
'''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
__lowercase							:				Dict					=       '''stas/tiny-wmt19-en-ru'''

class 		__lowercase			(					_lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						str		 =     FSMTTokenizer
			lowerCamelCase     :						List[str]		 =     False

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

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

								lowerCamelCase_   : int       =						['''en''', '''ru''']
								lowerCamelCase_   : List[str]       =						{
								    '''langs''': self.langs,
								    '''src_vocab_size''': 1_0,
								    '''tgt_vocab_size''': 2_0,
								}

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

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

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Any       =						FSMTTokenizer.from_pretrained(A )
								self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]     ,				['''en''', '''ru'''] )
								self.assertEqual(tokenizer.src_vocab_size     ,				2_1 )
								self.assertEqual(tokenizer.tgt_vocab_size     ,				2_1 )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						FSMTTokenizer(self.langs     ,				self.src_vocab_file     ,				self.tgt_vocab_file     ,				self.merges_file )

								lowerCamelCase_   : Optional[Any]       =						'''lower'''
								lowerCamelCase_   : Dict       =						['''low''', '''er</w>''']
								lowerCamelCase_   : List[Any]       =						tokenizer.tokenize(A )
								self.assertListEqual(A     ,				A )

								lowerCamelCase_   : List[Any]       =						tokens + ['''<unk>''']
								lowerCamelCase_   : Union[str, Any]       =						[1_4, 1_5, 2_0]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A )     ,				A )

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

								lowerCamelCase_   : List[str]       =						tokenizer.encode('''sequence builders'''     ,				add_special_tokens=A )
								lowerCamelCase_   : str       =						tokenizer.encode('''multi-sequence build'''     ,				add_special_tokens=A )

								lowerCamelCase_   : Union[str, Any]       =						tokenizer.build_inputs_with_special_tokens(A )
								lowerCamelCase_   : Any       =						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 ):
								lowerCamelCase_   : str       =						self.tokenizer_en_ru
								lowerCamelCase_   : int       =						self.tokenizer_ru_en

								lowerCamelCase_   : int       =						[
								    [
								        '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
								        [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
								    ],
								    ['''This is it. No more. I\'m done!''', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 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_   : str       =						tokenizer_enc.encode(A     ,				return_tensors=A )
													self.assertListEqual(A     ,				A )

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

			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''     ,				do_lower_case=A )
								lowerCamelCase_   : Optional[Any]       =						tokenizer.tokenize('''USA is United States of America''' )
								lowerCamelCase_   : List[Any]       =						['''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 ):
								pass






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




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

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


