
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": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

_A					 =			{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
_A					 =			0
_A					 =			1
_A					 =			2
_A					 =			3
_A					 =			4







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Any									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Optional[Any]									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Optional[Any]									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							int									=		'''left'''





     def __init__(self				:   Tuple    ,   _A				:   Optional[Any]    ,   _A				:   Optional[int]=False    ,   _A				:   int=True    ,   _A				:   List[Any]=False    ,   _A				:   int="<s>"    ,   _A				:   Union[str, Any]="</s>"    ,   _A				:   List[str]="<unk>"    ,   _A				:   str="<sep>"    ,   _A				:   Optional[Any]="<pad>"    ,   _A				:   Tuple="<cls>"    ,   _A				:   int="<mask>"    ,   _A				:   Union[str, Any]=["<eop>", "<eod>"]    ,   _A				:   Optional[Dict[str, Any]] = None    ,   **_A				:   int    ,   )						->						Optional[Any]:
         snake_case								= AddedToken(_snake_case    ,   lstrip=_snake_case    ,   rstrip=_snake_case       ) if isinstance(_snake_case    ,   _snake_case       ) else mask_token

         snake_case								= {} if sp_model_kwargs is None else sp_model_kwargs

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

         snake_case								= 3

         snake_case								= do_lower_case
         snake_case								= remove_space
         snake_case								= keep_accents
         snake_case								= vocab_file

         snake_case								= spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(_snake_case       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Tuple:
         return len(self.sp_model       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						List[Any]:
         snake_case								= {self.convert_ids_to_tokens(_snake_case       ): i for i in range(self.vocab_size       )}
         vocab.update(self.added_tokens_encoder       )
         return vocab





     def __getstate__(self				:   Any       )						->						int:
         snake_case								= self.__dict__.copy()
         snake_case								= None
         return state





     def __setstate__(self				:   str    ,   _A				:   Union[str, Any]       )						->						Dict:
         snake_case								= d

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

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





     def 		UpperCAmelCase(self				:   int    ,   _A				:   Any       )						->						Any:


         if self.remove_space:
             snake_case								= " ".join(inputs.strip().split()       )
         else:
             snake_case								= inputs
         snake_case								= outputs.replace("``"    ,   "\""       ).replace("\'\'"    ,   "\""       )

         if not self.keep_accents:
             snake_case								= unicodedata.normalize("NFKD"    ,   _snake_case       )
             snake_case								= "".join([c for c in outputs if not unicodedata.combining(_snake_case       )]       )
         if self.do_lower_case:
             snake_case								= outputs.lower()

         return outputs





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

         return new_pieces





     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   Dict       )						->						Optional[int]:
         return self.sp_model.PieceToId(_snake_case       )





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Union[str, Any]       )						->						Any:
         return self.sp_model.IdToPiece(_snake_case       )





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   Optional[Any]       )						->						Union[str, Any]:
         snake_case								= "".join(_snake_case       ).replace(_snake_case    ,   " "       ).strip()
         return out_string





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   List[int]    ,   _A				:   bool = False    ,   _A				:   bool = None    ,   _A				:   bool = True    ,   **_A				:   Dict    ,   )						->						List[Any]:
         snake_case								= kwargs.pop("use_source_tokenizer"    ,   _snake_case       )

         snake_case								= self.convert_ids_to_tokens(_snake_case    ,   skip_special_tokens=_snake_case       )

         # To avoid mixing byte-level and unicode for byte-level BPT
         # we need to build string separately for added tokens and byte-level tokens
         # cf. https://github.com/huggingface/transformers/issues/1133
         snake_case								= []
         snake_case								= []
         for token in filtered_tokens:
             if skip_special_tokens and token in self.all_special_ids:
                 continue
             if token in self.added_tokens_encoder:
                 if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(_snake_case       )       )
                     snake_case								= []
                 sub_texts.append(_snake_case       )
             else:
                 current_sub_text.append(_snake_case       )
         if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(_snake_case       )       )

         # Mimic the behavior of the Rust tokenizer:
         # By default, there are no spaces between special tokens
         snake_case								= "".join(_snake_case       )

         snake_case								= (
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
             snake_case								= self.clean_up_tokenization(_snake_case       )
             return clean_text
         else:
             return text





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						Optional[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [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 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None    ,   _A				:   bool = False       )						->						Union[str, Any]:


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

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





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						Optional[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [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 		UpperCAmelCase(self				:   Tuple    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple:


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

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

         return (out_vocab_file,)



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 						lowercase_      (       A__			,		A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= "\n".join(A__		)
    Path(A__		).open("w"		).writelines(A__		)



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

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







class 					lowerCamelCase		(  A_  ):





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     @slow
     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[str]       )						->						Optional[int]:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= 8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         snake_case					,							snake_case								= "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   src_lang=_A    ,   tgt_lang=_A    ,   )
         snake_case								= 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
             snake_case								= 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 		UpperCAmelCase(self				:   str    ,   _A				:   Dict       )						->						Dict:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= LegacySeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=2_0    ,   max_target_length=_A    ,   )
         snake_case								= 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 2_0 >= 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 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[Any]:
         snake_case								= AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         snake_case								= tmp_dir.joinpath("train.source"       ).open().readlines()
         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(_A    ,   _A    ,   1_2_8    ,   _A       )
         snake_case								= {x.name for x in tmp_dir.iterdir()}
         snake_case								= {x.name for x in save_dir.iterdir()}
         snake_case								= 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 		UpperCAmelCase(self				:   Optional[int]       )						->						Union[str, Any]:
         if not FAIRSEQ_AVAILABLE:
             return
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=6_4       )
         snake_case								= 6_4
         snake_case								= ds.make_dynamic_sampler(_A    ,   required_batch_size_multiple=_A       )
         snake_case								= [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
         snake_case								= DataLoader(_A    ,   batch_sampler=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= []
         snake_case								= []
         for batch in data_loader:
             snake_case								= batch["input_ids"].shape
             snake_case								= src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             snake_case								= 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 		UpperCAmelCase(self				:   int       )						->						str:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=5_1_2       )
         snake_case								= 2
         snake_case								= ds.make_sortish_sampler(_A    ,   shuffle=_A       )

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

         snake_case								= tokenizer.pad_token_id

         def count_pad_tokens(_A				:   Dict    ,   _A				:   Union[str, Any]="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 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]=1_0_0_0    ,   _A				:   Optional[int]=1_2_8       )						->						List[Any]:
         if os.getenv("USE_REAL_DATA"    ,   _A       ):
             snake_case								= "examples/seq2seq/wmt_en_ro"
             snake_case								= max_len * 2 * 6_4
             if not Path(_A       ).joinpath("train.len"       ).exists():
                 save_len_file(_A    ,   _A       )
         else:
             snake_case								= "examples/seq2seq/test_data/wmt_en_ro"
             snake_case								= max_len * 4
             save_len_file(_A    ,   _A       )

         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= 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 		UpperCAmelCase(self				:   List[Any]       )						->						Union[str, Any]:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset()
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=0    ,   add_extra_examples=_A       )       )
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   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 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[Any]       )						->						Union[str, Any]:
         snake_case								= AutoTokenizer.from_pretrained(_A    ,   use_fast=_A       )
         if tok_name == MBART_TINY:
             snake_case								= 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"    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             snake_case								= 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    ,   )
             snake_case								= 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

