
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


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




def    __UpperCamelCase     (				lowerCAmelCase__							:				Path  ,   lowerCAmelCase__							:				list  ):
 __a :  Any						=  '''\n'''.join(lowerCAmelCase__  )
 Path(lowerCAmelCase__  ).open('''w'''  ).writelines(lowerCAmelCase__  )




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

    __a :  int						=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
    __a :  int						=  tmp_dir.joinpath('''train.source'''   ).open().readlines()
    __a :  Union[str, Any]						=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
    pack_data_dir(snake_case_					,				snake_case_					,				1_2_8					,				snake_case_   )
    __a :  Tuple						=  {x.name for x in tmp_dir.iterdir()}
    __a :  Union[str, Any]						=  {x.name for x in save_dir.iterdir()}
    __a :  str						=  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(snake_case_   ) < len(snake_case_   )
    assert len(snake_case_   ) == 1
    assert len(packed_examples[0]   ) == sum(len(snake_case_   ) for x in orig_examples   )
    assert orig_paths == new_paths
   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE					,				reason='''This test requires fairseq'''   )
   def       lowerCAmelCase			(self :					int   ):
    if not FAIRSEQ_AVAILABLE:
     return
    __a				,       __a				,       __a :  Dict						=  self._get_dataset(max_len=6_4   )
    __a :  int						=  6_4
    __a :  int						=  ds.make_dynamic_sampler(snake_case_					,				required_batch_size_multiple=snake_case_   )
    __a :  List[Any]						=  [len(snake_case_   ) for x in batch_sampler]
    assert len(set(snake_case_   )   ) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(snake_case_   ) == len(snake_case_   )  # no dropped or added examples
    __a :  int						=  DataLoader(snake_case_					,				batch_sampler=snake_case_					,				collate_fn=ds.collate_fn					,				num_workers=2   )
    __a :  Optional[Any]						=  []
    __a :  int						=  []
    for batch in data_loader:
     __a :  Tuple						=  batch['''input_ids'''].shape
     __a :  Any						=  src_shape[0]
     assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
     __a :  Any						=  np.product(batch['''input_ids'''].shape   )
     num_src_per_batch.append(snake_case_   )
     if num_src_tokens > (max_tokens * 1.1):
      failures.append(snake_case_   )
    assert num_src_per_batch[0] == max(snake_case_   )
    if failures:
     raise AssertionError(f"too many tokens in {len(snake_case_   )} batches"   )
   def       lowerCAmelCase			(self :					Dict   ):
    __a				,       __a				,       __a :  int						=  self._get_dataset(max_len=5_1_2   )
    __a :  str						=  2
    __a :  Optional[Any]						=  ds.make_sortish_sampler(snake_case_					,				shuffle=snake_case_   )

    __a :  str						=  DataLoader(snake_case_					,				batch_size=snake_case_					,				collate_fn=ds.collate_fn					,				num_workers=2   )
    __a :  List[Any]						=  DataLoader(snake_case_					,				batch_size=snake_case_					,				collate_fn=ds.collate_fn					,				num_workers=2					,				sampler=snake_case_   )

    __a :  str						=  tokenizer.pad_token_id

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

    assert sum(count_pad_tokens(snake_case_					,				k='''labels'''   )   ) < sum(count_pad_tokens(snake_case_					,				k='''labels'''   )   )
    assert sum(count_pad_tokens(snake_case_   )   ) < sum(count_pad_tokens(snake_case_   )   )
    assert len(snake_case_   ) == len(snake_case_   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					Dict=1_0_0_0					,				snake_case_ :					str=1_2_8   ):
    if os.getenv('''USE_REAL_DATA'''					,				snake_case_   ):
     __a :  Any						=  '''examples/seq2seq/wmt_en_ro'''
     __a :  Union[str, Any]						=  max_len * 2 * 6_4
     if not Path(snake_case_   ).joinpath('''train.len'''   ).exists():
      save_len_file(snake_case_					,				snake_case_   )
    else:
     __a :  str						=  '''examples/seq2seq/test_data/wmt_en_ro'''
     __a :  Optional[int]						=  max_len * 4
     save_len_file(snake_case_					,				snake_case_   )

    __a :  List[Any]						=  AutoTokenizer.from_pretrained(snake_case_   )
    __a :  Optional[Any]						=  SeqaSeqDataset(
        snake_case_					,				data_dir=snake_case_					,				type_path='''train'''					,				max_source_length=snake_case_					,				max_target_length=snake_case_					,				n_obs=snake_case_					,				)
    return ds, max_tokens, tokenizer
   def       lowerCAmelCase			(self :					Dict   ):
    __a				,       __a				,       __a :  Union[str, Any]						=  self._get_dataset()
    __a :  Tuple						=  set(DistributedSortishSampler(snake_case_					,				2_5_6					,				num_replicas=2					,				rank=0					,				add_extra_examples=snake_case_   )   )
    __a :  Optional[int]						=  set(DistributedSortishSampler(snake_case_					,				2_5_6					,				num_replicas=2					,				rank=1					,				add_extra_examples=snake_case_   )   )
    assert idsa.intersection(snake_case_   ) == set()







   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]					,				)
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					Dict   ):
    __a :  int						=  AutoTokenizer.from_pretrained(snake_case_					,				use_fast=snake_case_   )
    if tok_name == MBART_TINY:
     __a :  Optional[Any]						=  SeqaSeqDataset(
         snake_case_					,				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 :  Union[str, Any]						=  train_dataset.dataset_kwargs
     assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
     __a :  Optional[int]						=  SeqaSeqDataset(
         snake_case_					,				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 :  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(snake_case_   ) == 1 if tok_name == BART_TINY else len(snake_case_   ) == 0


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


lowercase__       =logging.get_logger(__name__)

lowercase__       ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__       ={
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

lowercase__       ={
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


lowercase__       ={
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      RoFormerTokenizer
   def __init__(self :					List[str]					,				snake_case_ :					Optional[int]=None					,				snake_case_ :					str=None					,				snake_case_ :					Optional[Any]=True					,				snake_case_ :					str="[UNK]"					,				snake_case_ :					Dict="[SEP]"					,				snake_case_ :					Any="[PAD]"					,				snake_case_ :					str="[CLS]"					,				snake_case_ :					List[Any]="[MASK]"					,				snake_case_ :					Any=True					,				snake_case_ :					List[str]=None					,				**snake_case_ :					Optional[int]					,				):
    super().__init__(
        snake_case_					,				tokenizer_file=snake_case_					,				do_lower_case=snake_case_					,				unk_token=snake_case_					,				sep_token=snake_case_					,				pad_token=snake_case_					,				cls_token=snake_case_					,				mask_token=snake_case_					,				tokenize_chinese_chars=snake_case_					,				strip_accents=snake_case_					,				**snake_case_					,				)

    __a :  Optional[int]						=  json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
    if (
        pre_tok_state.get('''lowercase'''					,				snake_case_   ) != do_lower_case
        or pre_tok_state.get('''strip_accents'''					,				snake_case_   ) != strip_accents
    ):
     __a :  List[str]						=  getattr(snake_case_					,				pre_tok_state.pop('''type'''   )   )
     __a :  Optional[Any]						=  do_lower_case
     __a :  Optional[int]						=  strip_accents
     __a :  List[str]						=  pre_tok_class(**snake_case_   )

    __a :  Optional[Any]						=  do_lower_case
   def __getstate__(self :					Union[str, Any]   ):
    __a :  Any						=  self.__dict__.copy()
    __a :  Union[str, Any]						=  BertPreTokenizer()
    return state
   def __setstate__(self :					Tuple					,				snake_case_ :					Optional[Any]   ):
    __a :  Dict						=  d
    __a :  str						=  self.__dict__['''_tokenizer'''].get_vocab()
    __a :  Optional[Any]						=  PreTokenizer.custom(JiebaPreTokenizer(snake_case_   )   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]=None   ):
    __a :  Optional[int]						=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

    return output
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[int]					,				snake_case_ :					Optional[List[int]] = None   ):
    __a :  int						=  [self.sep_token_id]
    __a :  List[Any]						=  [self.cls_token_id]
    if token_ids_a is None:
     return len(cls + token_ids_a + sep   ) * [0]
    return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    __a :  Optional[Any]						=  self._tokenizer.model.save(snake_case_					,				name=snake_case_   )
    return tuple(snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Dict					,				snake_case_ :					Tuple=None					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Union[str, Any]=False					,				**snake_case_ :					Tuple					,				):
    __a :  List[str]						=  BertPreTokenizer()
    return super().save_pretrained(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				**snake_case_   )
