






'''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 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   str		,     __A:   List[str]		,     __A:   Union[str, Any]=99		,     __A:   Optional[int]=13		,     __A:   List[Any]=7		,     __A:   List[Any]=9		,     __A:   List[str]=True		,     __A:   List[Any]=True		,     __A:   List[Any]=False		,     __A:   Tuple=32		,     __A:   int=5		,     __A:   Optional[int]=4		,     __A:   Dict=37		,     __A:   Union[str, Any]=8		,     __A:   Dict=0.1		,     __A:   Dict=0.002		,     __A:   Dict=1		,     __A:   Union[str, Any]=0		,     __A:   int=0		,     __A:   List[str]=None		,     __A:   Optional[Any]=None		,     )    ->				List[str]:
												_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       __A (  self:   Any   )    ->				str:
												return TaConfig.from_pretrained('''google/umt5-base'''   )

							def       __A (  self:   Tuple		,     __A:   int		,     __A:   str		,     __A:   List[str]		,     __A:   Optional[Any]=None		,     __A:   str=None		,     __A:   int=None		,     __A:   Optional[int]=None		,     __A:   Dict=None		,     )    ->				Any:



												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=__A   )
												if decoder_head_mask is None:
																	_A								= torch.ones(config.num_decoder_layers		,     config.num_attention_heads		,     device=__A   )
												if cross_attn_head_mask is None:
																	_A								= torch.ones(
																	    config.num_decoder_layers		,     config.num_attention_heads		,     device=__A   )
												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       __A (  self:   Any   )    ->				Tuple:
												_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(__A		,     __A		,     __A   )
												return config, input_dict

							def       __A (  self:   str   )    ->				Dict:
												_A							,_A								= self.prepare_config_and_inputs()
												return config, inputs_dict

							def       __A (  self:   Any   )    ->				Optional[Any]:
												return TaConfig(
												    vocab_size=1_66		,     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       __A (  self:   Dict   )    ->				Optional[Any]:
												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       __A (  self:   Tuple		,     __A:   List[str]		,     __A:   Optional[Any]		,     __A:   Tuple		,     __A:   Any		,     __A:   Optional[Any]		,     __A:   int		,     )    ->				List[Any]:
												_A								= UMTaModel(config=__A   )
												model.to(__A   )
												model.eval()
												_A								= model(
												    input_ids=__A		,     decoder_input_ids=__A		,     attention_mask=__A		,     decoder_attention_mask=__A		,     )
												_A								= model(input_ids=__A		,     decoder_input_ids=__A   )
												_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(__A   )		,     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       __A (  self:   Optional[int]		,     __A:   List[Any]		,     __A:   str		,     __A:   str		,     __A:   Dict		,     __A:   Optional[int]		,     __A:   Any		,     )    ->				Optional[Any]:
												_A								= UMTaModel(config=__A   ).get_decoder().to(__A   ).eval()
												# first forward pass
												_A								= model(__A		,     use_cache=__A   )
												_A								= model(__A   )
												_A								= model(__A		,     use_cache=__A   )

												self.parent.assertTrue(len(__A   ) == len(__A   )   )
												self.parent.assertTrue(len(__A   ) == len(__A   ) + 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(__A   )['''last_hidden_state''']
												_A								= model(__A		,     past_key_values=__A   )['''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(__A		,     __A		,     atol=1e-3   )   )



							def       __A (  self:   str		,     __A:   Union[str, Any]		,     __A:   Optional[int]		,     )    ->				Dict:
												_A								= UMTaModel(config=__A   ).to(__A   ).half().eval()
												_A								= model(**__A   )['''last_hidden_state''']
												self.parent.assertFalse(torch.isnan(__A   ).any().item()   )





@require_torch
class 						SCREAMING_SNAKE_CASE				( UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	unittest.TestCase					):





							"""simple docstring"""

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

							def       __A (  self:   List[str]   )    ->				List[Any]:
												_A								= UMTaModelTester(self   )

							@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''   )
							def       __A (  self:   Any   )    ->				Union[str, Any]:
												_A								= self.model_tester.prepare_config_and_inputs()
												_A								= UMTaModel(config_and_inputs[0]   ).to(__A   )
												with tempfile.TemporaryDirectory() as tmpdirname:
																	torch.onnx.export(
																	    __A		,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,     f"""{tmpdirname}/t5_test.onnx"""		,     export_params=__A		,     opset_version=9		,     input_names=['''input_ids''', '''decoder_input_ids''']		,     )

							@unittest.skipIf(torch_device == '''cpu'''		,     '''Cant do half precision'''   )
							def       __A (  self:   Dict   )    ->				Optional[Any]:
												_A								= self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model_fpaa_forward(*__A   )

							def       __A (  self:   List[str]   )    ->				int:
												_A								= ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
												_A								= self.model_tester.prepare_config_and_inputs()
												_A								= config_and_inputs[0]
												_A								= UMTaForConditionalGeneration(__A   ).eval()
												model.to(__A   )

												_A								= {
												    '''head_mask''': torch.zeros(config.num_layers		,     config.num_heads		,     device=__A   ),
												    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers		,     config.num_heads		,     device=__A   ),
												    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers		,     config.num_heads		,     device=__A   ),
												}

												for attn_name, (name, mask) in zip(__A		,     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=__A   )

																	_A								= model.generate(
																	    config_and_inputs[1]['''input_ids''']		,     num_beams=1		,     max_length=3		,     output_attentions=__A		,     return_dict_in_generate=__A		,     **__A		,     )
																	# 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       __A (  self:   List[Any]   )    ->				int:
												pass





@require_torch
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE				( 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       __A (  self:   Any   )    ->				List[str]:
												_A								= UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''		,     return_dict=__A   ).to(__A   )
												_A								= AutoTokenizer.from_pretrained('''google/umt5-small'''		,     use_fast=__A		,     legacy=__A   )
												_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(__A		,     return_tensors='''pt'''		,     padding=__A   ).input_ids
												# fmt: off
												_A								= torch.tensor(
												    [
												        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
												        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
												        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
												        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
												        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
												    ]   )
												# fmt: on
												torch.testing.assert_allclose(__A		,     __A   )

												_A								= model.generate(input_ids.to(__A   )   )
												_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(__A   )
												self.assertEqual(__A		,     __A   )











import math




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= 2
					_A								= int(math.sqrt(_lowercase ) )  # Size of every segment
					_A								= [True] * (end + 1)
					_A								= []

					while start <= end:
										if temp[start] is True:
															in_prime.append(_lowercase )
															for i in range(start * start					,			end + 1					,			_lowercase ):
																				_A								= False
										start += 1
					prime += in_prime

					_A								= end + 1
					_A								= min(2 * end					,			_lowercase )

					while low <= n:
										_A								= [True] * (high - low + 1)
										for each in in_prime:
															_A								= math.floor(low / each ) * each
															if t < low:
																				t += each

															for j in range(_lowercase					,			high + 1					,			_lowercase ):
																				_A								= False

										for j in range(len(_lowercase ) ):
															if temp[j] is True:
																				prime.append(j + low )

										_A								= high + 1
										_A								= min(high + end					,			_lowercase )

					return prime


print(sieve(10**6))



