
"""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


UpperCamelCase_					:						List[str]				= get_tests_dir('''fixtures/test_sentencepiece.model''')


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

UpperCamelCase_					:						Union[str, Any]				= 25_6047
UpperCamelCase_					:						List[str]				= 25_6145



@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				NllbTokenizer
			snake_case	      =				NllbTokenizerFast
			snake_case	      =				True
			snake_case	      =				True
			snake_case	      =				{}




			def      lowerCamelCase__     (				self						:					int	)      -> Any:







								"""simple docstring"""
								super().setUp()

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




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







								"""simple docstring"""
								A_				 =						NllbTokenizer(_snake_case       ,   keep_accents=_snake_case	)

								A_				 =						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]]       ,   )

								A_				 =						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",
								        ".",
								    ]       ,   )
								A_				 =						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]
								    ]       ,   )

								A_				 =						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						:					Any	)      -> Dict:







								"""simple docstring"""
								A_				 =						(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})'	):
																		A_				 =						self.rust_tokenizer_class.from_pretrained(_snake_case       ,   **_snake_case	)
																		A_				 =						self.tokenizer_class.from_pretrained(_snake_case       ,   **_snake_case	)

																		A_				 =						tempfile.mkdtemp()

																		A_				 =						tokenizer_r.save_pretrained(_snake_case	)
																		A_				 =						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	)	)
																		A_				 =						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
																		A_				 =						tokenizer_r.from_pretrained(_snake_case	)
																		A_				 =						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=True
																		A_				 =						tempfile.mkdtemp()

																		A_				 =						tokenizer_r.save_pretrained(_snake_case       ,   legacy_format=_snake_case	)
																		A_				 =						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
																		A_				 =						tokenizer_r.from_pretrained(_snake_case	)
																		A_				 =						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
																		A_				 =						tempfile.mkdtemp()

																		A_				 =						tokenizer_r.save_pretrained(_snake_case       ,   legacy_format=_snake_case	)
																		A_				 =						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
																		A_				 =						tokenizer_r.from_pretrained(_snake_case	)
																		A_				 =						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
			def      lowerCamelCase__     (				self						:					List[str]	)      -> Union[str, Any]:







								"""simple docstring"""
								if not self.test_seqaseq:
													return

								A_				 =						self.get_tokenizers()
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'	):
																		# Longer text that will definitely require truncation.
																		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.",
																		]
																		try:
																							A_				 =						tokenizer.prepare_seqaseq_batch(
																							    src_texts=_snake_case       ,   tgt_texts=_snake_case       ,   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
																		A_				 =						tokenizer.prepare_seqaseq_batch(
																		    _snake_case       ,   tgt_texts=_snake_case       ,   max_length=3       ,   return_tensors="pt"	)
																		self.assertEqual(batch.input_ids.shape[1]       ,   3	)
																		self.assertEqual(batch.labels.shape[1]       ,   3	)

																		A_				 =						tokenizer.prepare_seqaseq_batch(
																		    src_texts=_snake_case       ,   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"       ,   _snake_case	)




			@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."	)
			def      lowerCamelCase__     (				self						:					Any	)      -> List[Any]:







								"""simple docstring"""
								pass






			def      lowerCamelCase__     (				self						:					Dict	)      -> Tuple:







								"""simple docstring"""
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'	):
																		A_				 =						[AddedToken("<special>"       ,   lstrip=_snake_case	)]

																		A_				 =						self.rust_tokenizer_class.from_pretrained(
																		    _snake_case       ,   additional_special_tokens=_snake_case       ,   **_snake_case	)
																		A_				 =						tokenizer_r.encode("Hey this is a <special> token"	)

																		A_				 =						tokenizer_r.encode("<special>"       ,   add_special_tokens=_snake_case	)[0]

																		self.assertTrue(special_token_id in r_output	)

																		if self.test_slow_tokenizer:
																							A_				 =						self.rust_tokenizer_class.from_pretrained(
																							    _snake_case       ,   additional_special_tokens=_snake_case       ,   **_snake_case       ,   )
																							A_				 =						self.tokenizer_class.from_pretrained(
																							    _snake_case       ,   additional_special_tokens=_snake_case       ,   **_snake_case	)

																							A_				 =						tokenizer_p.encode("Hey this is a <special> token"	)

																							A_				 =						tokenizer_cr.encode("Hey this is a <special> token"	)

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





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




			"""simple docstring"""







			snake_case	      =				"facebook/nllb-200-distilled-600M"
			snake_case	      =				[
			    " 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.",
			]
			snake_case	      =				[
			    "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.",
			]
			snake_case	      =				[
			    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      lowerCamelCase__     (				cls						:					Optional[int]	)      -> int:







								"""simple docstring"""
								A_				 =						NllbTokenizer.from_pretrained(
								    cls.checkpoint_name       ,   src_lang="eng_Latn"       ,   tgt_lang="ron_Latn"	)
								A_				 =						1
								return cls




			def      lowerCamelCase__     (				self						:					Dict	)      -> Optional[int]:







								"""simple docstring"""
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]       ,   256_001	)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]       ,   256_002	)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]       ,   256_057	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
								self.assertListEqual(self.expected_src_tokens       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> int:







								"""simple docstring"""
								self.assertIn(_snake_case       ,   self.tokenizer.all_special_ids	)
								# fmt: off
								A_				 =						[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
								# fmt: on

								A_				 =						self.tokenizer.decode(_snake_case       ,   skip_special_tokens=_snake_case	)
								A_				 =						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						:					int	)      -> Any:







								"""simple docstring"""
								A_				 =						["this is gunna be a long sentence " * 20]
								assert isinstance(src_text[0]       ,   _snake_case	)
								A_				 =						10
								A_				 =						self.tokenizer(_snake_case       ,   max_length=_snake_case       ,   truncation=_snake_case	).input_ids[0]
								self.assertEqual(ids[-1]       ,   2	)
								self.assertEqual(ids[0]       ,   _snake_case	)
								self.assertEqual(len(_snake_case	)       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Dict	)      -> Tuple:







								"""simple docstring"""
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]	)       ,   [256_203, 3]	)




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						tempfile.mkdtemp()
								A_				 =						self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(_snake_case	)
								A_				 =						NllbTokenizer.from_pretrained(_snake_case	)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,   _snake_case	)




			@require_torch
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Any:







								"""simple docstring"""
								A_				 =						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"       ,   )
								A_				 =						shift_tokens_right(
								    batch["labels"]       ,   self.tokenizer.pad_token_id       ,   self.tokenizer.lang_code_to_id["ron_Latn"]	)

								self.assertIsInstance(_snake_case       ,   _snake_case	)

								self.assertEqual((2, 15)       ,   batch.input_ids.shape	)
								self.assertEqual((2, 15)       ,   batch.attention_mask.shape	)
								A_				 =						batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens       ,   _snake_case	)
								self.assertEqual(_snake_case       ,   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      lowerCamelCase__     (				self						:					Any	)      -> Any:







								"""simple docstring"""
								A_				 =						self.tokenizer(self.src_text       ,   padding=_snake_case       ,   truncation=_snake_case       ,   max_length=3       ,   return_tensors="pt"	)
								A_				 =						self.tokenizer(
								    text_target=self.tgt_text       ,   padding=_snake_case       ,   truncation=_snake_case       ,   max_length=10       ,   return_tensors="pt"	)
								A_				 =						targets["input_ids"]
								A_				 =						shift_tokens_right(
								    _snake_case       ,   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      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						self.tokenizer._build_translation_inputs(
								    "A test"       ,   return_tensors="pt"       ,   src_lang="eng_Latn"       ,   tgt_lang="fra_Latn"	)

								self.assertEqual(
								    nested_simplify(_snake_case	)       ,   {
								        # A, test, EOS, en_XX
								        "input_ids": [[256_047, 70, 7_356, 2]],
								        "attention_mask": [[1, 1, 1, 1]],
								        # ar_AR
								        "forced_bos_token_id": 256_057,
								    }       ,   )






			@require_torch
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						True
								A_				 =						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       ,   [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]	)

								A_				 =						False
								A_				 =						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       ,   [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]	)






"""simple docstring"""





from __future__ import annotations

import math

UpperCamelCase_					:						List[str]				= '''2020.9.26'''
UpperCamelCase_					:						List[Any]				= '''xcodz-dot, cclaus, dhruvmanila'''
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not all(isinstance(__a				,   (float, int)				) for val in locals().values()				):
										A_				 =						f'Input values must either be float or int: {list(locals().values()				)}'
										raise TypeError(__a				)
					A_				 =						((x * distance) / (z + distance)) * scale
					A_				 =						((y * distance) / (z + distance)) * scale
					return projected_x, projected_y
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not isinstance(__a				,   __a				):
										raise TypeError("Axis must be a str"				)
					A_				 =						locals()
					del input_variables["axis"]
					if not all(isinstance(__a				,   (float, int)				) for val in input_variables.values()				):
										A_				 =						(
										    "Input values except axis must either be float or int: "
										    f'{list(input_variables.values()				)}'
										)
										raise TypeError(__a				)
					A_				 =						(angle % 360) / 450 * 180 / math.pi
					if axis == "z":
										A_				 =						x * math.cos(__a				) - y * math.sin(__a				)
										A_				 =						y * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						z
					elif axis == "x":
										A_				 =						y * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + y * math.sin(__a				)
										A_				 =						x
					elif axis == "y":
										A_				 =						x * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						y
					else:
										raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'"				)

					return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
		print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")




