




'''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


lowerCAmelCase_      :  List[str]				     =  'bert-base-cased'
lowerCAmelCase_      :  Union[str, Any]				     =  'google/pegasus-xsum'
lowerCAmelCase_      :  Any				     =  [' Sam ate lunch today.', 'Sams lunch ingredients.']
lowerCAmelCase_      :  int				     =  ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowerCAmelCase_      :  Dict				     =  'patrickvonplaten/t5-tiny-random'
lowerCAmelCase_      :  Optional[int]				     =  'sshleifer/bart-tiny-random'
lowerCAmelCase_      :  str				     =  'sshleifer/tiny-mbart'
lowerCAmelCase_      :  int				     =  'sshleifer/tiny-marian-en-de'



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Path ,    UpperCamelCase__    :					list				):




     """simple docstring"""


     a_     :							Optional[int]     						=  """\n""".join(UpperCamelCase__				)
     Path(UpperCamelCase__				).open("""w"""				).writelines(UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[Any]				):




     """simple docstring"""


     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      SCREAMING_SNAKE_CASE    (						snake_case_				):






      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]						,						)
      @slow
      def 							lowercase_						( self   :  str						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							str     						=  AutoTokenizer.from_pretrained(lowercase__       )
           a_     :							Union[str, Any]     						=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
           a_     :							Union[str, Any]     						=  max(len(tokenizer.encode(lowercase__       )       ) for a in ARTICLES       )
           a_     :							Dict     						=  max(len(tokenizer.encode(lowercase__       )       ) for a in SUMMARIES       )
           a_     :							List[str]     						=  4
           a_     :							Dict     						=  8
           assert max_len_target > max_src_len  # Will be truncated
           assert max_len_source > max_src_len  # Will be truncated
           a_						,     a_     :							Any     						=  """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
           a_     :							List[str]     						=  SeqaSeqDataset(
               lowercase__						,						data_dir=lowercase__						,						type_path="""train"""						,						max_source_length=lowercase__						,						max_target_length=lowercase__						,						src_lang=lowercase__						,						tgt_lang=lowercase__						,						)
           a_     :							Optional[Any]     						=  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
                a_     :							List[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 							lowercase_						( self   :  int						,						lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  AutoTokenizer.from_pretrained(lowercase__       )
           a_     :							Optional[int]     						=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
           a_     :							Dict     						=  max(len(tokenizer.encode(lowercase__       )       ) for a in ARTICLES       )
           a_     :							List[Any]     						=  max(len(tokenizer.encode(lowercase__       )       ) for a in SUMMARIES       )
           a_     :							Optional[int]     						=  4
           a_     :							Tuple     						=  LegacySeqaSeqDataset(
               lowercase__						,						data_dir=lowercase__						,						type_path="""train"""						,						max_source_length=20						,						max_target_length=lowercase__						,						)
           a_     :							Optional[int]     						=  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 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Any     						=  AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""       )

           a_     :							Tuple     						=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
           a_     :							Tuple     						=  tmp_dir.joinpath("""train.source"""       ).open().readlines()
           a_     :							List[str]     						=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
           pack_data_dir(lowercase__						,						lowercase__						,						128						,						lowercase__       )
           a_     :							Dict     						=  {x.name for x in tmp_dir.iterdir()}
           a_     :							Tuple     						=  {x.name for x in save_dir.iterdir()}
           a_     :							Optional[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(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 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           if not FAIRSEQ_AVAILABLE:
                return
           a_						,     a_						,     a_     :							List[str]     						=  self._get_dataset(max_len=64       )
           a_     :							List[str]     						=  64
           a_     :							str     						=  ds.make_dynamic_sampler(lowercase__						,						required_batch_size_multiple=lowercase__       )
           a_     :							Optional[int]     						=  [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
           a_     :							Any     						=  DataLoader(lowercase__						,						batch_sampler=lowercase__						,						collate_fn=ds.collate_fn						,						num_workers=2       )
           a_     :							List[Any]     						=  []
           a_     :							Tuple     						=  []
           for batch in data_loader:
                a_     :							Any     						=  batch["""input_ids"""].shape
                a_     :							Union[str, Any]     						=  src_shape[0]
                assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                a_     :							Union[str, 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 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_						,     a_						,     a_     :							str     						=  self._get_dataset(max_len=512       )
           a_     :							Optional[int]     						=  2
           a_     :							Tuple     						=  ds.make_sortish_sampler(lowercase__						,						shuffle=lowercase__       )

           a_     :							List[str]     						=  DataLoader(lowercase__						,						batch_size=lowercase__						,						collate_fn=ds.collate_fn						,						num_workers=2       )
           a_     :							Tuple     						=  DataLoader(lowercase__						,						batch_size=lowercase__						,						collate_fn=ds.collate_fn						,						num_workers=2						,						sampler=lowercase__       )

           a_     :							Union[str, Any]     						=  tokenizer.pad_token_id

           def count_pad_tokens(lowercase__   :  int						,						lowercase__   :  Dict="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 							lowercase_						( self   :  Tuple						,						lowercase__   :  Dict=1000						,						lowercase__   :  Dict=128       ):


           '''simple docstring'''

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

           a_     :							Union[str, Any]     						=  AutoTokenizer.from_pretrained(lowercase__       )
           a_     :							Tuple     						=  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 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_						,     a_						,     a_     :							Optional[int]     						=  self._get_dataset()
           a_     :							Dict     						=  set(DistributedSortishSampler(lowercase__						,						256						,						num_replicas=2						,						rank=0						,						add_extra_examples=lowercase__       )       )
           a_     :							str     						=  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 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							Any     						=  AutoTokenizer.from_pretrained(lowercase__						,						use_fast=lowercase__       )
           if tok_name == MBART_TINY:
                a_     :							int     						=  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"""						,						)
                a_     :							Any     						=  train_dataset.dataset_kwargs
                assert "src_lang" in kwargs and "tgt_lang" in kwargs
           else:
                a_     :							Optional[int]     						=  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						,						)
                a_     :							Union[str, Any]     						=  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 re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
 import torch


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


lowerCAmelCase_      :  List[str]				     =  logging.get_logger(__name__)
lowerCAmelCase_      :  List[str]				     =  {'vocab_file': 'spiece.model'}

lowerCAmelCase_      :  Union[str, Any]				     =  {
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

lowerCAmelCase_      :  str				     =  {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       List[Any]   							=					VOCAB_FILES_NAMES
      __magic_name__  :       Dict   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       Optional[int]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       List[str]   							=					['''input_ids''', '''attention_mask''']






      def __init__( self   :  Union[str, Any]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Optional[Any]=False						,						lowercase__   :  Any=False						,						lowercase__   :  Optional[Any]=False						,						lowercase__   :  Dict=None						,						lowercase__   :  Any=None						,						lowercase__   :  List[str]=None						,						lowercase__   :  int=None						,						lowercase__   :  Optional[Dict[str, Any]] = None						,						**lowercase__   :  List[str]						,						):


           '''simple docstring'''

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

           a_     :							Optional[Any]     						=  kwargs.get("""name_or_path"""       )
           if name_or_path is None:
                logger.warning(
                    """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
                    """ you are testing the model, this can safely be ignored"""       )
                a_     :							List[Any]     						=  """None"""

           # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
           a_     :							Union[str, Any]     						=  """<|endoftext|>""" if eos_token is None else eos_token
           a_     :							Dict     						=  """<unk>""" if unk_token is None else unk_token
           if "gpt-sw3-7b" in name_or_path:
                a_     :							Tuple     						=  unk_token if pad_token is None else pad_token
                a_     :							Any     						=  eos_token if bos_token is None else bos_token
           else:
                a_     :							Optional[int]     						=  """<pad>""" if pad_token is None else pad_token
                a_     :							str     						=  """<s>""" if bos_token is None else bos_token

           super().__init__(
               do_lower_case=lowercase__						,						remove_space=lowercase__						,						keep_accents=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						pad_token=lowercase__						,						sp_model_kwargs=self.sp_model_kwargs						,						**lowercase__						,						)

           a_     :							Optional[int]     						=  do_lower_case
           a_     :							Any     						=  remove_space
           a_     :							List[str]     						=  keep_accents
           a_     :							int     						=  vocab_file

           a_     :							Any     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(lowercase__       )

           # Used for whitespace normalization in input texts
           # fmt : off
           a_     :							Dict     						=  {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
           # fmt : on

           # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
           a_     :							Tuple     						=  re.compile(
               F"[{''.join(map(lowercase__						,						list(range(0						,						9       )       ) + list(range(11						,						32       )       ) + list(range(127						,						160       )       ) + [160, 173, 8203]       )       )}]"       )






      def __getstate__( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.__dict__.copy()
           a_     :							Tuple     						=  None
           return state






      def __setstate__( self   :  Tuple						,						lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  d

           # for backward compatibility
           if not hasattr(self						,						"""sp_model_kwargs"""       ):
                a_     :							Union[str, Any]     						=  {}

           a_     :							int     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(self.vocab_file       )






      @property
      # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return len(self.sp_model       )






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.non_printing_characters_re.sub(""""""						,						lowercase__       )

           # Normalize whitespaces
           a_     :							List[Any]     						=  """""".join([char if char not in self.whitespaces else """ """ for char in text]       )

           # NFC Unicode normalization
           a_     :							str     						=  unicodedata.normalize("""NFC"""						,						lowercase__       )
           return text






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.preprocess_text(lowercase__       )
           return self.sp_model.encode(lowercase__						,						out_type=lowercase__       )






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           return self.sp_model.PieceToId(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  int       ):


           '''simple docstring'''

           return self.sp_model.IdToPiece(lowercase__       )






      @staticmethod
      def 							lowercase_						( lowercase__   :  str       ):


           '''simple docstring'''

           return out_string






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Dict     						=  []
           a_     :							int     						=  """"""
           a_     :							Union[str, Any]     						=  False
           for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                     # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                     if not prev_is_special:
                          out_string += " "

                     out_string += self.sp_model.decode(lowercase__       ) + token
                     a_     :							int     						=  True
                     a_     :							Optional[int]     						=  []
                else:
                     current_sub_tokens.append(lowercase__       )
                     a_     :							List[str]     						=  False
           out_string += self.sp_model.decode(lowercase__       )

           return out_string






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  {self.convert_ids_to_tokens(lowercase__       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           if not os.path.isdir(lowercase__       ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
                return
           a_     :							Any     						=  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__       ) and os.path.isfile(self.vocab_file       ):
                copyfile(self.vocab_file						,						lowercase__       )
           elif not os.path.isfile(self.vocab_file       ):
                with open(lowercase__						,						"""wb"""       ) as fi:
                     a_     :							int     						=  self.sp_model.serialized_model_proto()
                     fi.write(lowercase__       )

           return (out_vocab_file,)






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Union[str, List[str]]						,						lowercase__   :  Union[str, bool] = False       ):


           '''simple docstring'''

           if isinstance(lowercase__						,						lowercase__       ):
                a_     :							List[str]     						=  self.preprocess_text(lowercase__       )
                a_     :							str     						=  self.sp_model.encode(lowercase__       )
           else:
                a_     :							Optional[Any]     						=  [self.preprocess_text(lowercase__       ) for t in text]
                a_     :							Any     						=  self.sp_model.encode(lowercase__       )

           if return_tensors is True or return_tensors == "pt":
                a_     :							Tuple     						=  torch.tensor(lowercase__       )

           return token_ids






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Union[int, List[int]]       ):


           '''simple docstring'''

           return self.sp_model.decode(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  "Conversation"       ):


           '''simple docstring'''

           a_     :							Dict     						=  [F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
           a_     :							Any     						=  (
               F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(lowercase__       ) + F"{self.bos_token}Bot:"
           )
           return self.encode(text=lowercase__       )
