
"""simple docstring"""


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 :    Tuple								=						"""stas/tiny-wmt19-en-ru"""

class    A_     (					A__				, unittest.TestCase       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       FSMTTokenizer
							SCREAMING_SNAKE_CASE_     				=       False





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										super().setUp()

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

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

										lowerCamelCase__     :					int		=os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['src_vocab_file']	)
										lowerCamelCase__     :					List[Any]		=os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['tgt_vocab_file']	)
										lowerCamelCase__     :					List[str]		=os.path.join(self.tmpdirname					,							'tokenizer_config.json'	)
										lowerCamelCase__     :					int		=os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['merges_file']	)
										with open(self.src_vocab_file					,							'w'	) as fp:
													fp.write(json.dumps(lowerCamelCase_	)	)
										with open(self.tgt_vocab_file					,							'w'	) as fp:
													fp.write(json.dumps(lowerCamelCase_	)	)
										with open(self.merges_file					,							'w'	) as fp:
													fp.write('\n'.join(lowerCamelCase_	)	)
										with open(lowerCamelCase_					,							'w'	) as fp:
													fp.write(json.dumps(lowerCamelCase_	)	)





							@cached_property
							def 				UpperCAmelCase__							(  self  :str	):





										"""simple docstring"""

										return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'	)





							@cached_property
							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'	)





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





										"""simple docstring"""

										lowerCamelCase__     :					Any		=FSMTTokenizer.from_pretrained(lowerCamelCase_	)
										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  :int	):





										"""simple docstring"""

										lowerCamelCase__     :					str		=FSMTTokenizer(self.langs					,							self.src_vocab_file					,							self.tgt_vocab_file					,							self.merges_file	)

										lowerCamelCase__     :					List[str]		='lower'
										lowerCamelCase__     :					Tuple		=['low', 'er</w>']
										lowerCamelCase__     :					int		=tokenizer.tokenize(lowerCamelCase_	)
										self.assertListEqual(lowerCamelCase_					,							lowerCamelCase_	)

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





							@slow
							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[int]		=self.tokenizer_ru_en

										lowerCamelCase__     :					Tuple		=tokenizer.encode('sequence builders'					,							add_special_tokens=lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=tokenizer.encode('multi-sequence build'					,							add_special_tokens=lowerCamelCase_	)

										lowerCamelCase__     :					Tuple		=tokenizer.build_inputs_with_special_tokens(lowerCamelCase_	)
										lowerCamelCase__     :					str		=tokenizer.build_inputs_with_special_tokens(lowerCamelCase_					,							lowerCamelCase_	)

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





							@slow
							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=self.tokenizer_en_ru
										lowerCamelCase__     :					int		=self.tokenizer_ru_en

										lowerCamelCase__     :					str		=[
										    [
										        '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:
													lowerCamelCase__     :					Union[str, Any]		=tokenizer_enc.encode(lowerCamelCase_					,							return_tensors=lowerCamelCase_	)
													self.assertListEqual(lowerCamelCase_					,							lowerCamelCase_	)

													# and decode backward, using the reversed languages model
													lowerCamelCase__     :					Tuple		=tokenizer_dec.decode(lowerCamelCase_					,							skip_special_tokens=lowerCamelCase_	)
													self.assertEqual(lowerCamelCase_					,							lowerCamelCase_	)





							@slow
							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		=FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'					,							do_lower_case=lowerCamelCase_	)
										lowerCamelCase__     :					Optional[Any]		=tokenizer.tokenize('USA is United States of America'	)
										lowerCamelCase__     :					int		=['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
										self.assertListEqual(lowerCamelCase_					,							lowerCamelCase_	)





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





										"""simple docstring"""

										pass





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





										"""simple docstring"""

										pass

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase								=						{
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DecisionTransformerGPT2Model""",
							    """DecisionTransformerGPT2PreTrainedModel""",
							    """DecisionTransformerModel""",
							    """DecisionTransformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_decision_transformer import (
							    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DecisionTransformerConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_decision_transformer import (
														    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DecisionTransformerGPTaModel,
														    DecisionTransformerGPTaPreTrainedModel,
														    DecisionTransformerModel,
														    DecisionTransformerPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)