'''simple docstring'''

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


a_						:       Union[str, Any]      			=				get_tests_dir("""fixtures/test_sentencepiece.model""")

a_						:       str      			=				{"""target_lang""": """fi""", """source_lang""": """en"""}
a_						:       Dict      			=				""">>zh<<"""
a_						:       List[str]      			=				"""Helsinki-NLP/"""

if is_torch_available():
     a_						:       Optional[Any]      			=				"""pt"""
elif is_tf_available():
     a_						:       Any      			=				"""tf"""
else:
     a_						:       str      			=				"""jax"""





@require_sentencepiece
class   snake_case					(					__SCREAMING_SNAKE_CASE    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						MarianTokenizer
  _lowerCamelCase						=						False
  _lowerCamelCase						=						True






  def 						snake_case	(					self  ):







   """simple docstring"""
   super().setUp()
   lowerCamelCase_  				=						["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
   lowerCamelCase_  				=						dict(zip(_a    ,						range(len(_a  )  )  )  )
   lowerCamelCase_  				=						Path(self.tmpdirname  )
   save_json(_a    ,						save_dir / VOCAB_FILES_NAMES["vocab"]  )
   save_json(_a    ,						save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]  )
   if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
    copyfile(_a    ,						save_dir / VOCAB_FILES_NAMES["source_spm"]  )
    copyfile(_a    ,						save_dir / VOCAB_FILES_NAMES["target_spm"]  )

   lowerCamelCase_  				=						MarianTokenizer.from_pretrained(self.tmpdirname  )
   tokenizer.save_pretrained(self.tmpdirname  )






  def 						snake_case	(					self    ,						**UpperCamelCase  ):







   """simple docstring"""
   return MarianTokenizer.from_pretrained(self.tmpdirname    ,						**_a  )






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   return (
       "This is a test",
       "This is a test",
   )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"</s>"
   lowerCamelCase_  				=						0

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a  )    ,						_a  )
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a  )    ,						_a  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						list(self.get_tokenizer().get_vocab().keys()  )

   self.assertEqual(vocab_keys[0]    ,						"</s>"  )
   self.assertEqual(vocab_keys[1]    ,						"<unk>"  )
   self.assertEqual(vocab_keys[-1]    ,						"<pad>"  )
   self.assertEqual(len(_a  )    ,						9  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   self.assertEqual(self.get_tokenizer().vocab_size    ,						9  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''  )
   lowerCamelCase_  				=						en_de_tokenizer(["I am a small frog"]    ,						return_tensors=_a  )
   self.assertIsInstance(_a    ,						_a  )
   lowerCamelCase_  				=						[38, 121, 14, 697, 3_8848, 0]
   self.assertListEqual(_a    ,						batch.input_ids[0]  )

   lowerCamelCase_  				=						tempfile.mkdtemp()
   en_de_tokenizer.save_pretrained(_a  )
   lowerCamelCase_  				=						[x.name for x in Path(_a  ).glob("*"  )]
   self.assertIn("source.spm"    ,						_a  )
   MarianTokenizer.from_pretrained(_a  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.get_tokenizer()

   lowerCamelCase_  				=						tok(
       ["I am a small frog" * 1000, "I am a small frog"]    ,						padding=_a    ,						truncation=_a    ,						return_tensors=_a  )
   self.assertIsInstance(_a    ,						_a  )
   self.assertEqual(batch.input_ids.shape    ,						(2, 512)  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.get_tokenizer()
   lowerCamelCase_  				=						tok(["I am a tiny frog", "I am a small frog"]    ,						padding=_a    ,						return_tensors=_a  )
   self.assertIsInstance(_a    ,						_a  )
   self.assertEqual(batch_smaller.input_ids.shape    ,						(2, 10)  )






  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # fmt: off
   lowerCamelCase_  				=						{"input_ids": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=_a    ,						model_name="Helsinki-NLP/opus-mt-en-de"    ,						revision="1a8c2263da11e68e50938f97e10cd57820bd504c"    ,						decode_kwargs={"use_source_tokenizer": True}    ,						)







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"  )

   lowerCamelCase_  				=						"Tämä on testi"
   lowerCamelCase_  				=						"This is a test"

   lowerCamelCase_  				=						[76, 7, 2047, 2]
   lowerCamelCase_  				=						[69, 12, 11, 940, 2]

   lowerCamelCase_  				=						tokenizer(_a  ).input_ids
   self.assertListEqual(_a    ,						_a  )

   lowerCamelCase_  				=						tokenizer(text_target=_a  ).input_ids
   self.assertListEqual(_a    ,						_a  )

   lowerCamelCase_  				=						tokenizer.decode(_a    ,						skip_special_tokens=_a  )
   self.assertEqual(_a    ,						_a  )







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[int]=1024      ,       UpperCAmelCase_		:		int=1024      ,       UpperCAmelCase_		:		Optional[Any]=False      ,       **UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="train"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						tok.pad_token_id

 def get_lens(UpperCAmelCase_		:		str	):
  lowerCamelCase_  				=						tqdm(
      DataLoader(UpperCAmelCase_      ,       batch_size=512      ,       num_workers=8      ,       shuffle=UpperCAmelCase_      ,       collate_fn=ds.collate_fn	)      ,       desc=str(ds.len_file	)      ,       )
  lowerCamelCase_  				=						[]
  for batch in dl:
   lowerCamelCase_  				=						batch["input_ids"].ne(UpperCAmelCase_	).sum(1	).tolist()
   lowerCamelCase_  				=						batch["labels"].ne(UpperCAmelCase_	).sum(1	).tolist()
   if consider_target:
    for src, tgt in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
     max_lens.append(max(UpperCAmelCase_      ,       UpperCAmelCase_	)	)
   else:
    max_lens.extend(UpperCAmelCase_	)
  return max_lens

 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="val"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 pickle_save(UpperCAmelCase_      ,       train_ds.len_file	)
 pickle_save(UpperCAmelCase_      ,       val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)



