



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__			:   Optional[int]      			=    'stas/tiny-wmt19-en-ru'





class 							lowerCAmelCase_		(		lowercase_							,		unittest.TestCase     ):
		SCREAMING_SNAKE_CASE_       :			int					       =	FSMTTokenizer
		SCREAMING_SNAKE_CASE_       :			Dict					       =	False







		def 					a_  (    self  :   Optional[int]					)       ->		int:


						'''simple docstring'''







						super().setUp()

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

						_UpperCAmelCase						:  List[Any]     					=	['''en''', '''ru''']
						_UpperCAmelCase						:  Optional[int]     					=	{
						    '''langs''': self.langs,
						    '''src_vocab_size''': 10,
						    '''tgt_vocab_size''': 20,
						}

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







		@cached_property
		def 					a_  (    self  :   Union[str, Any]					)       ->		Union[str, Any]:


						'''simple docstring'''







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







		@cached_property
		def 					a_  (    self  :   str					)       ->		Dict:


						'''simple docstring'''







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







		def 					a_  (    self  :   Tuple					)       ->		Any:


						'''simple docstring'''







						_UpperCAmelCase						:  List[Any]     					=	FSMTTokenizer.from_pretrained(UpperCAmelCase_					)
						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 					a_  (    self  :   List[str]					)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	FSMTTokenizer(self.langs       ,		self.src_vocab_file       ,		self.tgt_vocab_file       ,		self.merges_file					)

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

						_UpperCAmelCase						:  int     					=	tokens + ['''<unk>''']
						_UpperCAmelCase						:  Tuple     					=	[14, 15, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)       ,		UpperCAmelCase_					)







		@slow
		def 					a_  (    self  :   Any					)       ->		Union[str, Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Any     					=	self.tokenizer_ru_en

						_UpperCAmelCase						:  Optional[int]     					=	tokenizer.encode('''sequence builders'''       ,		add_special_tokens=UpperCAmelCase_					)
						_UpperCAmelCase						:  List[str]     					=	tokenizer.encode('''multi-sequence build'''       ,		add_special_tokens=UpperCAmelCase_					)

						_UpperCAmelCase						:  int     					=	tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_					)
						_UpperCAmelCase						:  List[str]     					=	tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_       ,		UpperCAmelCase_					)

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







		@slow
		def 					a_  (    self  :   Tuple					)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[int]     					=	self.tokenizer_en_ru
						_UpperCAmelCase						:  Dict     					=	self.tokenizer_ru_en

						_UpperCAmelCase						:  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						:  Dict     					=	tokenizer_enc.encode(UpperCAmelCase_       ,		return_tensors=UpperCAmelCase_					)
										self.assertListEqual(UpperCAmelCase_       ,		UpperCAmelCase_					)

										# and decode backward, using the reversed languages model
										_UpperCAmelCase						:  List[Any]     					=	tokenizer_dec.decode(UpperCAmelCase_       ,		skip_special_tokens=UpperCAmelCase_					)
										self.assertEqual(UpperCAmelCase_       ,		UpperCAmelCase_					)







		@slow
		def 					a_  (    self  :   str					)       ->		Dict:


						'''simple docstring'''







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







		@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''					)
		def 					a_  (    self  :   Tuple					)       ->		List[str]:


						'''simple docstring'''







						pass







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


						'''simple docstring'''







						pass





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__			:   Optional[int]      			=    {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   List[Any]      			=    ['ErnieMTokenizer']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__			:   Dict      			=    [
					    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ErnieMForMultipleChoice',
					    'ErnieMForQuestionAnswering',
					    'ErnieMForSequenceClassification',
					    'ErnieMForTokenClassification',
					    'ErnieMModel',
					    'ErnieMPreTrainedModel',
					    'ErnieMForInformationExtraction',
					]


if TYPE_CHECKING:
					from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ernie_m import ErnieMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ernie_m import (
										    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ErnieMForInformationExtraction,
										    ErnieMForMultipleChoice,
										    ErnieMForQuestionAnswering,
										    ErnieMForSequenceClassification,
										    ErnieMForTokenClassification,
										    ErnieMModel,
										    ErnieMPreTrainedModel,
										)


else:
					import sys

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