



"""simple docstring"""






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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			:			Union[str, Any]										=						get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

_lowercase			:			str										=						2_5_0_0_0_4
_lowercase			:			Optional[Any]										=						2_5_0_0_2_0

@require_sentencepiece
@require_tokenizers
class 	__SCREAMING_SNAKE_CASE     (				A_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							MBartTokenizer
					_a             =							MBartTokenizerFast
					_a             =							True
					_a             =							True



					def 		snake_case	(	self							:  int					)-> str:
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase__		:		List[Any]		     =MBartTokenizer(_lowerCamelCase,  keep_accents=_lowerCamelCase					)
								tokenizer.save_pretrained(self.tmpdirname					)



					def 		snake_case	(	self							:  Any					)-> Any:
								lowerCamelCase__		:		List[Any]		     =MBartTokenizer(_lowerCamelCase,  keep_accents=_lowerCamelCase					)

								lowerCamelCase__		:		Any		     =tokenizer.tokenize('''This is a test'''					)
								self.assertListEqual(_lowerCamelCase,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']					)

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

								lowerCamelCase__		:		Optional[int]		     =tokenizer.tokenize('''I was born in 92000, and this is falsé.'''					)
								self.assertListEqual(
								    _lowerCamelCase,  [
								        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__		:		List[Any]		     =tokenizer.convert_tokens_to_ids(_lowerCamelCase					)
								self.assertListEqual(
								    _lowerCamelCase,  [
								        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]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ],  )

								lowerCamelCase__		:		Dict		     =tokenizer.convert_ids_to_tokens(_lowerCamelCase					)
								self.assertListEqual(
								    _lowerCamelCase,  [
								        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 		snake_case	(	self							:  Any					)-> Any:
								if not self.test_slow_tokenizer:
											# as we don't have a slow version, we can't compare the outputs between slow and fast versions
											return

								lowerCamelCase__		:		List[Any]		     =(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
														lowerCamelCase__		:		Dict		     =self.rust_tokenizer_class.from_pretrained(_lowerCamelCase,  **_lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =self.tokenizer_class.from_pretrained(_lowerCamelCase,  **_lowerCamelCase					)

														lowerCamelCase__		:		Any		     =tempfile.mkdtemp()

														lowerCamelCase__		:		Union[str, Any]		     =tokenizer_r.save_pretrained(_lowerCamelCase					)
														lowerCamelCase__		:		Optional[int]		     =tokenizer_p.save_pretrained(_lowerCamelCase					)

														# 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__		:		Any		     =tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f					)
														self.assertSequenceEqual(_lowerCamelCase,  _lowerCamelCase					)

														# Checks everything loads correctly in the same way
														lowerCamelCase__		:		Any		     =tokenizer_r.from_pretrained(_lowerCamelCase					)
														lowerCamelCase__		:		int		     =tokenizer_p.from_pretrained(_lowerCamelCase					)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(_lowerCamelCase,  _lowerCamelCase					)					)
																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

														shutil.rmtree(_lowerCamelCase					)

														# Save tokenizer rust, legacy_format=True
														lowerCamelCase__		:		Any		     =tempfile.mkdtemp()

														lowerCamelCase__		:		List[Any]		     =tokenizer_r.save_pretrained(_lowerCamelCase,  legacy_format=_lowerCamelCase					)
														lowerCamelCase__		:		int		     =tokenizer_p.save_pretrained(_lowerCamelCase					)

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

														# Checks everything loads correctly in the same way
														lowerCamelCase__		:		Any		     =tokenizer_r.from_pretrained(_lowerCamelCase					)
														lowerCamelCase__		:		str		     =tokenizer_p.from_pretrained(_lowerCamelCase					)

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

														shutil.rmtree(_lowerCamelCase					)

														# Save tokenizer rust, legacy_format=False
														lowerCamelCase__		:		List[str]		     =tempfile.mkdtemp()

														lowerCamelCase__		:		Any		     =tokenizer_r.save_pretrained(_lowerCamelCase,  legacy_format=_lowerCamelCase					)
														lowerCamelCase__		:		Union[str, Any]		     =tokenizer_p.save_pretrained(_lowerCamelCase					)

														# 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__		:		Any		     =tokenizer_r.from_pretrained(_lowerCamelCase					)
														lowerCamelCase__		:		Tuple		     =tokenizer_p.from_pretrained(_lowerCamelCase					)

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

														shutil.rmtree(_lowerCamelCase					)





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


					'''simple docstring'''





					_a             =							'facebook/mbart-large-en-ro'
					_a             =							[
					    ' 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.',
					]
					_a             =							[
					    '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.',
					]
					_a             =							[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]



					@classmethod
					def 		snake_case	(	cls							:  Tuple					)-> str:
								lowerCamelCase__		:		Dict		     =MBartTokenizer.from_pretrained(
								    cls.checkpoint_name,  src_lang='''en_XX''',  tgt_lang='''ro_RO'''					)
								lowerCamelCase__		:		int		     =1
								return cls



					def 		snake_case	(	self							:  str					)-> Dict:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''],  25_0001					)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''],  25_0004					)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''],  25_0020					)



					def 		snake_case	(	self							:  int					)-> Tuple:
								lowerCamelCase__		:		Tuple		     =self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
								self.assertListEqual(self.expected_src_tokens,  _lowerCamelCase					)



					def 		snake_case	(	self							:  List[str]					)-> Optional[int]:
								self.assertIn(_lowerCamelCase,  self.tokenizer.all_special_ids					)
								lowerCamelCase__		:		List[str]		     =[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
								lowerCamelCase__		:		int		     =self.tokenizer.decode(_lowerCamelCase,  skip_special_tokens=_lowerCamelCase					)
								lowerCamelCase__		:		int		     =self.tokenizer.decode(generated_ids[1:],  skip_special_tokens=_lowerCamelCase					)
								self.assertEqual(_lowerCamelCase,  _lowerCamelCase					)
								self.assertNotIn(self.tokenizer.eos_token,  _lowerCamelCase					)



					def 		snake_case	(	self							:  Dict					)-> List[str]:
								lowerCamelCase__		:		int		     =['''this is gunna be a long sentence ''' * 20]
								assert isinstance(src_text[0],  _lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =10
								lowerCamelCase__		:		Optional[int]		     =self.tokenizer(_lowerCamelCase,  max_length=_lowerCamelCase,  truncation=_lowerCamelCase					).input_ids[0]
								self.assertEqual(ids[-2],  2					)
								self.assertEqual(ids[-1],  _lowerCamelCase					)
								self.assertEqual(len(_lowerCamelCase					),  _lowerCamelCase					)



					def 		snake_case	(	self							:  Union[str, Any]					)-> Optional[Any]:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']					),  [25_0026, 25_0001]					)



					def 		snake_case	(	self							:  Optional[int]					)-> List[str]:
								lowerCamelCase__		:		Optional[Any]		     =tempfile.mkdtemp()
								lowerCamelCase__		:		List[Any]		     =self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(_lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =MBartTokenizer.from_pretrained(_lowerCamelCase					)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids,  _lowerCamelCase					)



					@require_torch
					def 		snake_case	(	self							:  Union[str, Any]					)-> Optional[int]:
								lowerCamelCase__		:		Any		     =self.tokenizer(self.src_text,  text_target=self.tgt_text,  padding=_lowerCamelCase,  return_tensors='''pt'''					)
								lowerCamelCase__		:		Optional[int]		     =shift_tokens_right(batch['''labels'''],  self.tokenizer.pad_token_id					)

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
								assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
								assert batch.decoder_input_ids[1][-1] == 2
								assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



					@require_torch
					def 		snake_case	(	self							:  Optional[int]					)-> str:
								lowerCamelCase__		:		str		     =self.tokenizer(
								    self.src_text,  text_target=self.tgt_text,  padding=_lowerCamelCase,  truncation=_lowerCamelCase,  max_length=len(self.expected_src_tokens					),  return_tensors='''pt''',  )

								lowerCamelCase__		:		List[Any]		     =shift_tokens_right(batch['''labels'''],  self.tokenizer.pad_token_id					)

								self.assertIsInstance(_lowerCamelCase,  _lowerCamelCase					)

								self.assertEqual((2, 14),  batch.input_ids.shape					)
								self.assertEqual((2, 14),  batch.attention_mask.shape					)
								lowerCamelCase__		:		str		     =batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens,  _lowerCamelCase					)
								self.assertEqual(2,  batch.decoder_input_ids[0, -1]					)  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens,  []					)
								self.assertEqual(self.tokenizer.suffix_tokens,  [self.tokenizer.eos_token_id, EN_CODE]					)



					def 		snake_case	(	self							:  Tuple					)-> Any:
								lowerCamelCase__		:		str		     =self.tokenizer(self.src_text,  padding=_lowerCamelCase,  truncation=_lowerCamelCase,  max_length=3,  return_tensors='''pt'''					)
								lowerCamelCase__		:		Union[str, Any]		     =self.tokenizer(
								    text_target=self.tgt_text,  padding=_lowerCamelCase,  truncation=_lowerCamelCase,  max_length=10,  return_tensors='''pt'''					)
								lowerCamelCase__		:		Tuple		     =targets['''input_ids''']
								lowerCamelCase__		:		Optional[int]		     =shift_tokens_right(_lowerCamelCase,  self.tokenizer.pad_token_id					)

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




					@require_torch
					def 		snake_case	(	self							:  List[str]					)-> List[str]:
								lowerCamelCase__		:		Any		     =self.tokenizer._build_translation_inputs(
								    '''A test''',  return_tensors='''pt''',  src_lang='''en_XX''',  tgt_lang='''ar_AR'''					)

								self.assertEqual(
								    nested_simplify(_lowerCamelCase					),  {
								        # A, test, EOS, en_XX
								        '''input_ids''': [[62, 3034, 2, 25_0004]],
								        '''attention_mask''': [[1, 1, 1, 1]],
								        # ar_AR
								        '''forced_bos_token_id''': 25_0001,
								    },  )









"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp






def 					snake_case__       (		__lowerCamelCase    :   jnp.ndarray							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1.0e4							,		__lowerCamelCase    :   bool = False							,		__lowerCamelCase    :   float = 1.0							,		):
			"""simple docstring"""
			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
			lowerCamelCase__		:		Any		     =float(embedding_dim // 2     )
			lowerCamelCase__		:		List[str]		     =math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			lowerCamelCase__		:		int		     =min_timescale * jnp.exp(jnp.arange(__lowerCamelCase							,		dtype=jnp.floataa     ) * -log_timescale_increment     )
			lowerCamelCase__		:		Tuple		     =jnp.expand_dims(__lowerCamelCase							,		1     ) * jnp.expand_dims(__lowerCamelCase							,		0     )

			# scale embeddings
			lowerCamelCase__		:		List[str]		     =scale * emb

			if flip_sin_to_cos:
						lowerCamelCase__		:		int		     =jnp.concatenate([jnp.cos(__lowerCamelCase     ), jnp.sin(__lowerCamelCase     )]							,		axis=1     )
			else:
						lowerCamelCase__		:		List[str]		     =jnp.concatenate([jnp.sin(__lowerCamelCase     ), jnp.cos(__lowerCamelCase     )]							,		axis=1     )
			lowerCamelCase__		:		str		     =jnp.reshape(__lowerCamelCase							,		[jnp.shape(__lowerCamelCase     )[0], embedding_dim]     )
			return signal

class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							jnp.floataa



					@nn.compact
					def __call__(	self							:  Optional[Any],  lowerCamelCase							:  int					)-> Any:
								lowerCamelCase__		:		Optional[Any]		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_1'''					)(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =nn.silu(lowerCamelCase					)
								lowerCamelCase__		:		Any		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_2'''					)(lowerCamelCase					)
								return temb





class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							False
					_a             =							1



					@nn.compact
					def __call__(	self							:  Any,  lowerCamelCase							:  int					)-> int:
								return get_sinusoidal_embeddings(
								    lowerCamelCase,  embedding_dim=self.dim,  flip_sin_to_cos=self.flip_sin_to_cos,  freq_shift=self.freq_shift					)




