.src_file     ),  lowerCamelCase     ).rstrip('''\n'''     )
								lowerCamelCase__		:		Optional[Any]			=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
								lowerCamelCase__		:		Optional[int]			=(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer,  lowerCamelCase     ) else self.tokenizer
								)
								lowerCamelCase__		:		Tuple			=self.tokenizer.generator if isinstance(self.tokenizer,  lowerCamelCase     ) else self.tokenizer

								lowerCamelCase__		:		Optional[int]			=encode_line(lowerCamelCase,  lowerCamelCase,  self.max_source_length,  '''right'''     )
								lowerCamelCase__		:		str			=encode_line(lowerCamelCase,  lowerCamelCase,  self.max_target_length,  '''right'''     )

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



					@staticmethod
					def 		snake_case       (       lowerCamelCase							:  Union[str, Any]     )->					Optional[int]:
								return [len(lowerCamelCase     ) for x in Path(lowerCamelCase     ).open().readlines()]




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





_lowercase : Any      						=						getLogger(__name__)






def      snake_case__   (		__lowerCamelCase				:    List[List]	):
			"""simple docstring"""
			return list(itertools.chain.from_iterable(__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=get_git_info()
			save_json(__lowerCamelCase							,		os.path.join(__lowerCamelCase							,		'''git_log.json'''	)	)






def      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    Dict=4							,		**__lowerCamelCase				:    int	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''w'''	) as f:
						json.dump(__lowerCamelCase							,		__lowerCamelCase							,		indent=__lowerCamelCase							,		**__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			with open(__lowerCamelCase	) as f:
						return json.load(__lowerCamelCase	)






def      snake_case__   (		):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			=git.Repo(search_parent_directories=__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			={
			    '''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      snake_case__   (		__lowerCamelCase				:    Callable							,		__lowerCamelCase				:    Iterable	):
			"""simple docstring"""
			return list(map(__lowerCamelCase							,		__lowerCamelCase	)	)






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''wb'''	) as f:
						return pickle.dump(__lowerCamelCase							,		__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""

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

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

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

			def lower(__lowerCamelCase				:    Any	):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase	)	)	)	)






def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]			=normalize_answer(__lowerCamelCase	).split()
			lowerCamelCase__		:		List[str]			=normalize_answer(__lowerCamelCase	).split()
			lowerCamelCase__		:		Optional[int]			=Counter(__lowerCamelCase	) & Counter(__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			=sum(common.values()	)
			if num_same == 0:
						return 0
			lowerCamelCase__		:		Dict			=1.0 * num_same / len(__lowerCamelCase	)
			lowerCamelCase__		:		List[str]			=1.0 * num_same / len(__lowerCamelCase	)
			lowerCamelCase__		:		Union[str, Any]			=(2 * precision * recall) / (precision + recall)
			return fa






def      snake_case__   (		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			return normalize_answer(__lowerCamelCase	) == normalize_answer(__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			assert len(__lowerCamelCase	) == len(__lowerCamelCase	)
			lowerCamelCase__		:		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      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			return model_prefix.startswith('''rag'''	)






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    str	):
			"""simple docstring"""
			lowerCamelCase__		:		Any			={p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			lowerCamelCase__		:		Optional[int]			='''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
									lowerCamelCase__		:		List[Any]			=p if hasattr(__lowerCamelCase							,		__lowerCamelCase	) else equivalent_param[p]
									setattr(__lowerCamelCase							,		__lowerCamelCase							,		getattr(__lowerCamelCase							,		__lowerCamelCase	)	)
									delattr(__lowerCamelCase							,		__lowerCamelCase	)
			return hparams, config




