


'''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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Union[str, Any]       				=       """\n""".join(__UpperCAmelCase   )
			Path(__UpperCAmelCase   ).open("""w"""   ).writelines(__UpperCAmelCase   )







def       _UpperCamelCase (						UpperCamelCase__   ):

			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(__UpperCAmelCase	, f'''{split}.source'''   )	, __UpperCAmelCase   )
						_dump_articles(os.path.join(__UpperCAmelCase	, f'''{split}.target'''   )	, __UpperCAmelCase   )
			return tmp_dir


class 						_snake_case   (       _lowerCamelCase		):




					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ]							,							)
					@slow
					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[str]       				=       AutoTokenizer.from_pretrained(lowercase_)
								UpperCAmelCase__					:   Optional[Any]       				=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
								UpperCAmelCase__					:   str       				=       max(len(tokenizer.encode(lowercase_)) for a in ARTICLES)
								UpperCAmelCase__					:   Dict       				=       max(len(tokenizer.encode(lowercase_)) for a in SUMMARIES)
								UpperCAmelCase__					:   Optional[Any]       				=       4
								UpperCAmelCase__					:   Optional[Any]       				=       8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								UpperCAmelCase__				,    UpperCAmelCase__					:   str       				=       """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
								UpperCAmelCase__					:   Optional[int]       				=       SeqaSeqDataset(
								    lowercase_							,							data_dir=lowercase_							,							type_path="""train"""							,							max_source_length=lowercase_							,							max_target_length=lowercase_							,							src_lang=lowercase_							,							tgt_lang=lowercase_							,							)
								UpperCAmelCase__					:   Tuple       				=       DataLoader(lowercase_							,							batch_size=2							,							collate_fn=train_dataset.collate_fn)
								for batch in dataloader:
											assert isinstance(lowercase_							,							lowercase_)
											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
											UpperCAmelCase__					:   Optional[Any]       				=       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 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[Any]       				=       AutoTokenizer.from_pretrained(lowercase_)
								UpperCAmelCase__					:   Any       				=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
								UpperCAmelCase__					:   Dict       				=       max(len(tokenizer.encode(lowercase_)) for a in ARTICLES)
								UpperCAmelCase__					:   List[Any]       				=       max(len(tokenizer.encode(lowercase_)) for a in SUMMARIES)
								UpperCAmelCase__					:   List[str]       				=       4
								UpperCAmelCase__					:   int       				=       LegacySeqaSeqDataset(
								    lowercase_							,							data_dir=lowercase_							,							type_path="""train"""							,							max_source_length=20							,							max_target_length=lowercase_							,							)
								UpperCAmelCase__					:   Union[str, Any]       				=       DataLoader(lowercase_							,							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 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25""")

								UpperCAmelCase__					:   Optional[Any]       				=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
								UpperCAmelCase__					:   Optional[Any]       				=       tmp_dir.joinpath("""train.source""").open().readlines()
								UpperCAmelCase__					:   List[Any]       				=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
								pack_data_dir(lowercase_							,							lowercase_							,							128							,							lowercase_)
								UpperCAmelCase__					:   List[Any]       				=       {x.name for x in tmp_dir.iterdir()}
								UpperCAmelCase__					:   Tuple       				=       {x.name for x in save_dir.iterdir()}
								UpperCAmelCase__					:   Optional[Any]       				=       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(lowercase_) < len(lowercase_)
								assert len(lowercase_) == 1
								assert len(packed_examples[0]) == sum(len(lowercase_) for x in orig_examples)
								assert orig_paths == new_paths




					@pytest.mark.skipif(not FAIRSEQ_AVAILABLE							,							reason="""This test requires fairseq""")
					def 			snake_case__  ( self):
								if not FAIRSEQ_AVAILABLE:
											return
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Dict       				=       self._get_dataset(max_len=64)
								UpperCAmelCase__					:   Optional[Any]       				=       64
								UpperCAmelCase__					:   Union[str, Any]       				=       ds.make_dynamic_sampler(lowercase_							,							required_batch_size_multiple=lowercase_)
								UpperCAmelCase__					:   Any       				=       [len(lowercase_) for x in batch_sampler]
								assert len(set(lowercase_)) > 1  # it's not dynamic batch size if every batch is the same length
								assert sum(lowercase_) == len(lowercase_)  # no dropped or added examples
								UpperCAmelCase__					:   Optional[Any]       				=       DataLoader(lowercase_							,							batch_sampler=lowercase_							,							collate_fn=ds.collate_fn							,							num_workers=2)
								UpperCAmelCase__					:   Union[str, Any]       				=       []
								UpperCAmelCase__					:   Any       				=       []
								for batch in data_loader:
											UpperCAmelCase__					:   Any       				=       batch["""input_ids"""].shape
											UpperCAmelCase__					:   List[str]       				=       src_shape[0]
											assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
											UpperCAmelCase__					:   Optional[Any]       				=       np.product(batch["""input_ids"""].shape)
											num_src_per_batch.append(lowercase_)
											if num_src_tokens > (max_tokens * 1.1):
														failures.append(lowercase_)
								assert num_src_per_batch[0] == max(lowercase_)
								if failures:
											raise AssertionError(f'''too many tokens in {len(lowercase_)} batches''')




					def 			snake_case__  ( self):
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   str       				=       self._get_dataset(max_len=512)
								UpperCAmelCase__					:   str       				=       2
								UpperCAmelCase__					:   Optional[Any]       				=       ds.make_sortish_sampler(lowercase_							,							shuffle=lowercase_)

								UpperCAmelCase__					:   Tuple       				=       DataLoader(lowercase_							,							batch_size=lowercase_							,							collate_fn=ds.collate_fn							,							num_workers=2)
								UpperCAmelCase__					:   int       				=       DataLoader(lowercase_							,							batch_size=lowercase_							,							collate_fn=ds.collate_fn							,							num_workers=2							,							sampler=lowercase_)

								UpperCAmelCase__					:   str       				=       tokenizer.pad_token_id

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

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




					def 			snake_case__  ( self							,							_lowerCamelCase=1000							,							_lowerCamelCase=128):
								if os.getenv("""USE_REAL_DATA"""							,							lowercase_):
											UpperCAmelCase__					:   Optional[Any]       				=       """examples/seq2seq/wmt_en_ro"""
											UpperCAmelCase__					:   Tuple       				=       max_len * 2 * 64
											if not Path(lowercase_).joinpath("""train.len""").exists():
														save_len_file(lowercase_							,							lowercase_)
								else:
											UpperCAmelCase__					:   Any       				=       """examples/seq2seq/test_data/wmt_en_ro"""
											UpperCAmelCase__					:   List[Any]       				=       max_len * 4
											save_len_file(lowercase_							,							lowercase_)

								UpperCAmelCase__					:   Dict       				=       AutoTokenizer.from_pretrained(lowercase_)
								UpperCAmelCase__					:   List[str]       				=       SeqaSeqDataset(
								    lowercase_							,							data_dir=lowercase_							,							type_path="""train"""							,							max_source_length=lowercase_							,							max_target_length=lowercase_							,							n_obs=lowercase_							,							)
								return ds, max_tokens, tokenizer




					def 			snake_case__  ( self):
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Union[str, Any]       				=       self._get_dataset()
								UpperCAmelCase__					:   Union[str, Any]       				=       set(DistributedSortishSampler(lowercase_							,							256							,							num_replicas=2							,							rank=0							,							add_extra_examples=lowercase_))
								UpperCAmelCase__					:   int       				=       set(DistributedSortishSampler(lowercase_							,							256							,							num_replicas=2							,							rank=1							,							add_extra_examples=lowercase_))
								assert idsa.intersection(lowercase_) == set()




					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ]							,							)
					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[int]       				=       AutoTokenizer.from_pretrained(lowercase_							,							use_fast=lowercase_)
								if tok_name == MBART_TINY:
											UpperCAmelCase__					:   str       				=       SeqaSeqDataset(
											    lowercase_							,							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"""							,							)
											UpperCAmelCase__					:   Union[str, Any]       				=       train_dataset.dataset_kwargs
											assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
											UpperCAmelCase__					:   List[str]       				=       SeqaSeqDataset(
											    lowercase_							,							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							,							)
											UpperCAmelCase__					:   Tuple       				=       train_dataset.dataset_kwargs
											assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
											assert len(lowercase_) == 1 if tok_name == BART_TINY else len(lowercase_) == 0



'''simple docstring'''



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__A		=logging.get_logger(__name__)

__A		={'vocab_file': 'spiece.model'}

__A		={
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}


class 						_snake_case   (       a__		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=False							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase="<s>"							,							_lowerCamelCase="</s>"							,							_lowerCamelCase="<unk>"							,							_lowerCamelCase="<sep>"							,							_lowerCamelCase="<pad>"							,							_lowerCamelCase="<cls>"							,							_lowerCamelCase="<mask>"							,							_lowerCamelCase=["<eop>", "<eod>"]							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Dict       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else mask_token

								UpperCAmelCase__					:   Any       				=       {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=_lowerCamelCase							,							remove_space=_lowerCamelCase							,							keep_accents=_lowerCamelCase							,							bos_token=_lowerCamelCase							,							eos_token=_lowerCamelCase							,							unk_token=_lowerCamelCase							,							sep_token=_lowerCamelCase							,							pad_token=_lowerCamelCase							,							cls_token=_lowerCamelCase							,							mask_token=_lowerCamelCase							,							additional_special_tokens=_lowerCamelCase							,							sp_model_kwargs=self.sp_model_kwargs							,							**_lowerCamelCase							,							)

								UpperCAmelCase__					:   List[str]       				=       3

								UpperCAmelCase__					:   str       				=       do_lower_case
								UpperCAmelCase__					:   Tuple       				=       remove_space
								UpperCAmelCase__					:   List[str]       				=       keep_accents
								UpperCAmelCase__					:   Any       				=       vocab_file

								UpperCAmelCase__					:   str       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(_lowerCamelCase)

								try:
											import jieba
								except ModuleNotFoundError as error:
											raise error.__class__(
											    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
											    """See https://pypi.org/project/jieba/ for installation.""")
								UpperCAmelCase__					:   Union[str, Any]       				=       jieba
								UpperCAmelCase__					:   int       				=       str.maketrans(""" \n"""							,							"""\u2582\u2583""")




					@property
					# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
					def 			snake_case__  ( self):
								return len(self.sp_model)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       {self.convert_ids_to_tokens(_lowerCamelCase): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab




					def __getstate__( self):
								UpperCAmelCase__					:   List[str]       				=       self.__dict__.copy()
								UpperCAmelCase__					:   Optional[int]       				=       None
								return state




					def __setstate__( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[Any]       				=       d

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

								UpperCAmelCase__					:   Union[str, Any]       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.vocab_file)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if self.remove_space:
											UpperCAmelCase__					:   Tuple       				=       """ """.join(inputs.strip().split())
								else:
											UpperCAmelCase__					:   str       				=       inputs
								UpperCAmelCase__					:   List[Any]       				=       outputs.replace("""``"""							,							"""\"""").replace("""''"""							,							"""\"""")

								if not self.keep_accents:
											UpperCAmelCase__					:   str       				=       unicodedata.normalize("""NFKD"""							,							_lowerCamelCase)
											UpperCAmelCase__					:   Any       				=       """""".join([c for c in outputs if not unicodedata.combining(_lowerCamelCase)])
								if self.do_lower_case:
											UpperCAmelCase__					:   Optional[int]       				=       outputs.lower()

								return outputs




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[str]       				=       self.preprocess_text(_lowerCamelCase)
								UpperCAmelCase__					:   Dict       				=       self.sp_model.encode(_lowerCamelCase							,							out_type=_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       []
								for piece in pieces:
											if len(_lowerCamelCase) > 1 and piece[-1] == str(""",""") and piece[-2].isdigit():
														UpperCAmelCase__					:   str       				=       self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCamelCase							,							""""""))
														if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																	if len(cur_pieces[0]) == 1:
																				UpperCAmelCase__					:   Optional[int]       				=       cur_pieces[1:]
																	else:
																				UpperCAmelCase__					:   Optional[int]       				=       cur_pieces[0][1:]
														cur_pieces.append(piece[-1])
														new_pieces.extend(_lowerCamelCase)
											else:
														new_pieces.append(_lowerCamelCase)

								return new_pieces




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.sp_model.PieceToId(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.sp_model.IdToPiece(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   str       				=       """""".join(_lowerCamelCase).replace(_lowerCamelCase							,							""" """).strip()
								return out_string




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   Optional[Any]       				=       [self.sep_token_id]
								UpperCAmelCase__					:   Optional[int]       				=       [self.cls_token_id]
								if token_ids_a is None:
											return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False):

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

								if token_ids_a is not None:
											return ([0] * len(_lowerCamelCase)) + [1] + ([0] * len(_lowerCamelCase)) + [1, 1]
								return ([0] * len(_lowerCamelCase)) + [1, 1]




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   int       				=       [self.sep_token_id]
								UpperCAmelCase__					:   Tuple       				=       [2]

								if token_ids_a is None:
											return len(token_ids_a + sep) * [0] + cls_segment_id
								return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not os.path.isdir(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__					:   str       				=       os.path.join(
								    _lowerCamelCase							,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

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

								return (out_vocab_file,)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       super()._decode(*_lowerCamelCase							,							**_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       text.replace(""" """							,							"""""").replace("""\u2582"""							,							""" """).replace("""\u2583"""							,							"""\n""")
								return text