



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


_lowerCamelCase    :		Union[str, Any]		      =    get_tests_dir("""fixtures/test_sentencepiece.model""")


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

_lowerCamelCase    :		Optional[Any]		      =    250_004
_lowerCamelCase    :		List[str]		      =    250_020


@require_sentencepiece
@require_tokenizers
class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[int]		=		MBartTokenizer
						UpperCAmelCase			:		Union[str, Any]		=		MBartTokenizerFast
						UpperCAmelCase			:		Dict		=		True
						UpperCAmelCase			:		Optional[Any]		=		True







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											super().setUp()

											# We have a SentencePiece fixture for testing
											SCREAMING_SNAKE_CASE							:     List[str]						=				MBartTokenizer(snake_case       ,	keep_accents=snake_case	)
											tokenizer.save_pretrained(self.tmpdirname	)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				MBartTokenizer(snake_case       ,	keep_accents=snake_case	)

											SCREAMING_SNAKE_CASE							:     str						=				tokenizer.tokenize('This is a test'	)
											self.assertListEqual(snake_case       ,	['▁This', '▁is', '▁a', '▁t', 'est']	)

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

											SCREAMING_SNAKE_CASE							:     Tuple						=				tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
											self.assertListEqual(
											    snake_case       ,	[
											        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',
											        '.',
											    ]       ,	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				tokenizer.convert_tokens_to_ids(snake_case	)
											self.assertListEqual(
											    snake_case       ,	[
											        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 ^
											    ]       ,	)

											SCREAMING_SNAKE_CASE							:     Dict						=				tokenizer.convert_ids_to_tokens(snake_case	)
											self.assertListEqual(
											    snake_case       ,	[
											        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       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											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

											SCREAMING_SNAKE_CASE							:     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})'''	):
																					SCREAMING_SNAKE_CASE							:     Tuple						=				self.rust_tokenizer_class.from_pretrained(snake_case       ,	**snake_case	)
																					SCREAMING_SNAKE_CASE							:     Any						=				self.tokenizer_class.from_pretrained(snake_case       ,	**snake_case	)

																					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				tempfile.mkdtemp()

																					SCREAMING_SNAKE_CASE							:     Dict						=				tokenizer_r.save_pretrained(snake_case	)
																					SCREAMING_SNAKE_CASE							:     Dict						=				tokenizer_p.save_pretrained(snake_case	)

																					# 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	)	)
																					SCREAMING_SNAKE_CASE							:     Optional[int]						=				tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f	)
																					self.assertSequenceEqual(snake_case       ,	snake_case	)

																					# Checks everything loads correctly in the same way
																					SCREAMING_SNAKE_CASE							:     Any						=				tokenizer_r.from_pretrained(snake_case	)
																					SCREAMING_SNAKE_CASE							:     List[Any]						=				tokenizer_p.from_pretrained(snake_case	)

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

																					shutil.rmtree(snake_case	)

																					# Save tokenizer rust, legacy_format=True
																					SCREAMING_SNAKE_CASE							:     Dict						=				tempfile.mkdtemp()

																					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				tokenizer_r.save_pretrained(snake_case       ,	legacy_format=snake_case	)
																					SCREAMING_SNAKE_CASE							:     str						=				tokenizer_p.save_pretrained(snake_case	)

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

																					# Checks everything loads correctly in the same way
																					SCREAMING_SNAKE_CASE							:     Any						=				tokenizer_r.from_pretrained(snake_case	)
																					SCREAMING_SNAKE_CASE							:     List[Any]						=				tokenizer_p.from_pretrained(snake_case	)

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

																					shutil.rmtree(snake_case	)

																					# Save tokenizer rust, legacy_format=False
																					SCREAMING_SNAKE_CASE							:     Any						=				tempfile.mkdtemp()

																					SCREAMING_SNAKE_CASE							:     Optional[int]						=				tokenizer_r.save_pretrained(snake_case       ,	legacy_format=snake_case	)
																					SCREAMING_SNAKE_CASE							:     Tuple						=				tokenizer_p.save_pretrained(snake_case	)

																					# 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
																					SCREAMING_SNAKE_CASE							:     Dict						=				tokenizer_r.from_pretrained(snake_case	)
																					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				tokenizer_p.from_pretrained(snake_case	)

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

																					shutil.rmtree(snake_case	)


@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[Any]		=		'facebook/mbart-large-en-ro'
						UpperCAmelCase			:		Tuple		=		[
						    ' 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.',
						]
						UpperCAmelCase			:		Union[str, Any]		=		[
						    '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.',
						]
						UpperCAmelCase			:		Optional[Any]		=		[8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]







						@classmethod
						def       lowerCamelCase_				(  cls    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     MBartTokenizer						=				MBartTokenizer.from_pretrained(
											    cls.checkpoint_name       ,	src_lang='en_XX'       ,	tgt_lang='ro_RO'	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				1
											return cls







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']       ,	250001	)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']       ,	250004	)
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']       ,	250020	)







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
											self.assertListEqual(self.expected_src_tokens       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											self.assertIn(snake_case       ,	self.tokenizer.all_special_ids	)
											SCREAMING_SNAKE_CASE							:     Dict						=				[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
											SCREAMING_SNAKE_CASE							:     str						=				self.tokenizer.decode(snake_case       ,	skip_special_tokens=snake_case	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.tokenizer.decode(generated_ids[1:]       ,	skip_special_tokens=snake_case	)
											self.assertEqual(snake_case       ,	snake_case	)
											self.assertNotIn(self.tokenizer.eos_token       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']	)       ,	[250026, 250001]	)







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				tempfile.mkdtemp()
											SCREAMING_SNAKE_CASE							:     Dict						=				self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(snake_case	)
											SCREAMING_SNAKE_CASE							:     Dict						=				MBartTokenizer.from_pretrained(snake_case	)
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,	snake_case	)







						@require_torch
						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				self.tokenizer(self.src_text       ,	text_target=self.tgt_text       ,	padding=snake_case       ,	return_tensors='pt'	)
											SCREAMING_SNAKE_CASE							:     Any						=				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       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				self.tokenizer(
											    self.src_text       ,	text_target=self.tgt_text       ,	padding=snake_case       ,	truncation=snake_case       ,	max_length=len(self.expected_src_tokens	)       ,	return_tensors='pt'       ,	)

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

											self.assertIsInstance(snake_case       ,	snake_case	)

											self.assertEqual((2, 14)       ,	batch.input_ids.shape	)
											self.assertEqual((2, 14)       ,	batch.attention_mask.shape	)
											SCREAMING_SNAKE_CASE							:     Dict						=				batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens       ,	snake_case	)
											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       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				self.tokenizer(self.src_text       ,	padding=snake_case       ,	truncation=snake_case       ,	max_length=3       ,	return_tensors='pt'	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.tokenizer(
											    text_target=self.tgt_text       ,	padding=snake_case       ,	truncation=snake_case       ,	max_length=10       ,	return_tensors='pt'	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				targets['input_ids']
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				shift_tokens_right(snake_case       ,	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       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.tokenizer._build_translation_inputs(
											    'A test'       ,	return_tensors='pt'       ,	src_lang='en_XX'       ,	tgt_lang='ar_AR'	)

											self.assertEqual(
											    nested_simplify(snake_case	)       ,	{
											        # A, test, EOS, en_XX
											        'input_ids': [[62, 3034, 2, 250004]],
											        'attention_mask': [[1, 1, 1, 1]],
											        # ar_AR
											        'forced_bos_token_id': 250001,
											    }       ,	)




import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def        __a			(   __lowerCAmelCase   )     ->     tuple:
					return (data["data"], data["target"])

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     np.ndarray:
					SCREAMING_SNAKE_CASE							:     int						=				XGBRegressor(verbosity=0      ,	random_state=42   )
					xgb.fit(__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Predict target for test data
					SCREAMING_SNAKE_CASE							:     List[Any]						=				xgb.predict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				predictions.reshape(len(__lowerCAmelCase   )      ,	1   )
					return predictions

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Dict						=				fetch_california_housing()
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				data_handling(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				train_test_split(
					    __lowerCAmelCase      ,	__lowerCAmelCase      ,	test_size=0.25      ,	random_state=1   )
					SCREAMING_SNAKE_CASE							:     int						=				xgboost(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Error printing
					print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )
					print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()