
"""simple docstring"""





import random
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    num - 1
					A_								=    0

					while s % 2 == 0:
										A_								=    s // 2
										t += 1

					for _ in range(5					):
										A_								=    random.randrange(2					, num - 1					)
										A_								=    pow(__a					, __a					, __a					)
										if v != 1:
															A_								=    0
															while v != (num - 1):
																				if i == t - 1:
																									return False
																				else:
																									A_								=    i + 1
																									A_								=    (v**2) % num
					return True
def 				A_  (__a					):

					'''simple docstring'''





					if num < 2:
										return False

					A_								=    [
					    2,
					    3,
					    5,
					    7,
					    11,
					    13,
					    17,
					    19,
					    23,
					    29,
					    31,
					    37,
					    41,
					    43,
					    47,
					    53,
					    59,
					    61,
					    67,
					    71,
					    73,
					    79,
					    83,
					    89,
					    97,
					    101,
					    103,
					    107,
					    109,
					    113,
					    127,
					    131,
					    137,
					    139,
					    149,
					    151,
					    157,
					    163,
					    167,
					    173,
					    179,
					    181,
					    191,
					    193,
					    197,
					    199,
					    211,
					    223,
					    227,
					    229,
					    233,
					    239,
					    241,
					    251,
					    257,
					    263,
					    269,
					    271,
					    277,
					    281,
					    283,
					    293,
					    307,
					    311,
					    313,
					    317,
					    331,
					    337,
					    347,
					    349,
					    353,
					    359,
					    367,
					    373,
					    379,
					    383,
					    389,
					    397,
					    401,
					    409,
					    419,
					    421,
					    431,
					    433,
					    439,
					    443,
					    449,
					    457,
					    461,
					    463,
					    467,
					    479,
					    487,
					    491,
					    499,
					    503,
					    509,
					    521,
					    523,
					    541,
					    547,
					    557,
					    563,
					    569,
					    571,
					    577,
					    587,
					    593,
					    599,
					    601,
					    607,
					    613,
					    617,
					    619,
					    631,
					    641,
					    643,
					    647,
					    653,
					    659,
					    661,
					    673,
					    677,
					    683,
					    691,
					    701,
					    709,
					    719,
					    727,
					    733,
					    739,
					    743,
					    751,
					    757,
					    761,
					    769,
					    773,
					    787,
					    797,
					    809,
					    811,
					    821,
					    823,
					    827,
					    829,
					    839,
					    853,
					    857,
					    859,
					    863,
					    877,
					    881,
					    883,
					    887,
					    907,
					    911,
					    919,
					    929,
					    937,
					    941,
					    947,
					    953,
					    967,
					    971,
					    977,
					    983,
					    991,
					    997,
					]

					if num in low_primes:
										return True

					for prime in low_primes:
										if (num % prime) == 0:
															return False

					return rabin_miller(__a					)
def 				A_  (__a = 1024					):

					'''simple docstring'''





					while True:
										A_								=    random.randrange(2 ** (keysize - 1)					, 2 ** (keysize)					)
										if is_prime_low_num(__a					):
															return num


if __name__ == "__main__":
		UpperCamelCase_       :    int						      =			generate_large_prime()
		print(('''Prime number:''', num))
		print(('''is_prime_low_num:''', is_prime_low_num(num)))






"""simple docstring"""





import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class __lowerCAmelCase					:




			"""simple docstring"""







			def __init__(					self    :       Dict							, _snake_case    :       Any							, _snake_case    :       Union[str, Any]=99							, _snake_case    :       List[Any]=13							, _snake_case    :       Optional[Any]=7							, _snake_case    :       Union[str, Any]=9							, _snake_case    :       List[Any]=True							, _snake_case    :       Optional[int]=True							, _snake_case    :       Any=False							, _snake_case    :       str=32							, _snake_case    :       Any=5							, _snake_case    :       List[str]=4							, _snake_case    :       Dict=37							, _snake_case    :       List[Any]=8							, _snake_case    :       int=0.1							, _snake_case    :       List[str]=0.0_0_2							, _snake_case    :       Optional[Any]=1							, _snake_case    :       Union[str, Any]=0							, _snake_case    :       Any=0							, _snake_case    :       List[str]=None							, _snake_case    :       List[Any]=None							, )   ->			int:







								"""simple docstring"""
								A_								=    parent
								A_								=    batch_size
								A_								=    encoder_seq_length
								A_								=    decoder_seq_length
								# For common tests
								A_								=    self.decoder_seq_length
								A_								=    is_training
								A_								=    use_attention_mask
								A_								=    use_labels
								A_								=    vocab_size
								A_								=    hidden_size
								A_								=    num_hidden_layers
								A_								=    num_attention_heads
								A_								=    d_ff
								A_								=    relative_attention_num_buckets
								A_								=    dropout_rate
								A_								=    initializer_factor
								A_								=    eos_token_id
								A_								=    pad_token_id
								A_								=    decoder_start_token_id
								A_								=    None
								A_								=    decoder_layers




			def   lowerCamelCase__  (					self    :       Any						)   ->			Any:







								"""simple docstring"""
								return TaConfig.from_pretrained("google/umt5-base"						)




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Optional[int]							, _snake_case    :       Optional[int]							, _snake_case    :       int							, _snake_case    :       Dict=None							, _snake_case    :       Dict=None							, _snake_case    :       str=None							, _snake_case    :       Any=None							, _snake_case    :       Union[str, Any]=None							, )   ->			List[Any]:







								"""simple docstring"""
								if attention_mask is None:
													A_								=    input_ids.ne(config.pad_token_id						)
								if decoder_attention_mask is None:
													A_								=    decoder_input_ids.ne(config.pad_token_id						)
								if head_mask is None:
													A_								=    torch.ones(config.num_hidden_layers							, config.num_attention_heads							, device=_snake_case						)
								if decoder_head_mask is None:
													A_								=    torch.ones(config.num_decoder_layers							, config.num_attention_heads							, device=_snake_case						)
								if cross_attn_head_mask is None:
													A_								=    torch.ones(
													    config.num_decoder_layers							, config.num_attention_heads							, device=_snake_case						)
								return {
								    "input_ids": input_ids,
								    "decoder_input_ids": decoder_input_ids,
								    "attention_mask": attention_mask,
								    "decoder_attention_mask": decoder_attention_mask,
								    "head_mask": head_mask,
								    "decoder_head_mask": decoder_head_mask,
								    "cross_attn_head_mask": cross_attn_head_mask,
								}




			def   lowerCamelCase__  (					self    :       int						)   ->			Tuple:







								"""simple docstring"""
								A_								=    ids_tensor([self.batch_size, self.encoder_seq_length]							, self.vocab_size						)
								A_								=    ids_tensor([self.batch_size, self.decoder_seq_length]							, self.vocab_size						)

								# we need to clamp the input ids here to avoid having pad token in between
								# this is because for NllbMoe the position_ids are prepared such that
								# all pad tokens have pos id = 2 and rest are between 2..seq_length
								# and the seq_length here is seq_length - num_pad_tokens
								# but when using past, there is no way of knowing if the past input ids had
								# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
								# position_ids being off by num_pad_tokens in past input
								A_								=    input_ids.clamp(self.pad_token_id + 1						)
								A_								=    decoder_input_ids.clamp(self.pad_token_id + 1						)

								A_								=    self.get_config()
								A_								=    config.num_attention_heads
								A_								=    self.prepare_inputs_dict(_snake_case							, _snake_case							, _snake_case						)
								return config, input_dict




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







								"""simple docstring"""
								A_			,     A_								=    self.prepare_config_and_inputs()
								return config, inputs_dict




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[str]:







								"""simple docstring"""
								return TaConfig(
								    vocab_size=166							, d_model=self.hidden_size							, d_ff=self.d_ff							, d_kv=self.hidden_size // self.num_attention_heads							, num_layers=self.num_hidden_layers							, num_decoder_layers=self.decoder_layers							, num_heads=self.num_attention_heads							, relative_attention_num_buckets=self.relative_attention_num_buckets							, dropout_rate=self.dropout_rate							, initializer_factor=self.initializer_factor							, eos_token_id=self.eos_token_id							, bos_token_id=self.pad_token_id							, pad_token_id=self.pad_token_id							, decoder_start_token_id=self.decoder_start_token_id							, )




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







								"""simple docstring"""
								return TaConfig(
								    vocab_size=self.vocab_size							, d_model=self.hidden_size							, d_ff=self.d_ff							, d_kv=self.hidden_size // self.num_attention_heads							, num_layers=self.num_hidden_layers							, num_decoder_layers=self.decoder_layers							, num_heads=self.num_attention_heads							, relative_attention_num_buckets=self.relative_attention_num_buckets							, dropout_rate=self.dropout_rate							, initializer_factor=self.initializer_factor							, eos_token_id=self.eos_token_id							, bos_token_id=self.pad_token_id							, pad_token_id=self.pad_token_id							, decoder_start_token_id=self.decoder_start_token_id							, )




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







								"""simple docstring"""
								A_								=    UMTaModel(config=_snake_case						)
								model.to(_snake_case						)
								model.eval()
								A_								=    model(
								    input_ids=_snake_case							, decoder_input_ids=_snake_case							, attention_mask=_snake_case							, decoder_attention_mask=_snake_case							, )
								A_								=    model(input_ids=_snake_case							, decoder_input_ids=_snake_case						)
								A_								=    result.last_hidden_state
								A_								=    result.past_key_values
								A_								=    result.encoder_last_hidden_state

								self.parent.assertEqual(encoder_output.size()							, (self.batch_size, self.encoder_seq_length, self.hidden_size)						)
								self.parent.assertEqual(decoder_output.size()							, (self.batch_size, self.decoder_seq_length, self.hidden_size)						)
								# There should be `num_layers` key value embeddings stored in decoder_past
								self.parent.assertEqual(len(_snake_case						)							, config.num_layers						)
								# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
								self.parent.assertEqual(len(decoder_past[0]						)							, 4						)




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







								"""simple docstring"""
								A_								=    UMTaModel(config=_snake_case						).get_decoder().to(_snake_case						).eval()
								# first forward pass
								A_								=    model(_snake_case							, use_cache=_snake_case						)
								A_								=    model(_snake_case						)
								A_								=    model(_snake_case							, use_cache=_snake_case						)

								self.parent.assertTrue(len(_snake_case						) == len(_snake_case						)						)
								self.parent.assertTrue(len(_snake_case						) == len(_snake_case						) + 1						)

								A_			,     A_								=    outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								A_								=    ids_tensor((self.batch_size, 1)							, config.vocab_size						)

								# append to next input_ids and
								A_								=    torch.cat([input_ids, next_tokens]							, dim=-1						)

								A_								=    model(_snake_case						)["last_hidden_state"]
								A_								=    model(_snake_case							, past_key_values=_snake_case						)["last_hidden_state"]

								# select random slice
								A_								=    ids_tensor((1,)							, output_from_past.shape[-1]						).item()
								A_								=    output_from_no_past[:, -1, random_slice_idx].detach()
								A_								=    output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(_snake_case							, _snake_case							, atol=1e-3						)						)






			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       Any							, _snake_case    :       str							, )   ->			List[Any]:







								"""simple docstring"""
								A_								=    UMTaModel(config=_snake_case						).to(_snake_case						).half().eval()
								A_								=    model(**_snake_case						)["last_hidden_state"]
								self.parent.assertFalse(torch.isnan(_snake_case						).any().item()						)





@require_torch
class __lowerCAmelCase					(       _lowercase  ,					_lowercase  ,					_lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					(
			    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
			)
			snake_case						   =					(UMTaForConditionalGeneration,) if is_torch_available() else ()
			snake_case						   =					(
			    {
			        "conversational": UMTaForConditionalGeneration,
			        "feature-extraction": UMTaModel,
			        "summarization": UMTaForConditionalGeneration,
			        "text2text-generation": UMTaForConditionalGeneration,
			        "translation": UMTaForConditionalGeneration,
			        "question-answering": UMTaForQuestionAnswering,
			    }
			    if is_torch_available()
			    else {}
			)
			snake_case						   =					True
			snake_case						   =					False
			snake_case						   =					False
			snake_case						   =					True
			snake_case						   =					True
			# The small UMT5 model needs higher percentages for CPU/MP tests
			snake_case						   =					[0.8, 0.9]




			def   lowerCamelCase__  (					self    :       str						)   ->			Tuple:







								"""simple docstring"""
								A_								=    UMTaModelTester(self						)




			@unittest.skip("Test has a segmentation fault on torch 1.8.0"						)
			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								A_								=    UMTaModel(config_and_inputs[0]						).to(_snake_case						)
								with tempfile.TemporaryDirectory() as tmpdirname:
													torch.onnx.export(
													    _snake_case							, (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])							, F'{tmpdirname}/t5_test.onnx'							, export_params=_snake_case							, opset_version=9							, input_names=["input_ids", "decoder_input_ids"]							, )




			@unittest.skipIf(torch_device == "cpu"							, "Cant do half precision"						)
			def   lowerCamelCase__  (					self    :       Tuple						)   ->			List[str]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*_snake_case						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			Dict:







								"""simple docstring"""
								A_								=    ["encoder_attentions", "decoder_attentions", "cross_attentions"]
								A_								=    self.model_tester.prepare_config_and_inputs()
								A_								=    config_and_inputs[0]
								A_								=    UMTaForConditionalGeneration(_snake_case						).eval()
								model.to(_snake_case						)

								A_								=    {
								    "head_mask": torch.zeros(config.num_layers							, config.num_heads							, device=_snake_case						),
								    "decoder_head_mask": torch.zeros(config.num_decoder_layers							, config.num_heads							, device=_snake_case						),
								    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers							, config.num_heads							, device=_snake_case						),
								}

								for attn_name, (name, mask) in zip(_snake_case							, head_masking.items()						):
													A_								=    {name: mask}
													# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
													if name == "head_mask":
																		A_								=    torch.ones(
																		    config.num_decoder_layers							, config.num_heads							, device=_snake_case						)

													A_								=    model.generate(
													    config_and_inputs[1]["input_ids"]							, num_beams=1							, max_length=3							, output_attentions=_snake_case							, return_dict_in_generate=_snake_case							, **_snake_case							, )
													# We check the state of decoder_attentions and cross_attentions just from the last step
													A_								=    out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
													self.assertEqual(sum([w.sum().item() for w in attn_weights]						)							, 0.0						)






			@unittest.skip("Does not work on the tiny model as we keep hitting edge cases."						)
			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			List[str]:







								"""simple docstring"""
								pass





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




			"""simple docstring"""







			@slow
			@unittest.skip(
			    "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"						)
			def   lowerCamelCase__  (					self    :       Dict						)   ->			int:







								"""simple docstring"""
								A_								=    UMTaForConditionalGeneration.from_pretrained("google/umt5-small"							, return_dict=_snake_case						).to(_snake_case						)
								A_								=    AutoTokenizer.from_pretrained("google/umt5-small"							, use_fast=_snake_case							, legacy=_snake_case						)
								A_								=    [
								    "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
								    "No se como puedo <extra_id_0>.",
								    "This is the reason why we <extra_id_0> them.",
								    "The <extra_id_0> walks in <extra_id_1>, seats",
								    "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
								]
								A_								=    tokenizer(_snake_case							, return_tensors="pt"							, padding=_snake_case						).input_ids
								# fmt: off
								A_								=    torch.tensor(
								    [
								        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
								        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
								    ]						)
								# fmt: on
								torch.testing.assert_allclose(_snake_case							, _snake_case						)

								A_								=    model.generate(input_ids.to(_snake_case						)						)
								A_								=    [
								    "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
								    "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
								    "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
								    "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
								    "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
								]
								A_								=    tokenizer.batch_decode(_snake_case						)
								self.assertEqual(_snake_case							, _snake_case						)




