





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 				__UpperCamelCase  (		)  ->						Any:


   '''simple docstring'''







   __A				 =       Dataset.from_dict({'''i''': range(1_7     )}     )
   __A				 =       len(snake_case     )
   __A				 =       3
   __A				 =       [
       split_dataset_by_node(snake_case    ,	rank=snake_case    ,	world_size=snake_case     ) for rank in range(snake_case     )
   ]
   assert sum(len(snake_case     ) for ds in datasets_per_rank     ) == full_size
   assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size
def 				__UpperCamelCase  (		)  ->						Union[str, Any]:


   '''simple docstring'''







   def gen():
      return ({"i": i} for i in range(1_7     ))

   __A				 =       3
   __A				 =       IterableDataset.from_generator(snake_case     )
   __A				 =       len(list(snake_case     )     )
   __A				 =       [
       split_dataset_by_node(snake_case    ,	rank=snake_case    ,	world_size=snake_case     ) for rank in range(snake_case     )
   ]
   assert sum(len(list(snake_case     )     ) for ds in datasets_per_rank     ) == full_size
   assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size
@pytest.mark.parametrize('''shards_per_node'''    ,	[1, 2, 3]     )
def 				__UpperCamelCase  (		snake_case     )  ->						int:


   '''simple docstring'''







   def gen(snake_case     ):
      for shard in shards:
         yield from ({"i": i, "shard": shard} for i in range(1_7     ))

   __A				 =       3
   __A				 =       shards_per_node * world_size
   __A				 =       {'''shards''': [F"shard_{shard_idx}.txt" for shard_idx in range(snake_case     )]}
   __A				 =       IterableDataset.from_generator(snake_case    ,	gen_kwargs=snake_case     )
   __A				 =       len(list(snake_case     )     )
   assert full_ds.n_shards == world_size * shards_per_node
   __A				 =       [
       split_dataset_by_node(snake_case    ,	rank=snake_case    ,	world_size=snake_case     ) for rank in range(snake_case     )
   ]
   assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
   assert sum(len(list(snake_case     )     ) for ds in datasets_per_rank     ) == full_size
   assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size
def 				__UpperCamelCase  (		)  ->						Optional[int]:


   '''simple docstring'''







   def gen():
      return ({"i": i} for i in range(1_7     ))

   __A				 =       2
   __A				 =       IterableDataset.from_generator(snake_case     )
   __A				 =       len(list(snake_case     )     )

   __A				 =       split_dataset_by_node(snake_case    ,	rank=0    ,	world_size=snake_case     ).shuffle(seed=4_2     )
   assert len(list(snake_case     )     ) == 1 + full_size // world_size
   with pytest.raises(snake_case     ):
      split_dataset_by_node(snake_case    ,	rank=0    ,	world_size=snake_case     ).shuffle()

   __A				 =       split_dataset_by_node(full_ds.shuffle(seed=4_2     )    ,	rank=0    ,	world_size=snake_case     )
   assert len(list(snake_case     )     ) == 1 + full_size // world_size
   with pytest.raises(snake_case     ):
      split_dataset_by_node(full_ds.shuffle()    ,	rank=0    ,	world_size=snake_case     )
@pytest.mark.parametrize('''streaming'''    ,	[False, True]     )
@require_torch
@pytest.mark.skipif(os.name == '''nt'''    ,	reason='''execute_subprocess_async doesn\'t support windows'''     )
@pytest.mark.integration
def 				__UpperCamelCase  (		snake_case     )  ->						Union[str, Any]:


   '''simple docstring'''







   __A				 =       2
   __A				 =       get_torch_dist_unique_port()
   __A				 =       Path(snake_case     ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
   __A				 =       F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
   __A				 =       F"\n        --streaming={streaming}\n    ".split()
   __A				 =       [sys.executable] + distributed_args + args
   execute_subprocess_async(snake_case    ,	env=os.environ.copy()     )
@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''    ,	[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]    ,	)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''    ,	reason='''execute_subprocess_async doesn\'t support windows'''     )
@pytest.mark.integration
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						str:


   '''simple docstring'''







   __A				 =       True
   __A				 =       get_torch_dist_unique_port()
   __A				 =       Path(snake_case     ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
   __A				 =       F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
   __A				 =       F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
   __A				 =       [sys.executable] + distributed_args + args
   execute_subprocess_async(snake_case    ,	env=os.environ.copy()     )








import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
_UpperCamelCase   :							str     =	"""stas/tiny-wmt19-en-ru"""







class 						_lowerCAmelCase(						_a		,   unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__      					=   FSMTTokenizer
  lowerCamelCase__      					=   False

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __A				 =       [
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''w</w>''',
         '''r</w>''',
         '''t</w>''',
         '''lo''',
         '''low''',
         '''er</w>''',
         '''low</w>''',
         '''lowest</w>''',
         '''newer</w>''',
         '''wider</w>''',
         '''<unk>''',
     ]
     __A				 =       dict(zip(UpperCAmelCase  ,    range(len(UpperCAmelCase     )     )     )     )
     __A				 =       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     __A				 =       ['''en''', '''ru''']
     __A				 =       {
         '''langs''': self.langs,
         '''src_vocab_size''': 10,
         '''tgt_vocab_size''': 20,
     }

     __A				 =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''src_vocab_file''']     )
     __A				 =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''tgt_vocab_file''']     )
     __A				 =       os.path.join(self.tmpdirname  ,    '''tokenizer_config.json'''     )
     __A				 =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''merges_file''']     )
     with open(self.src_vocab_file  ,    '''w'''     ) as fp:
        fp.write(json.dumps(UpperCAmelCase     )     )
     with open(self.tgt_vocab_file  ,    '''w'''     ) as fp:
        fp.write(json.dumps(UpperCAmelCase     )     )
     with open(self.merges_file  ,    '''w'''     ) as fp:
        fp.write('''\n'''.join(UpperCAmelCase     )     )
     with open(UpperCAmelCase  ,    '''w'''     ) as fp:
        fp.write(json.dumps(UpperCAmelCase     )     )

  @cached_property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''     )

  @cached_property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       FSMTTokenizer.from_pretrained(UpperCAmelCase     )
     self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]  ,    ['''en''', '''ru''']     )
     self.assertEqual(tokenizer.src_vocab_size  ,    21     )
     self.assertEqual(tokenizer.tgt_vocab_size  ,    21     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     __A				 =       FSMTTokenizer(self.langs  ,    self.src_vocab_file  ,    self.tgt_vocab_file  ,    self.merges_file     )

     __A				 =       '''lower'''
     __A				 =       ['''low''', '''er</w>''']
     __A				 =       tokenizer.tokenize(UpperCAmelCase     )
     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

     __A				 =       tokens + ['''<unk>''']
     __A				 =       [14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase     )  ,    UpperCAmelCase     )

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       self.tokenizer_ru_en

     __A				 =       tokenizer.encode('''sequence builders'''  ,    add_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.encode('''multi-sequence build'''  ,    add_special_tokens=UpperCAmelCase     )

     __A				 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase     )
     __A				 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase  ,    UpperCAmelCase     )

     assert encoded_sentence == text + [2]
     assert encoded_pair == text + [2] + text_a + [2]

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       self.tokenizer_en_ru
     __A				 =       self.tokenizer_ru_en

     __A				 =       [
         [
             '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
             [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
         ],
         ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
     ]

     # if data needs to be recreated or added, run:
     # import torch
     # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
     # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

     for src_text, tgt_input_ids in targets:
        __A				 =       tokenizer_enc.encode(UpperCAmelCase  ,    return_tensors=UpperCAmelCase     )
        self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

        # and decode backward, using the reversed languages model
        __A				 =       tokenizer_dec.decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )
        self.assertEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''  ,    do_lower_case=UpperCAmelCase     )
     __A				 =       tokenizer.tokenize('''USA is United States of America'''     )
     __A				 =       ['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''     )
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     pass





  @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''     )
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     pass

