





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			:  List[str]										=				"stas/tiny-wmt19-en-ru"







class        __snake_case			(				__lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	FSMTTokenizer
       lowerCAmelCase__ 						=	False




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __snake_case:		str					    =		[
             """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>""",
         ]
         __snake_case:		int					    =		dict(zip(A				,							range(len(A						)						)						)						)
         __snake_case:		List[Any]					    =		["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

         __snake_case:		Optional[Any]					    =		["""en""", """ru"""]
         __snake_case:		Union[str, Any]					    =		{
             """langs""": self.langs,
             """src_vocab_size""": 10,
             """tgt_vocab_size""": 20,
         }

         __snake_case:		Optional[Any]					    =		os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES["""src_vocab_file"""]						)
         __snake_case:		Optional[int]					    =		os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES["""tgt_vocab_file"""]						)
         __snake_case:		str					    =		os.path.join(self.tmpdirname				,							"""tokenizer_config.json"""						)
         __snake_case:		str					    =		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 				UpperCAmelCase__					(    self					:       Tuple						):
         return FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""						)




       @cached_property
       def 				UpperCAmelCase__					(    self					:       List[str]						):
         return FSMTTokenizer.from_pretrained("""facebook/wmt19-en-ru"""						)




       def 				UpperCAmelCase__					(    self					:       Dict						):
         __snake_case:		Union[str, Any]					    =		FSMTTokenizer.from_pretrained(A						)
         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 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         __snake_case:		str					    =		FSMTTokenizer(self.langs				,							self.src_vocab_file				,							self.tgt_vocab_file				,							self.merges_file						)

         __snake_case:		Dict					    =		"""lower"""
         __snake_case:		List[str]					    =		["""low""", """er</w>"""]
         __snake_case:		Tuple					    =		tokenizer.tokenize(A						)
         self.assertListEqual(A				,							A						)

         __snake_case:		str					    =		tokens + ["""<unk>"""]
         __snake_case:		Tuple					    =		[14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A						)				,							A						)




       @slow
       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Any					    =		self.tokenizer_ru_en

         __snake_case:		Union[str, Any]					    =		tokenizer.encode("""sequence builders"""				,							add_special_tokens=A						)
         __snake_case:		Union[str, Any]					    =		tokenizer.encode("""multi-sequence build"""				,							add_special_tokens=A						)

         __snake_case:		List[Any]					    =		tokenizer.build_inputs_with_special_tokens(A						)
         __snake_case:		Dict					    =		tokenizer.build_inputs_with_special_tokens(A				,							A						)

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




       @slow
       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		Tuple					    =		self.tokenizer_en_ru
         __snake_case:		int					    =		self.tokenizer_ru_en

         __snake_case:		List[Any]					    =		[
             [
                 """Here's a little song I wrote. Don't worry, be happy.""",
                 [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
             ],
             ["""This is it. No more. I'm done!""", [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 427, 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:
           __snake_case:		int					    =		tokenizer_enc.encode(A				,							return_tensors=A						)
           self.assertListEqual(A				,							A						)

           # and decode backward, using the reversed languages model
           __snake_case:		Dict					    =		tokenizer_dec.decode(A				,							skip_special_tokens=A						)
           self.assertEqual(A				,							A						)




       @slow
       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Union[str, Any]					    =		FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""				,							do_lower_case=A						)
         __snake_case:		List[Any]					    =		tokenizer.tokenize("""USA is United States of America"""						)
         __snake_case:		int					    =		["""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 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         pass






       @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""						)
       def 				UpperCAmelCase__					(    self					:       int						):
         pass













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase			:  List[str]										=				{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  Any										=				["LevitFeatureExtractor"]
      __UpperCAmelCase			:  Tuple										=				["LevitImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  str										=				[
          "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LevitForImageClassification",
          "LevitForImageClassificationWithTeacher",
          "LevitModel",
          "LevitPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_levit import LevitFeatureExtractor
            from .image_processing_levit import LevitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_levit import (
                LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                LevitForImageClassification,
                LevitForImageClassificationWithTeacher,
                LevitModel,
                LevitPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase			:  Union[str, Any]										=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






