



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
__lowerCamelCase      =    """stas/tiny-wmt19-en-ru"""



class 						_snake_case					(	a_    ,unittest.TestCase ):





 """simple docstring"""



 lowerCamelCase_								=	FSMTTokenizer
 lowerCamelCase_								=	False
 def lowercase_						(  self    )		->							Union[str, Any]:




   """simple docstring"""







   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(a		,			range(len(a    )    )    )    )
   _A							=			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

   _A							=			['en', 'ru']
   _A							=			{
       'langs': self.langs,
       'src_vocab_size': 1_0,
       'tgt_vocab_size': 2_0,
   }

   _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(a    )    )
   with open(self.tgt_vocab_file		,			'''w'''    ) as fp:
     fp.write(json.dumps(a    )    )
   with open(self.merges_file		,			'''w'''    ) as fp:
     fp.write('''\n'''.join(a    )    )
   with open(a		,			'''w'''    ) as fp:
     fp.write(json.dumps(a    )    )
 @cached_property
 def lowercase_						(  self    )		->							Any:




   """simple docstring"""







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




   """simple docstring"""







   return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''    )
 def lowercase_						(  self    )		->							Any:




   """simple docstring"""







   _A							=			FSMTTokenizer.from_pretrained(a    )
   self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]		,			['''en''', '''ru''']    )
   self.assertEqual(tokenizer.src_vocab_size		,			2_1    )
   self.assertEqual(tokenizer.tgt_vocab_size		,			2_1    )
 def lowercase_						(  self    )		->							List[Any]:




   """simple docstring"""







   _A							=			FSMTTokenizer(self.langs		,			self.src_vocab_file		,			self.tgt_vocab_file		,			self.merges_file    )

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

   _A							=			tokens + ['<unk>']
   _A							=			[1_4, 1_5, 2_0]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(a    )		,			a    )
 @slow
 def lowercase_						(  self    )		->							Union[str, Any]:




   """simple docstring"""







   _A							=			self.tokenizer_ru_en

   _A							=			tokenizer.encode('''sequence builders'''		,			add_special_tokens=a    )
   _A							=			tokenizer.encode('''multi-sequence build'''		,			add_special_tokens=a    )

   _A							=			tokenizer.build_inputs_with_special_tokens(a    )
   _A							=			tokenizer.build_inputs_with_special_tokens(a		,			a    )

   assert encoded_sentence == text + [2]
   assert encoded_pair == text + [2] + text_a + [2]
 @slow
 def lowercase_						(  self    )		->							Optional[int]:




   """simple docstring"""







   _A							=			self.tokenizer_en_ru
   _A							=			self.tokenizer_ru_en

   _A							=			[
       [
           'Here\'s a little song I wrote. Don\'t worry, be happy.',
           [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
       ],
       ['This is it. No more. I\'m done!', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 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(a		,			return_tensors=a    )
     self.assertListEqual(a		,			a    )

     # and decode backward, using the reversed languages model
     _A							=			tokenizer_dec.decode(a		,			skip_special_tokens=a    )
     self.assertEqual(a		,			a    )
 @slow
 def lowercase_						(  self    )		->							Tuple:




   """simple docstring"""







   _A							=			FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''		,			do_lower_case=a    )
   _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(a		,			a    )
 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''    )
 def lowercase_						(  self    )		->							List[str]:




   """simple docstring"""







   pass



 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''    )
 def lowercase_						(  self    )		->							int:




   """simple docstring"""







   pass
import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		Any ):
       if version.parse(lowercase_ ) < version.parse(lowercase_ ):
              if "dev" in min_version:
                     _UpperCamelCase   :				Tuple       =     (
                         'This example requires a source install from HuggingFace diffusers (see '
                         '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
                     )
              else:
                     _UpperCamelCase   :				str       =     F"This example requires a minimum version of {min_version},"
              error_message += F" but the version found is {__version__}.\n"
              raise ImportError(lowercase_ )



