




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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=True	,     lowerCamelCase="pt"   ):
				__magic_name__	:	Optional[Any]								={"""add_prefix_space""": True} if isinstance(lowerCamelCase	,     lowerCamelCase   ) and not line.startswith(""" """   ) else {}
				__magic_name__	:	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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=None	,     ):
				__magic_name__	:	Tuple								=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      (			UpperCamelCase__			):

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

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

						def __len__(		self   :Tuple ):
										'''simple docstring'''

										return len(self.src_lens )

						def __getitem__(		self   :Tuple    , __snake_case   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=index + 1  # linecache starts at 1
										__magic_name__	:	Tuple								=self.prefix + linecache.getline(str(self.src_file )    , __snake_case ).rstrip("""\n""" )
										__magic_name__	:	List[Any]								=linecache.getline(str(self.tgt_file )    , __snake_case ).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    , __snake_case ):
														source_line += self.tokenizer.eos_token
														tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										__magic_name__	:	List[Any]								=(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer    , __snake_case ) else self.tokenizer
										)
										__magic_name__	:	Optional[Any]								=self.tokenizer.generator if isinstance(self.tokenizer    , __snake_case ) else self.tokenizer

										__magic_name__	:	Optional[int]								=encode_line(__snake_case    , __snake_case    , self.max_source_length    , """right""" )
										__magic_name__	:	Optional[Any]								=encode_line(__snake_case    , __snake_case    , self.max_target_length    , """right""" )

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

						@staticmethod
						def     A__						(		__snake_case   :Optional[int] ):
										'''simple docstring'''

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


						def     A__						(		self   :Optional[Any]    , __snake_case   :Dict ):
										'''simple docstring'''

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


UpperCAmelCase_		:				List[Any]									=     getLogger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				return list(itertools.chain.from_iterable(lowerCamelCase   )   )




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								=get_git_info()
				save_json(lowerCamelCase	,     os.path.join(lowerCamelCase	,     """git_log.json"""   )   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=4	,     **lowerCamelCase   ):
				with open(lowerCamelCase	,     """w"""   ) as f:
								json.dump(lowerCamelCase	,     lowerCamelCase	,     indent=lowerCamelCase	,     **lowerCamelCase   )




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				with open(lowerCamelCase   ) as f:
								return json.load(lowerCamelCase   )




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Union[str, Any]								=git.Repo(search_parent_directories=lowerCamelCase   )
				__magic_name__	:	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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				return list(map(lowerCamelCase	,     lowerCamelCase   )   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				with open(lowerCamelCase	,     """wb"""   ) as f:
								return pickle.dump(lowerCamelCase	,     lowerCamelCase   )




def 		lowerCAmelCase_   (	lowerCamelCase   ):

				def remove_articles(lowerCamelCase   ):
								return re.sub(R"""\b(a|an|the)\b"""	,     """ """	,     lowerCamelCase   )

				def white_space_fix(lowerCamelCase   ):
								return " ".join(text.split()   )

				def remove_punc(lowerCamelCase   ):
								__magic_name__	:	Optional[int]								=set(string.punctuation   )
								return "".join(ch for ch in text if ch not in exclude   )

				def lower(lowerCamelCase   ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase   )   )   )   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	int								=normalize_answer(lowerCamelCase   ).split()
				__magic_name__	:	List[Any]								=normalize_answer(lowerCamelCase   ).split()
				__magic_name__	:	Dict								=Counter(lowerCamelCase   ) & Counter(lowerCamelCase   )
				__magic_name__	:	Tuple								=sum(common.values()   )
				if num_same == 0:
								return 0
				__magic_name__	:	int								=1.0 * num_same / len(lowerCamelCase   )
				__magic_name__	:	Union[str, Any]								=1.0 * num_same / len(lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=(2 * precision * recall) / (precision + recall)
				return fa




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				return normalize_answer(lowerCamelCase   ) == normalize_answer(lowerCamelCase   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				assert len(lowerCamelCase   ) == len(lowerCamelCase   )
				__magic_name__	:	Optional[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 		lowerCAmelCase_   (	lowerCamelCase   ):
				return model_prefix.startswith("""rag"""   )




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










from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Tuple									=     {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""t5"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

						def __init__(		self   :Optional[Any]    , __snake_case   :Optional[Any]=3_21_28    , __snake_case   :Optional[int]=5_12    , __snake_case   :str=64    , __snake_case   :int=20_48    , __snake_case   :Union[str, Any]=6    , __snake_case   :Union[str, Any]=None    , __snake_case   :Union[str, Any]=8    , __snake_case   :List[str]=32    , __snake_case   :Dict=1_28    , __snake_case   :List[Any]=0.1    , __snake_case   :int=1E-6    , __snake_case   :List[str]=1.0    , __snake_case   :Optional[int]="relu"    , __snake_case   :List[str]=True    , __snake_case   :Union[str, Any]=True    , __snake_case   :Union[str, Any]=0    , __snake_case   :str=1    , **__snake_case   :int    , ):
										'''simple docstring'''

										__magic_name__	:	str								=vocab_size
										__magic_name__	:	str								=d_model
										__magic_name__	:	Optional[Any]								=d_kv
										__magic_name__	:	int								=d_ff
										__magic_name__	:	Union[str, Any]								=num_layers
										__magic_name__	:	Tuple								=(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										__magic_name__	:	List[str]								=num_heads
										__magic_name__	:	Optional[Any]								=relative_attention_num_buckets
										__magic_name__	:	Union[str, Any]								=relative_attention_max_distance
										__magic_name__	:	Dict								=dropout_rate
										__magic_name__	:	Dict								=layer_norm_epsilon
										__magic_name__	:	List[str]								=initializer_factor
										__magic_name__	:	Any								=feed_forward_proj
										__magic_name__	:	Optional[int]								=use_cache

										__magic_name__	:	Tuple								=self.feed_forward_proj.split("""-""" )
										__magic_name__	:	int								=act_info[-1]
										__magic_name__	:	int								=act_info[0] == """gated"""

										if len(__snake_case ) > 1 and act_info[0] != "gated" or len(__snake_case ) > 2:
														raise ValueError(
														    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
														    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
														    """'gated-gelu' or 'relu'""" )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
														__magic_name__	:	str								="""gelu_new"""

										super().__init__(
										    pad_token_id=__snake_case    , eos_token_id=__snake_case    , is_encoder_decoder=__snake_case    , **__snake_case    , )




class 		__A      (			UpperCamelCase__			):

						@property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								={
										    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
										    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
										}
										if self.use_past:
														__magic_name__	:	List[str]								="""past_encoder_sequence + sequence"""
														__magic_name__	:	List[Any]								={0: """batch"""}
														__magic_name__	:	Union[str, Any]								={0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
														__magic_name__	:	int								={0: """batch""", 1: """decoder_sequence"""}
														__magic_name__	:	Tuple								={0: """batch""", 1: """decoder_sequence"""}

										if self.use_past:
														self.fill_with_past_key_values_(__snake_case    , direction="""inputs""" )

										return common_inputs


						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										return 13




