
"""simple docstring"""





import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english



@require_tokenizers
class __lowerCAmelCase					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					RoCBertTokenizer
			snake_case						   =					None
			snake_case						   =					False
			snake_case						   =					True
			snake_case						   =					filter_non_english




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			Tuple:







								"""simple docstring"""
								super().setUp()

								A_								=    ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
								A_								=    {}
								A_								=    {}
								for i, value in enumerate(_snake_case						):
													A_								=    i
													A_								=    i
								A_								=    os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["vocab_file"]						)
								A_								=    os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["word_shape_file"]						)
								A_								=    os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["word_pronunciation_file"]						)
								with open(self.vocab_file							, "w"							, encoding="utf-8"						) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)
								with open(self.word_shape_file							, "w"							, encoding="utf-8"						) as word_shape_writer:
													json.dump(_snake_case							, _snake_case							, ensure_ascii=_snake_case						)
								with open(self.word_pronunciation_file							, "w"							, encoding="utf-8"						) as word_pronunciation_writer:
													json.dump(_snake_case							, _snake_case							, ensure_ascii=_snake_case						)




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







								"""simple docstring"""
								A_								=    self.tokenizer_class(self.vocab_file							, self.word_shape_file							, self.word_pronunciation_file						)

								A_								=    tokenizer.tokenize("你好[SEP]你是谁"						)
								self.assertListEqual(_snake_case							, ["你", "好", "[SEP]", "你", "是", "谁"]						)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case						)							, [5, 6, 2, 5, 7, 8]						)
								self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_snake_case						)							, [5, 6, 2, 5, 7, 8]						)
								self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_snake_case						)							, [5, 6, 2, 5, 7, 8]						)




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







								"""simple docstring"""
								A_								=    RoCBertBasicTokenizer()

								self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"						)							, ["ah", "\u535A", "\u63A8", "zz"]						)




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







								"""simple docstring"""
								A_								=    RoCBertBasicTokenizer(do_lower_case=_snake_case						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "						)							, ["hello", "!", "how", "are", "you", "?"]						)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)							, ["hello"]						)




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







								"""simple docstring"""
								A_								=    RoCBertBasicTokenizer(do_lower_case=_snake_case							, strip_accents=_snake_case						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)							, ["hällo", "!", "how", "are", "you", "?"]						)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)							, ["h\u00E9llo"]						)




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







								"""simple docstring"""
								A_								=    RoCBertBasicTokenizer(do_lower_case=_snake_case							, strip_accents=_snake_case						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)							, ["hallo", "!", "how", "are", "you", "?"]						)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)							, ["hello"]						)




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







								"""simple docstring"""
								A_								=    RoCBertBasicTokenizer(do_lower_case=_snake_case						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)							, ["hallo", "!", "how", "are", "you", "?"]						)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)							, ["hello"]						)




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







								"""simple docstring"""
								A_								=    RoCBertBasicTokenizer(do_lower_case=_snake_case						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "						)							, ["HeLLo", "!", "how", "Are", "yoU", "?"]						)




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







								"""simple docstring"""
								A_								=    RoCBertBasicTokenizer(do_lower_case=_snake_case							, strip_accents=_snake_case						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)							, ["HäLLo", "!", "how", "Are", "yoU", "?"]						)




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







								"""simple docstring"""
								A_								=    RoCBertBasicTokenizer(do_lower_case=_snake_case							, strip_accents=_snake_case						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)							, ["HaLLo", "!", "how", "Are", "yoU", "?"]						)




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







								"""simple docstring"""
								A_								=    RoCBertBasicTokenizer(do_lower_case=_snake_case							, never_split=["[UNK]"]						)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"						)							, ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]						)




			def   lowerCamelCase__  (					self    :       str						)   ->			Any:







								"""simple docstring"""
								A_								=    ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

								A_								=    {}
								for i, token in enumerate(_snake_case						):
													A_								=    i
								A_								=    RoCBertWordpieceTokenizer(vocab=_snake_case							, unk_token="[UNK]"						)

								self.assertListEqual(tokenizer.tokenize(""						)							, []						)

								self.assertListEqual(tokenizer.tokenize("unwanted running"						)							, ["un", "##want", "##ed", "runn", "##ing"]						)

								self.assertListEqual(tokenizer.tokenize("unwantedX running"						)							, ["[UNK]", "runn", "##ing"]						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			Tuple:







								"""simple docstring"""
								self.assertTrue(_is_whitespace(" "						)						)
								self.assertTrue(_is_whitespace("\t"						)						)
								self.assertTrue(_is_whitespace("\r"						)						)
								self.assertTrue(_is_whitespace("\n"						)						)
								self.assertTrue(_is_whitespace("\u00A0"						)						)

								self.assertFalse(_is_whitespace("A"						)						)
								self.assertFalse(_is_whitespace("-"						)						)




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







								"""simple docstring"""
								self.assertTrue(_is_control("\u0005"						)						)

								self.assertFalse(_is_control("A"						)						)
								self.assertFalse(_is_control(" "						)						)
								self.assertFalse(_is_control("\t"						)						)
								self.assertFalse(_is_control("\r"						)						)




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







								"""simple docstring"""
								self.assertTrue(_is_punctuation("-"						)						)
								self.assertTrue(_is_punctuation("$"						)						)
								self.assertTrue(_is_punctuation("`"						)						)
								self.assertTrue(_is_punctuation("."						)						)

								self.assertFalse(_is_punctuation("A"						)						)
								self.assertFalse(_is_punctuation(" "						)						)




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







								"""simple docstring"""
								A_								=    self.get_tokenizer()

								# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
								self.assertListEqual([tokenizer.tokenize(_snake_case						) for t in ["Test", "\xad", "test"]]							, [["[UNK]"], [], ["[UNK]"]]						)

								if self.test_rust_tokenizer:
													A_								=    self.get_rust_tokenizer()
													self.assertListEqual(
													    [rust_tokenizer.tokenize(_snake_case						) for t in ["Test", "\xad", "test"]]							, [["[UNK]"], [], ["[UNK]"]]						)




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







								"""simple docstring"""
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
																		A_								=    self.rust_tokenizer_class.from_pretrained(_snake_case							, **_snake_case						)

																		A_								=    F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
																		A_								=    tokenizer_r.encode_plus(
																		    _snake_case							, return_attention_mask=_snake_case							, return_token_type_ids=_snake_case							, return_offsets_mapping=_snake_case							, add_special_tokens=_snake_case							, )

																		A_								=    tokenizer_r.do_lower_case if hasattr(_snake_case							, "do_lower_case"						) else False
																		A_								=    (
																		    [
																		        ((0, 0), tokenizer_r.cls_token),
																		        ((0, 1), "A"),
																		        ((1, 2), ","),
																		        ((3, 5), "na"),
																		        ((5, 6), "##ï"),
																		        ((6, 8), "##ve"),
																		        ((9, 15), tokenizer_r.mask_token),
																		        ((16, 21), "Allen"),
																		        ((21, 23), "##NL"),
																		        ((23, 24), "##P"),
																		        ((25, 33), "sentence"),
																		        ((33, 34), "."),
																		        ((0, 0), tokenizer_r.sep_token),
																		    ]
																		    if not do_lower_case
																		    else [
																		        ((0, 0), tokenizer_r.cls_token),
																		        ((0, 1), "a"),
																		        ((1, 2), ","),
																		        ((3, 8), "naive"),
																		        ((9, 15), tokenizer_r.mask_token),
																		        ((16, 21), "allen"),
																		        ((21, 23), "##nl"),
																		        ((23, 24), "##p"),
																		        ((25, 33), "sentence"),
																		        ((33, 34), "."),
																		        ((0, 0), tokenizer_r.sep_token),
																		    ]
																		)

																		self.assertEqual(
																		    [e[1] for e in expected_results]							, tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]						)						)
																		self.assertEqual([e[0] for e in expected_results]							, tokens["offset_mapping"]						)




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







								"""simple docstring"""
								A_								=    ["的", "人", "有"]
								A_								=    "".join(_snake_case						)
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
																		A_								=    True
																		A_								=    self.tokenizer_class.from_pretrained(_snake_case							, **_snake_case						)
																		A_								=    self.rust_tokenizer_class.from_pretrained(_snake_case							, **_snake_case						)

																		A_								=    tokenizer_p.encode(_snake_case							, add_special_tokens=_snake_case						)
																		A_								=    tokenizer_r.encode(_snake_case							, add_special_tokens=_snake_case						)

																		A_								=    tokenizer_r.convert_ids_to_tokens(_snake_case						)
																		A_								=    tokenizer_p.convert_ids_to_tokens(_snake_case						)

																		# it is expected that each Chinese character is not preceded by "##"
																		self.assertListEqual(_snake_case							, _snake_case						)
																		self.assertListEqual(_snake_case							, _snake_case						)

																		A_								=    False
																		A_								=    self.rust_tokenizer_class.from_pretrained(_snake_case							, **_snake_case						)
																		A_								=    self.tokenizer_class.from_pretrained(_snake_case							, **_snake_case						)

																		A_								=    tokenizer_r.encode(_snake_case							, add_special_tokens=_snake_case						)
																		A_								=    tokenizer_p.encode(_snake_case							, add_special_tokens=_snake_case						)

																		A_								=    tokenizer_r.convert_ids_to_tokens(_snake_case						)
																		A_								=    tokenizer_p.convert_ids_to_tokens(_snake_case						)

																		# it is expected that only the first Chinese character is not preceded by "##".
																		A_								=    [
																		    F'##{token}' if idx != 0 else token for idx, token in enumerate(_snake_case						)
																		]
																		self.assertListEqual(_snake_case							, _snake_case						)
																		self.assertListEqual(_snake_case							, _snake_case						)




			@slow
			def   lowerCamelCase__  (					self    :       int						)   ->			Any:







								"""simple docstring"""
								A_								=    self.tokenizer_class(self.vocab_file							, self.word_shape_file							, self.word_pronunciation_file						)

								A_								=    tokenizer.encode("你好"							, add_special_tokens=_snake_case						)
								A_								=    tokenizer.encode("你是谁"							, add_special_tokens=_snake_case						)

								A_								=    tokenizer.build_inputs_with_special_tokens(_snake_case						)
								A_								=    tokenizer.build_inputs_with_special_tokens(_snake_case							, _snake_case						)

								assert encoded_sentence == [1] + text + [2]
								assert encoded_pair == [1] + text + [2] + text_a + [2]






			def   lowerCamelCase__  (					self    :       Any						)   ->			int:







								"""simple docstring"""
								A_								=    self.get_tokenizers(do_lower_case=_snake_case						)
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'						):
																		A_								=    "你好，你是谁"
																		A_								=    tokenizer.tokenize(_snake_case						)
																		A_								=    tokenizer.convert_tokens_to_ids(_snake_case						)
																		A_								=    tokenizer.convert_tokens_to_shape_ids(_snake_case						)
																		A_								=    tokenizer.convert_tokens_to_pronunciation_ids(_snake_case						)
																		A_								=    tokenizer.prepare_for_model(
																		    _snake_case							, _snake_case							, _snake_case							, add_special_tokens=_snake_case						)

																		A_								=    tokenizer.encode_plus(_snake_case							, add_special_tokens=_snake_case						)

																		self.assertEqual(_snake_case							, _snake_case						)






"""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 				A_  (__a					, __a					, __a					, __a					, __a=True					, __a="pt"					):

					'''simple docstring'''





					A_								=    {"add_prefix_space": True} if isinstance(__a					, __a					) and not line.startswith(" "					) else {}
					A_								=    padding_side
					return tokenizer(
					    [line]					, max_length=__a					, padding="max_length" if pad_to_max_length else None					, truncation=__a					, return_tensors=__a					, add_special_tokens=__a					, **__a					, )
def 				A_  (__a					, __a					, __a=None					, ):

					'''simple docstring'''





					A_								=    input_ids.ne(__a					).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 __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







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







								"""simple docstring"""
								super().__init__()
								A_								=    Path(_snake_case						).joinpath(type_path + ".source"						)
								A_								=    Path(_snake_case						).joinpath(type_path + ".target"						)
								A_								=    self.get_char_lens(self.src_file						)
								A_								=    max_source_length
								A_								=    max_target_length
								assert min(self.src_lens						) > 0, F'found empty line in {self.src_file}'
								A_								=    tokenizer
								A_								=    prefix
								if n_obs is not None:
													A_								=    self.src_lens[:n_obs]
								A_								=    src_lang
								A_								=    tgt_lang




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







								"""simple docstring"""
								return len(self.src_lens						)




			def __getitem__(					self    :       str							, _snake_case    :       Optional[int]						)   ->			Dict[str, torch.Tensor]:







								"""simple docstring"""
								A_								=    index + 1  # linecache starts at 1
								A_								=    self.prefix + linecache.getline(str(self.src_file						)							, _snake_case						).rstrip("\n"						)
								A_								=    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
								A_								=    (
								    self.tokenizer.question_encoder if isinstance(self.tokenizer							, _snake_case						) else self.tokenizer
								)
								A_								=    self.tokenizer.generator if isinstance(self.tokenizer							, _snake_case						) else self.tokenizer

								A_								=    encode_line(_snake_case							, _snake_case							, self.max_source_length							, "right"						)
								A_								=    encode_line(_snake_case							, _snake_case							, self.max_target_length							, "right"						)

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




			@staticmethod
			def   lowerCamelCase__  (					_snake_case    :       List[Any]						)   ->			str:







								"""simple docstring"""
								return [len(_snake_case						) for x in Path(_snake_case						).open().readlines()]






			def   lowerCamelCase__  (					self    :       Any							, _snake_case    :       List[str]						)   ->			Dict[str, torch.Tensor]:







								"""simple docstring"""
								A_								=    torch.stack([x["input_ids"] for x in batch]						)
								A_								=    torch.stack([x["attention_mask"] for x in batch]						)
								A_								=    torch.stack([x["decoder_input_ids"] for x in batch]						)
								A_								=    (
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer							, _snake_case						)
								    else self.tokenizer.pad_token_id
								)
								A_								=    (
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer							, _snake_case						)
								    else self.tokenizer.pad_token_id
								)
								A_								=    trim_batch(_snake_case							, _snake_case						)
								A_			,     A_								=    trim_batch(_snake_case							, _snake_case							, attention_mask=_snake_case						)
								A_								=    {
								    "input_ids": source_ids,
								    "attention_mask": source_mask,
								    "decoder_input_ids": y,
								}
								return batch





UpperCamelCase_       :    Any						      =			getLogger(__name__)
def 				A_  (__a					):

					'''simple docstring'''





					return list(itertools.chain.from_iterable(__a					)					)
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    get_git_info()
					save_json(__a					, os.path.join(__a					, "git_log.json"					)					)
def 				A_  (__a					, __a					, __a=4					, **__a					):

					'''simple docstring'''





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

					'''simple docstring'''





					with open(__a					) as f:
										return json.load(__a					)
def 				A_  ():

					'''simple docstring'''





					A_								=    git.Repo(search_parent_directories=__a					)
					A_								=    {
					    "repo_id": str(__a					),
					    "repo_sha": str(repo.head.object.hexsha					),
					    "repo_branch": str(repo.active_branch					),
					    "hostname": str(socket.gethostname()					),
					}
					return repo_infos
def 				A_  (__a					, __a					):

					'''simple docstring'''





					return list(map(__a					, __a					)					)
def 				A_  (__a					, __a					):

					'''simple docstring'''





					with open(__a					, "wb"					) as f:
										return pickle.dump(__a					, __a					)
def 				A_  (__a					):

					'''simple docstring'''





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

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

					def remove_punc(__a					):
										A_								=    set(string.punctuation					)
										return "".join(ch for ch in text if ch not in exclude					)

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

					return white_space_fix(remove_articles(remove_punc(lower(__a					)					)					)					)
def 				A_  (__a					, __a					):

					'''simple docstring'''





					A_								=    normalize_answer(__a					).split()
					A_								=    normalize_answer(__a					).split()
					A_								=    Counter(__a					) & Counter(__a					)
					A_								=    sum(common.values()					)
					if num_same == 0:
										return 0
					A_								=    1.0 * num_same / len(__a					)
					A_								=    1.0 * num_same / len(__a					)
					A_								=    (2 * precision * recall) / (precision + recall)
					return fa
def 				A_  (__a					, __a					):

					'''simple docstring'''





					return normalize_answer(__a					) == normalize_answer(__a					)
def 				A_  (__a					, __a					):

					'''simple docstring'''





					assert len(__a					) == len(__a					)
					A_								=    0
					for hypo, pred in zip(__a					, __a					):
										em += exact_match_score(__a					, __a					)
					if len(__a					) > 0:
										em /= len(__a					)
					return {"em": em}
def 				A_  (__a					):

					'''simple docstring'''





					return model_prefix.startswith("rag"					)




def 				A_  (__a					, __a					, __a					):

					'''simple docstring'''





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




