


"""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


A  :     Dict    					=    get_tests_dir("fixtures/test_sentencepiece.model")


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

A  :     Any    					=    2_5_6_0_4_7
A  :     Dict    					=    2_5_6_1_4_5
@require_sentencepiece
@require_tokenizers
class 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[Any]          =NllbTokenizer
							__UpperCAmelCase		:					Tuple          =NllbTokenizerFast
							__UpperCAmelCase		:					List[str]          =True
							__UpperCAmelCase		:					Optional[int]          =True
							__UpperCAmelCase		:					Tuple          ={}

							def     snake_case       (  self			):
										super().setUp()

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

							def     snake_case       (  self			):
										__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 [2_85, 46, 10, 1_70, 3_82]]					,     )

										__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, 6_02, 3_47, 3_47, 3_47, 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     snake_case       (  self			):
										__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     snake_case       (  self			):
										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     snake_case       (  self			):
										pass



							def     snake_case       (  self			):
										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 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          ="""facebook/nllb-200-distilled-600M"""
							__UpperCAmelCase		:					Optional[int]          =[
							    """ 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		:					str          =[
							    """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		:					Tuple          =[
							    2_5_6_0_4_7,
							    1_6_2_9_7,
							    1_3_4_4_0_8,
							    8_1_6_5,
							    2_4_8_0_6_6,
							    1_4_7_3_4,
							    9_5_0,
							    1_1_3_5,
							    1_0_5_7_2_1,
							    3_5_7_3,
							    8_3,
							    2_7_3_5_2,
							    1_0_8,
							    4_9_4_8_6,
							    2,
							]

							@classmethod
							def     snake_case       (  cls			):
										__lowerCAmelCase       					=    NllbTokenizer.from_pretrained(
										    cls.checkpoint_name					,     src_lang="eng_Latn"					,     tgt_lang="ron_Latn"			)
										__lowerCAmelCase       					=    1
										return cls

							def     snake_case       (  self			):
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]					,     25_60_01			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]					,     25_60_02			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]					,     25_60_57			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
										self.assertListEqual(self.expected_src_tokens					,     __a			)

							def     snake_case       (  self			):
										self.assertIn(__a					,     self.tokenizer.all_special_ids			)
										# fmt: off
										__lowerCAmelCase       					=    [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
										# 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     snake_case       (  self			):
										__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     snake_case       (  self			):
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]			)					,     [25_62_03, 3]			)

							def     snake_case       (  self			):
										__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     snake_case       (  self			):
										__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     snake_case       (  self			):
										__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     snake_case       (  self			):
										__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_60_47, 70, 73_56, 2]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # ar_AR
										        "forced_bos_token_id": 25_60_57,
										    }					,     )



							@require_torch
							def     snake_case       (  self			):
										__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_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]			)

										__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_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]			)








"""simple docstring"""


from itertools import product


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    sides_number
			__lowerCAmelCase       					=    max_face_number * dice_number
			__lowerCAmelCase       					=    [0] * (max_total + 1)

			__lowerCAmelCase       					=    1
			__lowerCAmelCase       					=    range(_UpperCamelCase					,				max_face_number + 1   )
			for dice_numbers in product(_UpperCamelCase					,				repeat=_UpperCamelCase   ):
						__lowerCAmelCase       					=    sum(_UpperCamelCase   )
						totals_frequencies[total] += 1

			return totals_frequencies


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    total_frequency_distribution(
			    sides_number=4					,				dice_number=9   )
			__lowerCAmelCase       					=    total_frequency_distribution(
			    sides_number=6					,				dice_number=6   )

			__lowerCAmelCase       					=    0
			__lowerCAmelCase       					=    9
			__lowerCAmelCase       					=    4 * 9
			__lowerCAmelCase       					=    6
			for peter_total in range(_UpperCamelCase					,				max_peter_total + 1   ):
						peter_wins_count += peter_totals_frequencies[peter_total] * sum(
						    colin_totals_frequencies[min_colin_total:peter_total]   )

			__lowerCAmelCase       					=    (4**9) * (6**6)
			__lowerCAmelCase       					=    peter_wins_count / total_games_number

			__lowerCAmelCase       					=    round(_UpperCamelCase					,				ndigits=7   )

			return rounded_peter_win_probability


if __name__ == "__main__":
	print(f'''{solution() = }''')




