"""simple docstring"""



import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
				from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
				_UpperCamelCase								=		get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
				from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_UpperCamelCase								=		12_80_22
_UpperCamelCase								=		12_80_28
@require_sentencepiece
class 						__UpperCAmelCase    (a_							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						List[str]					     =				MaMaaaTokenizer
			_UpperCamelCase  :						Tuple					     =				False
			_UpperCamelCase  :						Optional[int]					     =				False
			_UpperCamelCase  :						str					     =				True





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										super().setUp()

										A__       :     Optional[int]								=			['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
										A__       :     List[str]								=			dict(zip(lowercase_   ,      range(len(lowercase_							)							)							)							)
										A__       :     str								=			Path(self.tmpdirname							)
										save_json(lowercase_   ,      save_dir / VOCAB_FILES_NAMES["""vocab_file"""]							)
										if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																	copyfile(lowercase_   ,      save_dir / VOCAB_FILES_NAMES["""spm_file"""]							)

										A__       :     Optional[int]								=			MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
										tokenizer.save_pretrained(self.tmpdirname							)





			def 		lowerCamelCase					(			self   ,      **snake_case_							):







										'''simple docstring'''


										return MaMaaaTokenizer.from_pretrained(self.tmpdirname   ,      **lowercase_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return (
										    "This is a test",
										    "This is a test",
										)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			'''</s>'''
										A__       :     List[Any]								=			0

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_							)   ,      lowercase_							)
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_							)   ,      lowercase_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.get_tokenizer()
										A__       :     List[Any]								=			list(tokenizer.get_vocab().keys()							)

										self.assertEqual(vocab_keys[0]   ,      """</s>"""							)
										self.assertEqual(vocab_keys[1]   ,      """<unk>"""							)
										self.assertEqual(vocab_keys[-1]   ,      """<s>"""							)
										self.assertEqual(len(lowercase_							)   ,      tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)





			@unittest.skip("""Skip this test while all models are still to be uploaded."""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			self.get_tokenizer()

										A__       :     Union[str, Any]								=			tokenizer.tokenize("""This is a test"""							)
										self.assertListEqual(lowercase_   ,      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowercase_							)   ,      [2, 3, 4, 5, 6]   ,      )

										A__       :     Any								=			tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
										self.assertListEqual(lowercase_   ,      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

										A__       :     List[Any]								=			tokenizer.convert_tokens_to_string(lowercase_							)
										self.assertEqual(lowercase_   ,      """This is a test"""							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			{'''input_ids''': [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=lowercase_   ,      model_name="""facebook/m2m100_418M"""   ,      revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""   ,      )





@require_torch
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						List[str]					     =				'facebook/m2m100_418M'
			_UpperCamelCase  :						List[Any]					     =				[
			    'In my opinion, there are two levels of response from the French government.',
			    'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
			]
			_UpperCamelCase  :						int					     =				[
			    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
			    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
			]

			# fmt: off
			_UpperCamelCase  :						int					     =				[EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]





			@classmethod
			def 		lowerCamelCase					(			cls							):







										'''simple docstring'''


										A__       :     MaMaaaTokenizer								=			MaMaaaTokenizer.from_pretrained(
										    cls.checkpoint_name   ,      src_lang="""en"""   ,      tgt_lang="""fr"""							)
										A__       :     Union[str, Any]								=			1
										return cls





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self.assertEqual(self.tokenizer.get_lang_id("""ar"""							)   ,      128_006							)
										self.assertEqual(self.tokenizer.get_lang_id("""en"""							)   ,      128_022							)
										self.assertEqual(self.tokenizer.get_lang_id("""ro"""							)   ,      128_076							)
										self.assertEqual(self.tokenizer.get_lang_id("""mr"""							)   ,      128_063							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.tokenizer.get_vocab()
										self.assertEqual(len(lowercase_							)   ,      self.tokenizer.vocab_size							)
										self.assertEqual(vocab["""<unk>"""]   ,      3							)
										self.assertIn(self.tokenizer.get_lang_token("""en"""							)   ,      lowercase_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Dict								=			'''en'''
										A__       :     Tuple								=			self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
										self.assertListEqual(self.expected_src_tokens   ,      lowercase_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self.assertIn(lowercase_   ,      self.tokenizer.all_special_ids							)
										# fmt: off
										A__       :     Optional[int]								=			[FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
										# fmt: on
										A__       :     Any								=			self.tokenizer.decode(lowercase_   ,      skip_special_tokens=lowercase_							)
										A__       :     Optional[Any]								=			self.tokenizer.decode(generated_ids[1:]   ,      skip_special_tokens=lowercase_							)
										self.assertEqual(lowercase_   ,      lowercase_							)
										self.assertNotIn(self.tokenizer.eos_token   ,      lowercase_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			tempfile.mkdtemp()
										A__       :     Dict								=			self.tokenizer.lang_token_to_id
										self.tokenizer.save_pretrained(lowercase_							)
										A__       :     str								=			MaMaaaTokenizer.from_pretrained(lowercase_							)
										self.assertDictEqual(new_tok.lang_token_to_id   ,      lowercase_							)





			@require_torch
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			'''en'''
										A__       :     Optional[int]								=			'''fr'''

										A__       :     int								=			self.tokenizer(self.src_text   ,      text_target=self.tgt_text   ,      padding=lowercase_   ,      return_tensors="""pt"""							)

										A__       :     List[str]								=			shift_tokens_right(
										    batch["""labels"""]   ,      self.tokenizer.pad_token_id   ,      self.tokenizer.eos_token_id							)

										for k in batch:
																	A__       :     Tuple								=			batch[k].tolist()
										# batch = {k: v.tolist() for k,v in batch.items()}
										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										# batch.decoder_inputs_ids[0][0] ==
										assert batch.input_ids[1][0] == EN_CODE
										assert batch.input_ids[1][-1] == 2
										assert batch.labels[1][0] == FR_CODE
										assert batch.labels[1][-1] == 2
										assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]





			@require_torch
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			'''mr'''
										self.assertListEqual(self.tokenizer.prefix_tokens   ,      [self.tokenizer.get_lang_id("""mr"""							)]							)
										self.assertListEqual(self.tokenizer.suffix_tokens   ,      [self.tokenizer.eos_token_id]							)

										A__       :     Tuple								=			'''zh'''
										self.assertListEqual(self.tokenizer.prefix_tokens   ,      [self.tokenizer.get_lang_id("""zh"""							)]							)
										self.assertListEqual(self.tokenizer.suffix_tokens   ,      [self.tokenizer.eos_token_id]							)





			@require_torch
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			'''mr'''
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens   ,      [self.tokenizer.get_lang_id("""mr"""							)]							)
										self.assertListEqual(self.tokenizer.suffix_tokens   ,      [self.tokenizer.eos_token_id]							)
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens   ,      [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

										A__       :     Dict								=			'''zh'''
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens   ,      [self.tokenizer.get_lang_id("""zh"""							)]							)
										self.assertListEqual(self.tokenizer.suffix_tokens   ,      [self.tokenizer.eos_token_id]							)
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens   ,      [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)





			@require_torch
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			self.tokenizer._build_translation_inputs("""A test"""   ,      return_tensors="""pt"""   ,      src_lang="""en"""   ,      tgt_lang="""ar"""							)

										self.assertEqual(
										    nested_simplify(lowercase_							)   ,      {
										        # en_XX, A, test, EOS
										        """input_ids""": [[128_022, 58, 4_183, 2]],
										        """attention_mask""": [[1, 1, 1, 1]],
										        # ar_AR
										        """forced_bos_token_id""": 128_006,
										    }   ,      )








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    : str				 =						{
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : List[Any]				 =						[
							    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Swinv2ForImageClassification''',
							    '''Swinv2ForMaskedImageModeling''',
							    '''Swinv2Model''',
							    '''Swinv2PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swinva import (
														    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinvaForImageClassification,
														    SwinvaForMaskedImageModeling,
														    SwinvaModel,
														    SwinvaPreTrainedModel,
														)


else:
							import sys

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




