


"""simple docstring"""


import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


a_       							=    "bert-base-cased"
a_       							=    "google/pegasus-xsum"
a_       							=    [" Sam ate lunch today.", "Sams lunch ingredients."]
a_       							=    ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
a_       							=    "patrickvonplaten/t5-tiny-random"
a_       							=    "sshleifer/bart-tiny-random"
a_       							=    "sshleifer/tiny-mbart"
a_       							=    "sshleifer/tiny-marian-en-de"






def      a__ (				__lowercase     ,			__lowercase   )	->	Optional[int]:
				_A					  =			"\n".join(__lowercase   )
				Path(__lowercase   ).open("w"   ).writelines(__lowercase   )






def      a__ (				__lowercase   )	->	Union[str, Any]:
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(__lowercase     ,			f"""{split}.source"""   )     ,			__lowercase   )
								_dump_articles(os.path.join(__lowercase     ,			f"""{split}.target"""   )     ,			__lowercase   )
				return tmp_dir




class snake_case      (  _UpperCamelCase):







		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]    ,   )
		@slow
		def  a_ (					self      :       Tuple    ,   a__      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						_A					  =			AutoTokenizer.from_pretrained(a__		)
						_A					  =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
						_A					  =			max(len(tokenizer.encode(a__		)		) for a in ARTICLES		)
						_A					  =			max(len(tokenizer.encode(a__		)		) for a in SUMMARIES		)
						_A					  =			4
						_A					  =			8
						assert max_len_target > max_src_len  # Will be truncated
						assert max_len_source > max_src_len  # Will be truncated
						_A  ,		_A					  =			"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
						_A					  =			SeqaSeqDataset(
						    a__    ,   data_dir=a__    ,   type_path="train"    ,   max_source_length=a__    ,   max_target_length=a__    ,   src_lang=a__    ,   tgt_lang=a__    ,   )
						_A					  =			DataLoader(a__    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn		)
						for batch in dataloader:
										assert isinstance(a__    ,   a__		)
										assert batch["attention_mask"].shape == batch["input_ids"].shape
										# show that articles were trimmed.
										assert batch["input_ids"].shape[1] == max_src_len
										# show that targets are the same len
										assert batch["labels"].shape[1] == max_tgt_len
										if tok_name != MBART_TINY:
														continue
										# check language codes in correct place
										_A					  =			shift_tokens_right(batch["labels"]    ,   tokenizer.pad_token_id		)
										assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
										assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
										assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
										assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

										break  # No need to test every batch







		@parameterized.expand([BART_TINY, BERT_BASE_CASED]		)
		def  a_ (					self      :       Dict    ,   a__      :       Tuple		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			AutoTokenizer.from_pretrained(a__		)
						_A					  =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
						_A					  =			max(len(tokenizer.encode(a__		)		) for a in ARTICLES		)
						_A					  =			max(len(tokenizer.encode(a__		)		) for a in SUMMARIES		)
						_A					  =			4
						_A					  =			LegacySeqaSeqDataset(
						    a__    ,   data_dir=a__    ,   type_path="train"    ,   max_source_length=20    ,   max_target_length=a__    ,   )
						_A					  =			DataLoader(a__    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn		)
						for batch in dataloader:
										assert batch["attention_mask"].shape == batch["input_ids"].shape
										# show that articles were trimmed.
										assert batch["input_ids"].shape[1] == max_len_source
										assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
										# show that targets were truncated
										assert batch["labels"].shape[1] == trunc_target  # Truncated
										assert max_len_target > trunc_target  # Truncated
										break  # No need to test every batch







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





						'''simple docstring'''







						_A					  =			AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"		)

						_A					  =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
						_A					  =			tmp_dir.joinpath("train.source"		).open().readlines()
						_A					  =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
						pack_data_dir(a__    ,   a__    ,   1_28    ,   a__		)
						_A					  =			{x.name for x in tmp_dir.iterdir()}
						_A					  =			{x.name for x in save_dir.iterdir()}
						_A					  =			save_dir.joinpath("train.source"		).open().readlines()
						# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
						# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
						assert len(a__		) < len(a__		)
						assert len(a__		) == 1
						assert len(packed_examples[0]		) == sum(len(a__		) for x in orig_examples		)
						assert orig_paths == new_paths







		@pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,   reason="This test requires fairseq"		)
		def  a_ (					self      :       Any		)       ->						List[str]:





						'''simple docstring'''







						if not FAIRSEQ_AVAILABLE:
										return
						_A  ,		_A  ,		_A					  =			self._get_dataset(max_len=64		)
						_A					  =			64
						_A					  =			ds.make_dynamic_sampler(a__    ,   required_batch_size_multiple=a__		)
						_A					  =			[len(a__		) for x in batch_sampler]
						assert len(set(a__		)		) > 1  # it's not dynamic batch size if every batch is the same length
						assert sum(a__		) == len(a__		)  # no dropped or added examples
						_A					  =			DataLoader(a__    ,   batch_sampler=a__    ,   collate_fn=ds.collate_fn    ,   num_workers=2		)
						_A					  =			[]
						_A					  =			[]
						for batch in data_loader:
										_A					  =			batch["input_ids"].shape
										_A					  =			src_shape[0]
										assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
										_A					  =			np.product(batch["input_ids"].shape		)
										num_src_per_batch.append(a__		)
										if num_src_tokens > (max_tokens * 1.1):
														failures.append(a__		)
						assert num_src_per_batch[0] == max(a__		)
						if failures:
										raise AssertionError(F"""too many tokens in {len(a__		)} batches"""		)







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





						'''simple docstring'''







						_A  ,		_A  ,		_A					  =			self._get_dataset(max_len=5_12		)
						_A					  =			2
						_A					  =			ds.make_sortish_sampler(a__    ,   shuffle=a__		)

						_A					  =			DataLoader(a__    ,   batch_size=a__    ,   collate_fn=ds.collate_fn    ,   num_workers=2		)
						_A					  =			DataLoader(a__    ,   batch_size=a__    ,   collate_fn=ds.collate_fn    ,   num_workers=2    ,   sampler=a__		)

						_A					  =			tokenizer.pad_token_id

						def count_pad_tokens(a__      :       int    ,   a__      :       str="input_ids"		):
										return [batch[k].eq(a__		).sum().item() for batch in data_loader]

						assert sum(count_pad_tokens(a__    ,   k="labels"		)		) < sum(count_pad_tokens(a__    ,   k="labels"		)		)
						assert sum(count_pad_tokens(a__		)		) < sum(count_pad_tokens(a__		)		)
						assert len(a__		) == len(a__		)







		def  a_ (					self      :       Union[str, Any]    ,   a__      :       Dict=10_00    ,   a__      :       Tuple=1_28		)       ->						str:





						'''simple docstring'''







						if os.getenv("USE_REAL_DATA"    ,   a__		):
										_A					  =			"examples/seq2seq/wmt_en_ro"
										_A					  =			max_len * 2 * 64
										if not Path(a__		).joinpath("train.len"		).exists():
														save_len_file(a__    ,   a__		)
						else:
										_A					  =			"examples/seq2seq/test_data/wmt_en_ro"
										_A					  =			max_len * 4
										save_len_file(a__    ,   a__		)

						_A					  =			AutoTokenizer.from_pretrained(a__		)
						_A					  =			SeqaSeqDataset(
						    a__    ,   data_dir=a__    ,   type_path="train"    ,   max_source_length=a__    ,   max_target_length=a__    ,   n_obs=a__    ,   )
						return ds, max_tokens, tokenizer







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





						'''simple docstring'''







						_A  ,		_A  ,		_A					  =			self._get_dataset()
						_A					  =			set(DistributedSortishSampler(a__    ,   2_56    ,   num_replicas=2    ,   rank=0    ,   add_extra_examples=a__		)		)
						_A					  =			set(DistributedSortishSampler(a__    ,   2_56    ,   num_replicas=2    ,   rank=1    ,   add_extra_examples=a__		)		)
						assert idsa.intersection(a__		) == set()







		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]    ,   )
		def  a_ (					self      :       List[Any]    ,   a__      :       List[str]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			AutoTokenizer.from_pretrained(a__    ,   use_fast=a__		)
						if tok_name == MBART_TINY:
										_A					  =			SeqaSeqDataset(
										    a__    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   src_lang="EN"    ,   tgt_lang="FR"    ,   )
										_A					  =			train_dataset.dataset_kwargs
										assert "src_lang" in kwargs and "tgt_lang" in kwargs
						else:
										_A					  =			SeqaSeqDataset(
										    a__    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   )
										_A					  =			train_dataset.dataset_kwargs
										assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
										assert len(a__		) == 1 if tok_name == BART_TINY else len(a__		) == 0



"""simple docstring"""


import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
		from ...tokenization_utils_base import TextInput
from ...utils import logging


a_       							=    logging.get_logger(__name__)

a_       							=    {"vocab_file": "spiece.model"}

a_       							=    {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
a_       							=    {
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}

a_       							=    "▁"
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			VOCAB_FILES_NAMES
		__UpperCamelCase           =			PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase           =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase           =			['input_ids', 'attention_mask']







		def __init__(					self      :       List[str]    ,   a__      :       Optional[int]    ,   a__      :       Union[str, Any]="</s>"    ,   a__      :       Union[str, Any]="<unk>"    ,   a__      :       str="<pad>"    ,   a__      :       Optional[int]=1_00    ,   a__      :       List[Any]=None    ,   a__      :       Optional[Dict[str, Any]] = None    ,   a__      :       Any=True    ,   **a__      :       Optional[int]    ,   )       ->						None:





						'''simple docstring'''







						if extra_ids > 0 and additional_special_tokens is None:
										_A					  =			[F"""<extra_id_{i}>""" for i in range(a__		)]
						elif extra_ids > 0 and additional_special_tokens is not None:
										# Check that we have the right number of extra_id special tokens
										_A					  =			len(set(filter(lambda a__		: bool("extra_id" in str(a__		)		)    ,   a__		)		)		)
										if extra_tokens != extra_ids:
														raise ValueError(
														    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
														    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
														    " tokens"		)
						if legacy:
										logger.warning_once(
										    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
										    " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"		)

						_A					  =			legacy
						_A					  =			{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    eos_token=a__    ,   unk_token=a__    ,   pad_token=a__    ,   extra_ids=a__    ,   additional_special_tokens=a__    ,   sp_model_kwargs=self.sp_model_kwargs    ,   legacy=a__    ,   **a__    ,   )

						_A					  =			vocab_file
						_A					  =			extra_ids

						_A					  =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(a__		)







		@staticmethod
		def  a_ (					a__      :       List[str]    ,   a__      :       Optional[int]    ,   a__      :       Tuple		)       ->						Tuple:





						'''simple docstring'''







						if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
										_A					  =			TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
										if init_max_model_length is not None and init_max_model_length != max_model_length:
														return init_max_model_length
										elif init_max_model_length is None:
														warnings.warn(
														    "This tokenizer was incorrectly instantiated with a model max length of"
														    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
														    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
														    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
														    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
														    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
														    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
														    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
														    " instantiate this tokenizer with `model_max_length` set to your preferred value."    ,   a__    ,   )

						return max_model_length







		@property
		def  a_ (					self      :       List[Any]		)       ->						Dict:





						'''simple docstring'''







						return self.sp_model.get_piece_size() + self._extra_ids







		def  a_ (					self      :       Dict		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			{self.convert_ids_to_tokens(a__		): i for i in range(self.vocab_size		)}
						vocab.update(self.added_tokens_encoder		)
						return vocab







		def  a_ (					self      :       Optional[Any]    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None    ,   a__      :       bool = False		)       ->						List[int]:





						'''simple docstring'''







						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=a__    ,   token_ids_a=a__    ,   already_has_special_tokens=a__		)

						# normal case: some special tokens
						if token_ids_a is None:
										return ([0] * len(a__		)) + [1]
						return ([0] * len(a__		)) + [1] + ([0] * len(a__		)) + [1]







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





						'''simple docstring'''







						return list(
						    set(filter(lambda a__		: bool(re.search(r"<extra_id_\d+>"    ,   a__		)		) is not None    ,   self.additional_special_tokens		)		)		)







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





						'''simple docstring'''







						return [self._convert_token_to_id(a__		) for token in self.get_sentinel_tokens()]







		def  a_ (					self      :       List[Any]    ,   a__      :       List[int]		)       ->						List[int]:





						'''simple docstring'''







						if len(a__		) > 0 and token_ids[-1] == self.eos_token_id:
										warnings.warn(
										    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
										    " eos tokens being added."		)
										return token_ids
						else:
										return token_ids + [self.eos_token_id]







		def  a_ (					self      :       int    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None		)       ->						List[int]:





						'''simple docstring'''







						_A					  =			[self.eos_token_id]

						if token_ids_a is None:
										return len(token_ids_a + eos		) * [0]
						return len(token_ids_a + eos + token_ids_a + eos		) * [0]







		def  a_ (					self      :       Union[str, Any]    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None		)       ->						List[int]:





						'''simple docstring'''







						_A					  =			self._add_eos_if_not_present(a__		)
						if token_ids_a is None:
										return token_ids_a
						else:
										_A					  =			self._add_eos_if_not_present(a__		)
										return token_ids_a + token_ids_a







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





						'''simple docstring'''







						_A					  =			self.__dict__.copy()
						_A					  =			None
						return state







		def __setstate__(					self      :       int    ,   a__      :       Optional[int]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			d

						# for backward compatibility
						if not hasattr(self    ,   "sp_model_kwargs"		):
										_A					  =			{}

						_A					  =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(self.vocab_file		)







		def  a_ (					self      :       int    ,   a__      :       "TextInput"    ,   **a__      :       List[str]		)       ->						List[str]:





						'''simple docstring'''







						if not self.legacy:
										_A					  =			SPIECE_UNDERLINE + text.replace(a__    ,   " "		)
						return super().tokenize(a__    ,   **a__		)







		def  a_ (					self      :       str    ,   a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						Any:





						'''simple docstring'''







						if not self.legacy:
										_A					  =			text.startswith(a__		)
										if is_first:
														_A					  =			text[1:]

						_A					  =			self.sp_model.encode(a__    ,   out_type=a__		)

						if not self.legacy and not is_first and not text.startswith(" "		) and tokens[0].startswith(a__		):
										_A					  =			([tokens[0][1:]] if len(tokens[0]		) > 1 else []) + tokens[1:]
						return tokens







		def  a_ (					self      :       int    ,   a__      :       List[Any]		)       ->						List[str]:





						'''simple docstring'''







						if token.startswith("<extra_id_"		):
										_A					  =			re.match(r"<extra_id_(\d+)>"    ,   a__		)
										_A					  =			int(match.group(1		)		)
										return self.vocab_size - num - 1
						return self.sp_model.piece_to_id(a__		)







		def  a_ (					self      :       Dict    ,   a__      :       Union[str, Any]		)       ->						Any:





						'''simple docstring'''







						if index < self.sp_model.get_piece_size():
										_A					  =			self.sp_model.IdToPiece(a__		)
						else:
										_A					  =			F"""<extra_id_{self.vocab_size - 1 - index}>"""
						return token







		def  a_ (					self      :       Optional[int]    ,   a__      :       Tuple		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			[]
						_A					  =			""
						_A					  =			False
						for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
														if not prev_is_special:
																		out_string += " "
														out_string += self.sp_model.decode(a__		) + token
														_A					  =			True
														_A					  =			[]
										else:
														current_sub_tokens.append(a__		)
														_A					  =			False
						out_string += self.sp_model.decode(a__		)
						return out_string.strip()







		def  a_ (					self      :       Dict    ,   a__      :       str    ,   a__      :       Optional[str] = None		)       ->						Tuple[str]:





						'''simple docstring'''







						if not os.path.isdir(a__		):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
										return
						_A					  =			os.path.join(
						    a__    ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

						if os.path.abspath(self.vocab_file		) != os.path.abspath(a__		) and os.path.isfile(self.vocab_file		):
										copyfile(self.vocab_file    ,   a__		)
						elif not os.path.isfile(self.vocab_file		):
										with open(a__    ,   "wb"		) as fi:
														_A					  =			self.sp_model.serialized_model_proto()
														fi.write(a__		)

						return (out_vocab_file,)