



"""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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=True, SCREAMING_SNAKE_CASE__="pt"	)			->      Any:

		'''simple docstring'''





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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None, )			->      Optional[int]:

		'''simple docstring'''





		a_     :      Optional[int] 						=     input_ids.ne(SCREAMING_SNAKE_CASE__	).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 			snake_case_						(						a_				):






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






							def __len__(   self      ):
									return len(self.src_lens      )






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

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

									a_     :      Any 						=     encode_line(a_			,     a_			,     self.max_source_length			,     "right"      )
									a_     :      Union[str, Any] 						=     encode_line(a_			,     a_			,     self.max_target_length			,     "right"      )

									a_     :      str 						=     source_inputs["input_ids"].squeeze()
									a_     :      Optional[Any] 						=     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 snake_case_  (   a_      ):
									return [len(a_      ) for x in Path(a_      ).open().readlines()]






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

SCREAMING_SNAKE_CASE_				= getLogger(__name__)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:

		'''simple docstring'''





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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:

		'''simple docstring'''





		a_     :      Any 						=     get_git_info()
		save_json(SCREAMING_SNAKE_CASE__, os.path.join(SCREAMING_SNAKE_CASE__, "git_log.json"	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=4, **SCREAMING_SNAKE_CASE__	)			->      List[str]:

		'''simple docstring'''





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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      str:

		'''simple docstring'''





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



def        lowerCAmelCase_						(							)			->      Dict:

		'''simple docstring'''





		a_     :      int 						=     git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE__	)
		a_     :      Dict 						=     {
		    "repo_id": str(SCREAMING_SNAKE_CASE__	),
		    "repo_sha": str(repo.head.object.hexsha	),
		    "repo_branch": str(repo.active_branch	),
		    "hostname": str(socket.gethostname()	),
		}
		return repo_infos



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List:

		'''simple docstring'''





		return list(map(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Union[str, Any]:

		'''simple docstring'''





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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:

		'''simple docstring'''





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

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

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

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

		return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__	)	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      List[str]:

		'''simple docstring'''





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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:

		'''simple docstring'''





		return normalize_answer(SCREAMING_SNAKE_CASE__	) == normalize_answer(SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:

		'''simple docstring'''





		assert len(SCREAMING_SNAKE_CASE__	) == len(SCREAMING_SNAKE_CASE__	)
		a_     :      List[str] 						=     0
		for hypo, pred in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
				em += exact_match_score(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) > 0:
				em /= len(SCREAMING_SNAKE_CASE__	)
		return {"em": em}



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Any:

		'''simple docstring'''





		return model_prefix.startswith("rag"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:

		'''simple docstring'''





		a_     :      int 						=     {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(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						if not hasattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and not hasattr(SCREAMING_SNAKE_CASE__, equivalent_param[p]	):
								logger.info("config doesn't have a `{}` attribute".format(SCREAMING_SNAKE_CASE__	)	)
								delattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
								continue
						a_     :      Optional[Any] 						=     p if hasattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) else equivalent_param[p]
						setattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, getattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)
						delattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		return hparams, config




"""simple docstring"""
import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					GPTSwaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_     :      Optional[int] 						=     GPTSwaTokenizer(a_			,     eos_token="<unk>"			,     bos_token="<unk>"			,     pad_token="<unk>"      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     "This is a test"
									a_     :      Tuple 						=     "This is a test"
									return input_text, output_text






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     "<s>"
									a_     :      Any 						=     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_      )			,     a_      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_      )			,     a_      )






							def snake_case_  (   self      ):
									a_     :      int 						=     list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0]			,     "<unk>"      )
									self.assertEqual(vocab_keys[1]			,     "<s>"      )
									self.assertEqual(vocab_keys[-1]			,     "j"      )
									self.assertEqual(len(a_      )			,     2_0_0_0      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     2_0_0_0      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     GPTSwaTokenizer(a_      )

									a_     :      str 						=     tokenizer.tokenize("This is a test"      )
									self.assertListEqual(a_			,     ["▁This", "▁is", "▁a", "▁t", "est"]      )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_      )			,     [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]      )

									a_     :      str 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]			,     )
									# fmt: on

									a_     :      List[str] 						=     tokenizer.convert_tokens_to_ids(a_      )
									self.assertListEqual(
									    a_			,     [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]			,     )

									a_     :      Optional[int] 						=     tokenizer.convert_ids_to_tokens(a_      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]      )
									# fmt: on






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     GPTSwaTokenizer(a_      )
									a_     :      List[Any] 						=     ["This is a test", "I was born in 92000, and this is falsé."]
									a_     :      Optional[Any] 						=     [
									    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
									    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
									]

									# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
									for text, expected_ids in zip(a_			,     a_      ):
											self.assertListEqual(tokenizer.encode_fast(a_      )			,     a_      )

									# Test that decode_fast returns the input text
									for text, token_ids in zip(a_			,     a_      ):
											self.assertEqual(tokenizer.decode_fast(a_      )			,     a_      )






							@slow
							def snake_case_  (   self      ):
									a_     :      Dict 						=     [
									    "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
									    "Hey there, how are you doing this fine day?",
									    "This is a text with a trailing spaces followed by a dot     .",
									    "Häj sväjs lillebrör! =)",
									    "Det är inget fel på Mr. Cool",
									]

									# fmt: off
									a_     :      Union[str, Any] 						=     {"input_ids": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
									# fmt: on
									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="AI-Sweden/gpt-sw3-126m"			,     sequences=a_			,     )