






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
a	: Any      =				"stas/tiny-wmt19-en-ru"
class 					a					(				lowercase__							, unittest.TestCase     ):



      """simple docstring"""


      a						:				Optional[Any]							 =			FSMTTokenizer
      a						:				List[Any]							 =			False
      def 			UpperCAmelCase   (   self      :    Any      )							->   str:
        super().setUp()

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

        __UpperCAmelCase							:			List[str]     =						["""en""", """ru"""]
        __UpperCAmelCase							:			Optional[int]     =						{
            """langs""": self.langs,
            """src_vocab_size""": 10,
            """tgt_vocab_size""": 20,
        }

        __UpperCAmelCase							:			Any     =						os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES["""src_vocab_file"""]      )
        __UpperCAmelCase							:			List[str]     =						os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES["""tgt_vocab_file"""]      )
        __UpperCAmelCase							:			Dict     =						os.path.join(self.tmpdirname	,   """tokenizer_config.json"""      )
        __UpperCAmelCase							:			Union[str, Any]     =						os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES["""merges_file"""]      )
        with open(self.src_vocab_file	,   """w"""      ) as fp:
          fp.write(json.dumps(__lowercase      )      )
        with open(self.tgt_vocab_file	,   """w"""      ) as fp:
          fp.write(json.dumps(__lowercase      )      )
        with open(self.merges_file	,   """w"""      ) as fp:
          fp.write("""\n""".join(__lowercase      )      )
        with open(__lowercase	,   """w"""      ) as fp:
          fp.write(json.dumps(__lowercase      )      )
      @cached_property
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   Tuple:
        return FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""      )
      @cached_property
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   List[str]:
        return FSMTTokenizer.from_pretrained("""facebook/wmt19-en-ru"""      )
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   Optional[Any]:
        __UpperCAmelCase							:			Optional[int]     =						FSMTTokenizer.from_pretrained(__lowercase      )
        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      :    Tuple      )							->   int:
        __UpperCAmelCase							:			List[Any]     =						FSMTTokenizer(self.langs	,   self.src_vocab_file	,   self.tgt_vocab_file	,   self.merges_file      )

        __UpperCAmelCase							:			Optional[int]     =						"""lower"""
        __UpperCAmelCase							:			int     =						["""low""", """er</w>"""]
        __UpperCAmelCase							:			Any     =						tokenizer.tokenize(__lowercase      )
        self.assertListEqual(__lowercase	,   __lowercase      )

        __UpperCAmelCase							:			Optional[int]     =						tokens + ["""<unk>"""]
        __UpperCAmelCase							:			List[Any]     =						[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase      )	,   __lowercase      )
      @slow
      def 			UpperCAmelCase   (   self      :    int      )							->   Tuple:
        __UpperCAmelCase							:			List[Any]     =						self.tokenizer_ru_en

        __UpperCAmelCase							:			int     =						tokenizer.encode("""sequence builders"""	,   add_special_tokens=__lowercase      )
        __UpperCAmelCase							:			List[Any]     =						tokenizer.encode("""multi-sequence build"""	,   add_special_tokens=__lowercase      )

        __UpperCAmelCase							:			Dict     =						tokenizer.build_inputs_with_special_tokens(__lowercase      )
        __UpperCAmelCase							:			Union[str, Any]     =						tokenizer.build_inputs_with_special_tokens(__lowercase	,   __lowercase      )

        assert encoded_sentence == text + [2]
        assert encoded_pair == text + [2] + text_a + [2]
      @slow
      def 			UpperCAmelCase   (   self      :    int      )							->   List[str]:
        __UpperCAmelCase							:			Optional[Any]     =						self.tokenizer_en_ru
        __UpperCAmelCase							:			Union[str, Any]     =						self.tokenizer_ru_en

        __UpperCAmelCase							:			List[str]     =						[
            [
                """Here's a little song I wrote. Don't worry, be happy.""",
                [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 5, 26, 6445, 7, 2],
            ],
            ["""This is it. No more. I'm done!""", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 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:
          __UpperCAmelCase							:			Tuple     =						tokenizer_enc.encode(__lowercase	,   return_tensors=__lowercase      )
          self.assertListEqual(__lowercase	,   __lowercase      )

          # and decode backward, using the reversed languages model
          __UpperCAmelCase							:			str     =						tokenizer_dec.decode(__lowercase	,   skip_special_tokens=__lowercase      )
          self.assertEqual(__lowercase	,   __lowercase      )
      @slow
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   Dict:
        __UpperCAmelCase							:			int     =						FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""	,   do_lower_case=__lowercase      )
        __UpperCAmelCase							:			Dict     =						tokenizer.tokenize("""USA is United States of America"""      )
        __UpperCAmelCase							:			Tuple     =						["""us""", """a</w>""", """is</w>""", """un""", """i""", """ted</w>""", """st""", """ates</w>""", """of</w>""", """am""", """er""", """ica</w>"""]
        self.assertListEqual(__lowercase	,   __lowercase      )
      @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""      )
      def 			UpperCAmelCase   (   self      :    Dict      )							->   Any:
        pass




      @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""      )
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   Optional[int]:
        pass










from typing import TYPE_CHECKING

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


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


