
'''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_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    int     			=						"\n".join(_lowerCamelCase       )
    Path(_lowerCamelCase       ).open("w"       ).writelines(_lowerCamelCase       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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






class    UpperCAmelCase_				(	snake_case__					):





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

     snake_case_ :    Tuple     			=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
     snake_case_ :    Dict     			=						tmp_dir.joinpath("train.source").open().readlines()
     snake_case_ :    List[Any]     			=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
     pack_data_dir(UpperCamelCase__ ,       UpperCamelCase__ ,       1_28 ,       UpperCamelCase__)
     snake_case_ :    Optional[int]     			=						{x.name for x in tmp_dir.iterdir()}
     snake_case_ :    Dict     			=						{x.name for x in save_dir.iterdir()}
     snake_case_ :    int     			=						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(UpperCamelCase__) < len(UpperCamelCase__)
     assert len(UpperCamelCase__) == 1
     assert len(packed_examples[0]) == sum(len(UpperCamelCase__) 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
     snake_case_			, snake_case_			, snake_case_ :    List[str]     			=						self._get_dataset(max_len=64)
     snake_case_ :    List[str]     			=						64
     snake_case_ :    List[str]     			=						ds.make_dynamic_sampler(UpperCamelCase__ ,       required_batch_size_multiple=UpperCamelCase__)
     snake_case_ :    Any     			=						[len(UpperCamelCase__) for x in batch_sampler]
     assert len(set(UpperCamelCase__)) > 1  # it's not dynamic batch size if every batch is the same length
     assert sum(UpperCamelCase__) == len(UpperCamelCase__)  # no dropped or added examples
     snake_case_ :    Dict     			=						DataLoader(UpperCamelCase__ ,       batch_sampler=UpperCamelCase__ ,       collate_fn=ds.collate_fn ,       num_workers=2)
     snake_case_ :    List[Any]     			=						[]
     snake_case_ :    List[str]     			=						[]
     for batch in data_loader:
         snake_case_ :    int     			=						batch["input_ids"].shape
         snake_case_ :    Optional[int]     			=						src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         snake_case_ :    Union[str, Any]     			=						np.product(batch["input_ids"].shape)
         num_src_per_batch.append(UpperCamelCase__)
         if num_src_tokens > (max_tokens * 1.1):
             failures.append(UpperCamelCase__)
     assert num_src_per_batch[0] == max(UpperCamelCase__)
     if failures:
         raise AssertionError(F'too many tokens in {len(UpperCamelCase__)} batches')





 def        snake_case__		(    self):
     snake_case_			, snake_case_			, snake_case_ :    str     			=						self._get_dataset(max_len=5_12)
     snake_case_ :    List[str]     			=						2
     snake_case_ :    int     			=						ds.make_sortish_sampler(UpperCamelCase__ ,       shuffle=UpperCamelCase__)

     snake_case_ :    Optional[int]     			=						DataLoader(UpperCamelCase__ ,       batch_size=UpperCamelCase__ ,       collate_fn=ds.collate_fn ,       num_workers=2)
     snake_case_ :    List[Any]     			=						DataLoader(UpperCamelCase__ ,       batch_size=UpperCamelCase__ ,       collate_fn=ds.collate_fn ,       num_workers=2 ,       sampler=UpperCamelCase__)

     snake_case_ :    int     			=						tokenizer.pad_token_id

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

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





 def        snake_case__		(    self ,       lowercase_=10_00 ,       lowercase_=1_28):





     if os.getenv("USE_REAL_DATA" ,       UpperCamelCase__):
         snake_case_ :    str     			=						"examples/seq2seq/wmt_en_ro"
         snake_case_ :    Optional[int]     			=						max_len * 2 * 64
         if not Path(UpperCamelCase__).joinpath("train.len").exists():
             save_len_file(UpperCamelCase__ ,       UpperCamelCase__)
     else:
         snake_case_ :    Any     			=						"examples/seq2seq/test_data/wmt_en_ro"
         snake_case_ :    Optional[Any]     			=						max_len * 4
         save_len_file(UpperCamelCase__ ,       UpperCamelCase__)

     snake_case_ :    Union[str, Any]     			=						AutoTokenizer.from_pretrained(UpperCamelCase__)
     snake_case_ :    Any     			=						SeqaSeqDataset(
         UpperCamelCase__ ,       data_dir=UpperCamelCase__ ,       type_path="train" ,       max_source_length=UpperCamelCase__ ,       max_target_length=UpperCamelCase__ ,       n_obs=UpperCamelCase__ ,       )
     return ds, max_tokens, tokenizer





 def        snake_case__		(    self):
     snake_case_			, snake_case_			, snake_case_ :    int     			=						self._get_dataset()
     snake_case_ :    Dict     			=						set(DistributedSortishSampler(UpperCamelCase__ ,       2_56 ,       num_replicas=2 ,       rank=0 ,       add_extra_examples=UpperCamelCase__))
     snake_case_ :    Optional[int]     			=						set(DistributedSortishSampler(UpperCamelCase__ ,       2_56 ,       num_replicas=2 ,       rank=1 ,       add_extra_examples=UpperCamelCase__))
     assert idsa.intersection(UpperCamelCase__) == set()





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






'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  a_											=						None


a_											=						logging.get_logger(__name__)


a_											=						"▁"

a_											=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

a_											=						{
    "google/pegasus-xsum": 512,
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						PegasusTokenizer
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_="<pad>" ,       lowercase_="</s>" ,       lowercase_="<unk>" ,       lowercase_="<mask_2>" ,       lowercase_="<mask_1>" ,       lowercase_=None ,       lowercase_=1_03 ,       **lowercase_ ,       ):
     snake_case_ :    List[Any]     			=						offset

     if additional_special_tokens is not None:
         if not isinstance(lowercase_ ,       lowercase_):
             raise TypeError(
                 F'additional_special_tokens should be of type {type(lowercase_)}, but is'
                 F' {type(lowercase_)}')

         snake_case_ :    Tuple     			=						(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             F'<unk_{i}>' for i in range(len(lowercase_) ,       self.offset - 1)
         ]

         if len(set(lowercase_)) != len(lowercase_):
             raise ValueError(
                 "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                 F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
         snake_case_ :    Optional[Any]     			=						additional_special_tokens_extended
     else:
         snake_case_ :    Tuple     			=						[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [F'<unk_{i}>' for i in range(2 ,       self.offset)]

     super().__init__(
         lowercase_ ,       tokenizer_file=lowercase_ ,       pad_token=lowercase_ ,       eos_token=lowercase_ ,       unk_token=lowercase_ ,       mask_token=lowercase_ ,       mask_token_sent=lowercase_ ,       offset=lowercase_ ,       additional_special_tokens=lowercase_ ,       **lowercase_ ,       )
     snake_case_ :    int     			=						vocab_file
     snake_case_ :    str     			=						False if not self.vocab_file else True





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Union[str, Any]     			=						set(self.all_special_ids)  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
         raise ValueError(
             "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
             F' {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}')

     return [1 if x in all_special_ids else 0 for x in seq]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = False):
     if already_has_special_tokens:
         return self._special_token_mask(lowercase_)
     elif token_ids_a is None:
         return self._special_token_mask(lowercase_) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a) + [1]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
             "tokenizer.")

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

     if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_):
         copyfile(self.vocab_file ,       lowercase_)

     return (out_vocab_file,)




