





"""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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[Any]       ,			_lowercase		:       Any       ,			_lowercase		:       Optional[Any]       ,			_lowercase		:       List[Any]       ,			_lowercase		:       Union[str, Any]=True       ,			_lowercase		:       Dict="pt"   )					->Optional[int]:






	'''simple docstring'''






	a :						int      =							{"add_prefix_space": True} if isinstance(_lowercase       ,			_lowercase   ) and not line.startswith(" "   ) else {}
	a :						List[Any]      =							padding_side
	return tokenizer(
	    [line]       ,			max_length=_lowercase       ,			padding="max_length" if pad_to_max_length else None       ,			truncation=_lowercase       ,			return_tensors=_lowercase       ,			add_special_tokens=_lowercase       ,			**_lowercase       ,			)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]       ,			_lowercase		:       Dict       ,			_lowercase		:       Dict=None       ,			)					->Union[str, Any]:






	'''simple docstring'''






	a :						str      =							input_ids.ne(_lowercase   ).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 			__UpperCamelCase							(		a__		):






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






							def __len__(  self )      ->					Dict:
								return len(self.src_lens )






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

								a :						Tuple      =							encode_line(lowerCAmelCase__ ,   lowerCAmelCase__ ,   self.max_source_length ,   "right" )
								a :						str      =							encode_line(lowerCAmelCase__ ,   lowerCAmelCase__ ,   self.max_target_length ,   "right" )

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






							@staticmethod
							def        __a  (  lowerCAmelCase__ )      ->					Optional[int]:
								return [len(lowerCAmelCase__ ) for x in Path(lowerCAmelCase__ ).open().readlines()]






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[List]   )					->Optional[Any]:






	'''simple docstring'''






	return list(itertools.chain.from_iterable(_lowercase   )   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str   )					->None:






	'''simple docstring'''






	a :						Optional[int]      =							get_git_info()
	save_json(_lowercase       ,			os.path.join(_lowercase       ,			"git_log.json"   )   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]       ,			_lowercase		:       Any       ,			_lowercase		:       List[str]=4       ,			**_lowercase		:       Optional[int]   )					->Union[str, Any]:






	'''simple docstring'''






	with open(_lowercase       ,			"w"   ) as f:
		json.dump(_lowercase       ,			_lowercase       ,			indent=_lowercase       ,			**_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]   )					->Tuple:






	'''simple docstring'''






	with open(_lowercase   ) as f:
		return json.load(_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      )					->Dict:






	'''simple docstring'''






	a :						Optional[Any]      =							git.Repo(search_parent_directories=_lowercase   )
	a :						int      =							{
	    "repo_id": str(_lowercase   ),
	    "repo_sha": str(repo.head.object.hexsha   ),
	    "repo_branch": str(repo.active_branch   ),
	    "hostname": str(socket.gethostname()   ),
	}
	return repo_infos






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Callable       ,			_lowercase		:       Iterable   )					->List:






	'''simple docstring'''






	return list(map(_lowercase       ,			_lowercase   )   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]       ,			_lowercase		:       Dict   )					->Optional[int]:






	'''simple docstring'''






	with open(_lowercase       ,			"wb"   ) as f:
		return pickle.dump(_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str   )					->Union[str, Any]:






	'''simple docstring'''






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

	def white_space_fix(_lowercase		:       Optional[int]   ):
		return " ".join(text.split()   )

	def remove_punc(_lowercase		:       int   ):
		a :						Union[str, Any]      =							set(string.punctuation   )
		return "".join(ch for ch in text if ch not in exclude   )

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

	return white_space_fix(remove_articles(remove_punc(lower(_lowercase   )   )   )   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       List[Any]   )					->str:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]       ,			_lowercase		:       int   )					->str:






	'''simple docstring'''






	return normalize_answer(_lowercase   ) == normalize_answer(_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       List[str]   )					->Dict:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]   )					->List[str]:






	'''simple docstring'''






	return model_prefix.startswith("rag"   )







def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       Union[str, Any]       ,			_lowercase		:       Tuple   )					->Optional[int]:






	'''simple docstring'''






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









"""simple docstring"""




import unittest

from transformers import DonutProcessor


a		:					Optional[int]	      =     '''naver-clova-ix/donut-base'''


class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					Dict:
								a :						Optional[Any]      =							DonutProcessor.from_pretrained(lowerCAmelCase__ )






							def        __a  (  self )      ->					List[Any]:
								a :						Optional[Any]      =							{
								    "name": "John Doe",
								    "age": "99",
								    "city": "Atlanta",
								    "state": "GA",
								    "zip": "30301",
								    "phone": "123-4567",
								    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
								}

								a :						Tuple      =							(
								    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
								    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
								    "<s_nicknames><s_nickname>Johnny</s_nickname>"
								    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
								)
								a :						List[Any]      =							self.processor.tokenajson(lowerCAmelCase__ )

								self.assertDictEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )


