


'''simple docstring'''
import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
		from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
		from tensorflow.keras.optimizers import Adam
class    lowercase	(							tf.keras.optimizers.schedules.LearningRateSchedule					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_ = 1.0    ,   UpperCamelCase_ = None    ,   ):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__   :Dict												= initial_learning_rate
							UpperCamelCase__   :Optional[int]												= warmup_steps
							UpperCamelCase__   :str												= power
							UpperCamelCase__   :Dict												= decay_schedule_fn
							UpperCamelCase__   :List[Any]												= name




		def __call__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							with tf.name_scope(self.name or '''WarmUp'''							) as name:
												# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
												# learning rate will be `global_step/num_warmup_steps * init_lr`.
												UpperCamelCase__   :int												= tf.cast(UpperCamelCase_    ,   tf.floataa							)
												UpperCamelCase__   :int												= tf.cast(self.warmup_steps    ,   tf.floataa							)
												UpperCamelCase__   :Any												= global_step_float / warmup_steps_float
												UpperCamelCase__   :Union[str, Any]												= self.initial_learning_rate * tf.math.pow(UpperCamelCase_    ,   self.power							)
												return tf.cond(
												    global_step_float < warmup_steps_float    ,   lambda: warmup_learning_rate    ,   lambda: self.decay_schedule_fn(step - self.warmup_steps							)    ,   name=UpperCamelCase_    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return {
							    "initial_learning_rate": self.initial_learning_rate,
							    "decay_schedule_fn": self.decay_schedule_fn,
							    "warmup_steps": self.warmup_steps,
							    "power": self.power,
							    "name": self.name,
							}



def 			a      (			__a ,					__a ,					__a ,					__a = 0.0 ,					__a = 0.9 ,					__a = 0.9_9_9 ,					__a = 1e-8 ,					__a = None ,					__a = None ,					__a = 0.0 ,					__a = 1.0 ,					__a = None ,					)  ->	Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__   :Union[str, Any]												= tf.keras.optimizers.schedules.PolynomialDecay(
					    initial_learning_rate=__a ,					decay_steps=num_train_steps - num_warmup_steps ,					end_learning_rate=init_lr * min_lr_ratio ,					power=__a ,					)
					if num_warmup_steps:
										UpperCamelCase__   :int												= WarmUp(
										    initial_learning_rate=__a ,					decay_schedule_fn=__a ,					warmup_steps=__a ,					)
					if weight_decay_rate > 0.0:
										UpperCamelCase__   :int												= AdamWeightDecay(
										    learning_rate=__a ,					weight_decay_rate=__a ,					beta_a=__a ,					beta_a=__a ,					epsilon=__a ,					clipnorm=__a ,					global_clipnorm=__a ,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''] ,					include_in_weight_decay=__a ,					)
					else:
										UpperCamelCase__   :Optional[int]												= tf.keras.optimizers.Adam(
										    learning_rate=__a ,					beta_a=__a ,					beta_a=__a ,					epsilon=__a ,					clipnorm=__a ,					global_clipnorm=__a ,					)
					# We return the optimizer and the LR scheduler in order to better track the
					# evolution of the LR independently of the optimizer.
					return optimizer, lr_schedule
class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_ = 0.001    ,   UpperCamelCase_ = 0.9    ,   UpperCamelCase_ = 0.999    ,   UpperCamelCase_ = 1e-7    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = 0.0    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = "AdamWeightDecay"    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							)
							UpperCamelCase__   :Any												= weight_decay_rate
							UpperCamelCase__   :Union[str, Any]												= include_in_weight_decay
							UpperCamelCase__   :Optional[int]												= exclude_from_weight_decay




		@classmethod
		def     lowerCAmelCase__				(		cls    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= {'''WarmUp''': WarmUp}
							return super(UpperCamelCase_    ,   cls							).from_config(UpperCamelCase_    ,   custom_objects=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							super(UpperCamelCase_    ,   self							)._prepare_local(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							UpperCamelCase__   :List[str]												= tf.constant(
							    self.weight_decay_rate    ,   name='''adam_weight_decay_rate'''							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= self._do_use_weight_decay(var.name							)
							if do_decay:
												return var.assign_sub(
												    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']    ,   use_locking=self._use_locking    ,   )
							return tf.no_op()




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=None    ,   **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :List[str]												= list(zip(*UpperCamelCase_							)							)
							return super(UpperCamelCase_    ,   self							).apply_gradients(zip(UpperCamelCase_    ,   UpperCamelCase_							)    ,   name=UpperCamelCase_    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							if apply_state is None:
												return self._decayed_lr_t[var_dtype], {}

							UpperCamelCase__   :Dict												= apply_state or {}
							UpperCamelCase__   :str												= apply_state.get((var_device, var_dtype)							)
							if coefficients is None:
												UpperCamelCase__   :List[str]												= self._fallback_apply_state(UpperCamelCase_    ,   UpperCamelCase_							)
												UpperCamelCase__   :List[Any]												= coefficients

							return coefficients["lr_t"], {"apply_state": apply_state}




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= self._get_lr(var.device    ,   var.dtype.base_dtype    ,   UpperCamelCase_							)
							UpperCamelCase__   :Dict												= self._decay_weights_op(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							with tf.control_dependencies([decay]							):
												return super(UpperCamelCase_    ,   self							)._resource_apply_dense(UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= self._get_lr(var.device    ,   var.dtype.base_dtype    ,   UpperCamelCase_							)
							UpperCamelCase__   :Dict												= self._decay_weights_op(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							with tf.control_dependencies([decay]							):
												return super(UpperCamelCase_    ,   self							)._resource_apply_sparse(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= super().get_config()
							config.update({'''weight_decay_rate''': self.weight_decay_rate}							)
							return config




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if self.weight_decay_rate == 0:
												return False

							if self._include_in_weight_decay:
												for r in self._include_in_weight_decay:
																	if re.search(UpperCamelCase_    ,   UpperCamelCase_							) is not None:
																						return True

							if self._exclude_from_weight_decay:
												for r in self._exclude_from_weight_decay:
																	if re.search(UpperCamelCase_    ,   UpperCamelCase_							) is not None:
																						return False
							return True



class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= []
							UpperCamelCase__   :Tuple												= None




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self._accum_steps is None:
												UpperCamelCase__   :List[str]												= tf.Variable(
												    tf.constant(0    ,   dtype=tf.intaa							)    ,   trainable=UpperCamelCase_    ,   synchronization=tf.VariableSynchronization.ON_READ    ,   aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA    ,   )

							return self._accum_steps.value()




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if not self._gradients:
												raise ValueError('''The accumulator should be called first to initialize the gradients'''							)
							return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]




		def __call__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							if not self._gradients:
												UpperCamelCase__   :Tuple												= self.step  # Create the step variable.
												self._gradients.extend(
												    [
												        tf.Variable(
												            tf.zeros_like(UpperCamelCase_							)    ,   trainable=UpperCamelCase_    ,   synchronization=tf.VariableSynchronization.ON_READ    ,   aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA    ,   )
												        if gradient is not None
												        else gradient
												        for gradient in gradients
												    ]							)
							if len(UpperCamelCase_							) != len(self._gradients							):
												raise ValueError(F'''Expected {len(self._gradients							)} gradients, but got {len(UpperCamelCase_							)}'''							)

							for accum_gradient, gradient in zip(self._gradients    ,   UpperCamelCase_							):
												if accum_gradient is not None and gradient is not None:
																	accum_gradient.assign_add(UpperCamelCase_							)

							self._accum_steps.assign_add(1							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if not self._gradients:
												return
							self._accum_steps.assign(0							)
							for gradient in self._gradients:
												if gradient is not None:
																	gradient.assign(tf.zeros_like(UpperCamelCase_							)							)




"""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




