'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_     		= get_tests_dir('''fixtures/test_sentencepiece.model''')


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

lowercase_     		= 25_6047
lowercase_     		= 25_6145
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      int       =							NllbTokenizer
		lowercase   :      Union[str, Any]       =							NllbTokenizerFast
		lowercase   :      Tuple       =							True
		lowercase   :      Dict       =							True
		lowercase   :      Tuple       =							{}






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								_lowerCAmelCase										=NllbTokenizer(__A		,						keep_accents=__A				)
								tokenizer.save_pretrained(self.tmpdirname				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=NllbTokenizer(__A		,						keep_accents=__A				)

								_lowerCAmelCase										=tokenizer.tokenize('This is a test'				)
								self.assertListEqual(__A		,						['▁This', '▁is', '▁a', '▁t', 'est']				)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(__A				)		,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,						)

								_lowerCAmelCase										=tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
								self.assertListEqual(
								    __A		,						[
								        SPIECE_UNDERLINE + 'I',
								        SPIECE_UNDERLINE + 'was',
								        SPIECE_UNDERLINE + 'b',
								        'or',
								        'n',
								        SPIECE_UNDERLINE + 'in',
								        SPIECE_UNDERLINE + '',
								        '9',
								        '2',
								        '0',
								        '0',
								        '0',
								        ',',
								        SPIECE_UNDERLINE + 'and',
								        SPIECE_UNDERLINE + 'this',
								        SPIECE_UNDERLINE + 'is',
								        SPIECE_UNDERLINE + 'f',
								        'al',
								        's',
								        'é',
								        '.',
								    ]		,						)
								_lowerCAmelCase										=tokenizer.convert_tokens_to_ids(__A				)
								self.assertListEqual(
								    __A		,						[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ]		,						)

								_lowerCAmelCase										=tokenizer.convert_ids_to_tokens(__A				)
								self.assertListEqual(
								    __A		,						[
								        SPIECE_UNDERLINE + 'I',
								        SPIECE_UNDERLINE + 'was',
								        SPIECE_UNDERLINE + 'b',
								        'or',
								        'n',
								        SPIECE_UNDERLINE + 'in',
								        SPIECE_UNDERLINE + '',
								        '<unk>',
								        '2',
								        '0',
								        '0',
								        '0',
								        ',',
								        SPIECE_UNDERLINE + 'and',
								        SPIECE_UNDERLINE + 'this',
								        SPIECE_UNDERLINE + 'is',
								        SPIECE_UNDERLINE + 'f',
								        'al',
								        's',
								        '<unk>',
								        '.',
								    ]		,						)






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
																				_lowerCAmelCase										=self.rust_tokenizer_class.from_pretrained(__A		,						**__A				)
																				_lowerCAmelCase										=self.tokenizer_class.from_pretrained(__A		,						**__A				)

																				_lowerCAmelCase										=tempfile.mkdtemp()

																				_lowerCAmelCase										=tokenizer_r.save_pretrained(__A				)
																				_lowerCAmelCase										=tokenizer_p.save_pretrained(__A				)

																				# Checks it save with the same files + the tokenizer.json file for the fast one
																				self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)
																				_lowerCAmelCase										=tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f				)
																				self.assertSequenceEqual(__A		,						__A				)

																				# Checks everything loads correctly in the same way
																				_lowerCAmelCase										=tokenizer_r.from_pretrained(__A				)
																				_lowerCAmelCase										=tokenizer_p.from_pretrained(__A				)

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(__A		,						__A				)				)

																				shutil.rmtree(__A				)

																				# Save tokenizer rust, legacy_format=True
																				_lowerCAmelCase										=tempfile.mkdtemp()

																				_lowerCAmelCase										=tokenizer_r.save_pretrained(__A		,						legacy_format=__A				)
																				_lowerCAmelCase										=tokenizer_p.save_pretrained(__A				)

																				# Checks it save with the same files
																				self.assertSequenceEqual(__A		,						__A				)

																				# Checks everything loads correctly in the same way
																				_lowerCAmelCase										=tokenizer_r.from_pretrained(__A				)
																				_lowerCAmelCase										=tokenizer_p.from_pretrained(__A				)

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(__A		,						__A				)				)

																				shutil.rmtree(__A				)

																				# Save tokenizer rust, legacy_format=False
																				_lowerCAmelCase										=tempfile.mkdtemp()

																				_lowerCAmelCase										=tokenizer_r.save_pretrained(__A		,						legacy_format=__A				)
																				_lowerCAmelCase										=tokenizer_p.save_pretrained(__A				)

																				# Checks it saved the tokenizer.json file
																				self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)

																				# Checks everything loads correctly in the same way
																				_lowerCAmelCase										=tokenizer_r.from_pretrained(__A				)
																				_lowerCAmelCase										=tokenizer_p.from_pretrained(__A				)

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(__A		,						__A				)				)

																				shutil.rmtree(__A				)






		@require_torch
		def 		UpperCamelCase__       (   self				) ->     str:
								if not self.test_seqaseq:
														return

								_lowerCAmelCase										=self.get_tokenizers()
								for tokenizer in tokenizers:
														with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
																				# Longer text that will definitely require truncation.
																				_lowerCAmelCase										=[
																				    ' UN Chief Says There Is No Military Solution in Syria',
																				    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
																				    ' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
																				    ' will only worsen the violence and misery for millions of people.',
																				]
																				_lowerCAmelCase										=[
																				    'Şeful ONU declară că nu există o soluţie militară în Siria',
																				    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
																				    ' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
																				    ' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
																				]
																				try:
																										_lowerCAmelCase										=tokenizer.prepare_seqaseq_batch(
																										    src_texts=__A		,						tgt_texts=__A		,						max_length=3		,						max_target_length=10		,						return_tensors='pt'		,						src_lang='eng_Latn'		,						tgt_lang='ron_Latn'		,						)
																				except NotImplementedError:
																										return
																				self.assertEqual(batch.input_ids.shape[1]		,						3				)
																				self.assertEqual(batch.labels.shape[1]		,						10				)
																				# max_target_length will default to max_length if not specified
																				_lowerCAmelCase										=tokenizer.prepare_seqaseq_batch(
																				    __A		,						tgt_texts=__A		,						max_length=3		,						return_tensors='pt'				)
																				self.assertEqual(batch.input_ids.shape[1]		,						3				)
																				self.assertEqual(batch.labels.shape[1]		,						3				)

																				_lowerCAmelCase										=tokenizer.prepare_seqaseq_batch(
																				    src_texts=__A		,						max_length=3		,						max_target_length=10		,						return_tensors='pt'				)
																				self.assertEqual(batch_encoder_only.input_ids.shape[1]		,						3				)
																				self.assertEqual(batch_encoder_only.attention_mask.shape[1]		,						3				)
																				self.assertNotIn('decoder_input_ids'		,						__A				)






		@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'				)
		def 		UpperCamelCase__       (   self				) ->     Any:
								pass






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
																				_lowerCAmelCase										=[AddedToken('<special>'		,						lstrip=__A				)]

																				_lowerCAmelCase										=self.rust_tokenizer_class.from_pretrained(
																				    __A		,						additional_special_tokens=__A		,						**__A				)
																				_lowerCAmelCase										=tokenizer_r.encode('Hey this is a <special> token'				)

																				_lowerCAmelCase										=tokenizer_r.encode('<special>'		,						add_special_tokens=__A				)[0]

																				self.assertTrue(special_token_id in r_output				)

																				if self.test_slow_tokenizer:
																										_lowerCAmelCase										=self.rust_tokenizer_class.from_pretrained(
																										    __A		,						additional_special_tokens=__A		,						**__A		,						)
																										_lowerCAmelCase										=self.tokenizer_class.from_pretrained(
																										    __A		,						additional_special_tokens=__A		,						**__A				)

																										_lowerCAmelCase										=tokenizer_p.encode('Hey this is a <special> token'				)

																										_lowerCAmelCase										=tokenizer_cr.encode('Hey this is a <special> token'				)

																										self.assertEqual(__A		,						__A				)
																										self.assertEqual(__A		,						__A				)
																										self.assertTrue(special_token_id in p_output				)
																										self.assertTrue(special_token_id in cr_output				)

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







		"""simple docstring"""
		lowercase   :      Optional[Any]       =							'facebook/nllb-200-distilled-600M'
		lowercase   :      List[Any]       =							[
		    ' UN Chief Says There Is No Military Solution in Syria',
		    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
		]
		lowercase   :      Tuple       =							[
		    'Şeful ONU declară că nu există o soluţie militară în Siria',
		    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
		    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
		    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
		]
		lowercase   :      int       =							[
		    25_60_47,
		    1_62_97,
		    13_44_08,
		    81_65,
		    24_80_66,
		    1_47_34,
		    9_50,
		    11_35,
		    10_57_21,
		    35_73,
		    83,
		    2_73_52,
		    1_08,
		    4_94_86,
		    2,
		]






		@classmethod
		def 		UpperCamelCase__       (   cls				) ->     Optional[Any]:
								_lowerCAmelCase										=NllbTokenizer.from_pretrained(
								    cls.checkpoint_name		,						src_lang='eng_Latn'		,						tgt_lang='ron_Latn'				)
								_lowerCAmelCase										=1
								return cls






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']		,						25_6001				)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']		,						25_6002				)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']		,						25_6057				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
								self.assertListEqual(self.expected_src_tokens		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								self.assertIn(__A		,						self.tokenizer.all_special_ids				)
								# fmt: off
								_lowerCAmelCase										=[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
								# fmt: on

								_lowerCAmelCase										=self.tokenizer.decode(__A		,						skip_special_tokens=__A				)
								_lowerCAmelCase										=self.tokenizer.decode(generated_ids[1:]		,						skip_special_tokens=__A				)
								self.assertEqual(__A		,						__A				)
								self.assertNotIn(self.tokenizer.eos_token		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=['this is gunna be a long sentence ' * 20]
								assert isinstance(src_text[0]		,						__A				)
								_lowerCAmelCase										=10
								_lowerCAmelCase										=self.tokenizer(__A		,						max_length=__A		,						truncation=__A				).input_ids[0]
								self.assertEqual(ids[-1]		,						2				)
								self.assertEqual(ids[0]		,						__A				)
								self.assertEqual(len(__A				)		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']				)		,						[25_6203, 3]				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=tempfile.mkdtemp()
								_lowerCAmelCase										=self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(__A				)
								_lowerCAmelCase										=NllbTokenizer.from_pretrained(__A				)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,						__A				)






		@require_torch
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=self.tokenizer(
								    self.src_text		,						text_target=self.tgt_text		,						padding=__A		,						truncation=__A		,						max_length=len(self.expected_src_tokens				)		,						return_tensors='pt'		,						)
								_lowerCAmelCase										=shift_tokens_right(
								    batch['labels']		,						self.tokenizer.pad_token_id		,						self.tokenizer.lang_code_to_id['ron_Latn']				)

								self.assertIsInstance(__A		,						__A				)

								self.assertEqual((2, 15)		,						batch.input_ids.shape				)
								self.assertEqual((2, 15)		,						batch.attention_mask.shape				)
								_lowerCAmelCase										=batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens		,						__A				)
								self.assertEqual(__A		,						batch.decoder_input_ids[0, 0]				)  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens		,						[EN_CODE]				)
								self.assertEqual(self.tokenizer.suffix_tokens		,						[self.tokenizer.eos_token_id]				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										=self.tokenizer(self.src_text		,						padding=__A		,						truncation=__A		,						max_length=3		,						return_tensors='pt'				)
								_lowerCAmelCase										=self.tokenizer(
								    text_target=self.tgt_text		,						padding=__A		,						truncation=__A		,						max_length=10		,						return_tensors='pt'				)
								_lowerCAmelCase										=targets['input_ids']
								_lowerCAmelCase										=shift_tokens_right(
								    __A		,						self.tokenizer.pad_token_id		,						decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]		,						)

								self.assertEqual(batch.input_ids.shape[1]		,						3				)
								self.assertEqual(batch.decoder_input_ids.shape[1]		,						10				)






		@require_torch
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=self.tokenizer._build_translation_inputs(
								    'A test'		,						return_tensors='pt'		,						src_lang='eng_Latn'		,						tgt_lang='fra_Latn'				)

								self.assertEqual(
								    nested_simplify(__A				)		,						{
								        # A, test, EOS, en_XX
								        'input_ids': [[25_6047, 70, 7356, 2]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # ar_AR
								        'forced_bos_token_id': 25_6057,
								    }		,						)






		@require_torch
		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								_lowerCAmelCase										=True
								_lowerCAmelCase										=self.tokenizer(
								    'UN Chief says there is no military solution in Syria'		,						src_lang='eng_Latn'		,						tgt_lang='fra_Latn'				)
								self.assertEqual(
								    inputs.input_ids		,						[1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047]				)

								_lowerCAmelCase										=False
								_lowerCAmelCase										=self.tokenizer(
								    'UN Chief says there is no military solution in Syria'		,						src_lang='eng_Latn'		,						tgt_lang='fra_Latn'				)
								self.assertEqual(
								    inputs.input_ids		,						[25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2]				)






'''simple docstring'''

lowercase_     		= '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowercase_     		= [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_     		= {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





