

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
snake_case_    						=				'''stas/tiny-wmt19-en-ru'''


class    SCREAMING_SNAKE_CASE__      (__snake_case	, unittest.TestCase  ):
				__lowerCamelCase		: Union[str, Any]														=  FSMTTokenizer
				__lowerCamelCase		: Optional[Any]														=  False





				def    snake_case_       (   self):
									super().setUp()

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

									lowercase__					:     Optional[int]   = ['en', 'ru']
									lowercase__					:     Dict   = {
									    'langs': self.langs,
									    'src_vocab_size': 10,
									    'tgt_vocab_size': 20,
									}

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





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





				def    snake_case_       (   self):
									lowercase__					:     List[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    snake_case_       (   self):
									lowercase__					:     Tuple   = FSMTTokenizer(self.langs		,     self.src_vocab_file		,     self.tgt_vocab_file		,     self.merges_file)

									lowercase__					:     Union[str, Any]   = 'lower'
									lowercase__					:     Union[str, Any]   = ['low', 'er</w>']
									lowercase__					:     Dict   = tokenizer.tokenize(a)
									self.assertListEqual(a		,     a)

									lowercase__					:     List[str]   = tokens + ['<unk>']
									lowercase__					:     int   = [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)





				@slow
				def    snake_case_       (   self):
									lowercase__					:     Union[str, Any]   = self.tokenizer_ru_en

									lowercase__					:     Tuple   = tokenizer.encode('sequence builders'		,     add_special_tokens=a)
									lowercase__					:     int   = tokenizer.encode('multi-sequence build'		,     add_special_tokens=a)

									lowercase__					:     Union[str, Any]   = tokenizer.build_inputs_with_special_tokens(a)
									lowercase__					:     Dict   = tokenizer.build_inputs_with_special_tokens(a		,     a)

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





				@slow
				def    snake_case_       (   self):
									lowercase__					:     Tuple   = self.tokenizer_en_ru
									lowercase__					:     str   = self.tokenizer_ru_en

									lowercase__					:     Any   = [
									    [
									        'Here\'s a little song I wrote. Don\'t worry, be happy.',
									        [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 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:
														lowercase__					:     List[str]   = tokenizer_enc.encode(a		,     return_tensors=a)
														self.assertListEqual(a		,     a)

														# and decode backward, using the reversed languages model
														lowercase__					:     Union[str, Any]   = tokenizer_dec.decode(a		,     skip_special_tokens=a)
														self.assertEqual(a		,     a)





				@slow
				def    snake_case_       (   self):
									lowercase__					:     Tuple   = FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'		,     do_lower_case=a)
									lowercase__					:     Dict   = tokenizer.tokenize('USA is United States of America')
									lowercase__					:     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    snake_case_       (   self):
									pass





				@unittest.skip('FSMTConfig.__init__  requires non-optional args')
				def    snake_case_       (   self):
									pass




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''GPTJForCausalLM''',
           '''GPTJForQuestionAnswering''',
           '''GPTJForSequenceClassification''',
           '''GPTJModel''',
           '''GPTJPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''TFGPTJForCausalLM''',
           '''TFGPTJForQuestionAnswering''',
           '''TFGPTJForSequenceClassification''',
           '''TFGPTJModel''',
           '''TFGPTJPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''FlaxGPTJForCausalLM''',
           '''FlaxGPTJModel''',
           '''FlaxGPTJPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptj import (
                  GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTJForCausalLM,
                  GPTJForQuestionAnswering,
                  GPTJForSequenceClassification,
                  GPTJModel,
                  GPTJPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_gptj import (
                  TFGPTJForCausalLM,
                  TFGPTJForQuestionAnswering,
                  TFGPTJForSequenceClassification,
                  TFGPTJModel,
                  TFGPTJPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
       import sys

       snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

