
"""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        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=99							,						__a=13							,						__a=7							,						__a=9							,						__a=True							,						__a=True							,						__a=False							,						__a=32							,						__a=5							,						__a=4							,						__a=37							,						__a=8							,						__a=0.1							,						__a=0.002							,						__a=1							,						__a=0							,						__a=0							,						__a=None							,						__a=None							,						)			->			List[Any]:






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



		def 		UpperCAmelCase				(   self)			->			Any:






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



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						)			->			List[Any]:






							'''simple docstring'''
							if attention_mask is None:
												_UpperCamelCase									=    input_ids.ne(config.pad_token_id)
							if decoder_attention_mask is None:
												_UpperCamelCase									=    decoder_input_ids.ne(config.pad_token_id)
							if head_mask is None:
												_UpperCamelCase									=    torch.ones(config.num_hidden_layers							,						config.num_attention_heads							,						device=__a)
							if decoder_head_mask is None:
												_UpperCamelCase									=    torch.ones(config.num_decoder_layers							,						config.num_attention_heads							,						device=__a)
							if cross_attn_head_mask is None:
												_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    ids_tensor([self.batch_size, self.encoder_seq_length]							,						self.vocab_size)
							_UpperCamelCase									=    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
							_UpperCamelCase									=    input_ids.clamp(self.pad_token_id + 1)
							_UpperCamelCase									=    decoder_input_ids.clamp(self.pad_token_id + 1)

							_UpperCamelCase									=    self.get_config()
							_UpperCamelCase									=    config.num_attention_heads
							_UpperCamelCase									=    self.prepare_inputs_dict(__a							,						__a							,						__a)
							return config, input_dict



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.prepare_config_and_inputs()
							return config, inputs_dict



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							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 		UpperCAmelCase				(   self)			->			int:






							'''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 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    UMTaModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(
							    input_ids=__a							,						decoder_input_ids=__a							,						attention_mask=__a							,						decoder_attention_mask=__a							,						)
							_UpperCamelCase									=    model(input_ids=__a							,						decoder_input_ids=__a)
							_UpperCamelCase									=    result.last_hidden_state
							_UpperCamelCase									=    result.past_key_values
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    UMTaModel(config=__a).get_decoder().to(__a).eval()
							# first forward pass
							_UpperCamelCase									=    model(__a							,						use_cache=__a)
							_UpperCamelCase									=    model(__a)
							_UpperCamelCase									=    model(__a							,						use_cache=__a)

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

							_UpperCamelCase					,    _UpperCamelCase									=    outputs.to_tuple()

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

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

							_UpperCamelCase									=    model(__a)['''last_hidden_state''']
							_UpperCamelCase									=    model(__a							,						past_key_values=__a)['''last_hidden_state''']

							# select random slice
							_UpperCamelCase									=    ids_tensor((1,)							,						output_from_past.shape[-1]).item()
							_UpperCamelCase									=    output_from_no_past[:, -1, random_slice_idx].detach()
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self							,						__a							,						__a							,						)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    UMTaModel(config=__a).to(__a).half().eval()
							_UpperCamelCase									=    model(**__a)['''last_hidden_state''']
							self.parent.assertFalse(torch.isnan(__a).any().item())






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (
		    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
		)
		lowercase__         = (UMTaForConditionalGeneration,) if is_torch_available() else ()
		lowercase__         = (
		    {
		        'conversational': UMTaForConditionalGeneration,
		        'feature-extraction': UMTaModel,
		        'summarization': UMTaForConditionalGeneration,
		        'text2text-generation': UMTaForConditionalGeneration,
		        'translation': UMTaForConditionalGeneration,
		        'question-answering': UMTaForQuestionAnswering,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase__         = True
		lowercase__         = False
		lowercase__         = False
		lowercase__         = True
		lowercase__         = True
		# The small UMT5 model needs higher percentages for CPU/MP tests
		lowercase__         = [0.8, 0.9]



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    UMTaModelTester(self)



		@unittest.skip('''Test has a segmentation fault on torch 1.8.0''')
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model_fpaa_forward(*__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							_UpperCamelCase									=    config_and_inputs[0]
							_UpperCamelCase									=    UMTaForConditionalGeneration(__a).eval()
							model.to(__a)

							_UpperCamelCase									=    {
							    '''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()):
												_UpperCamelCase									=    {name: mask}
												# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
												if name == "head_mask":
																	_UpperCamelCase									=    torch.ones(
																	    config.num_decoder_layers							,						config.num_heads							,						device=__a)

												_UpperCamelCase									=    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
												_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							pass






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



		@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 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''							,						return_dict=__a).to(__a)
							_UpperCamelCase									=    AutoTokenizer.from_pretrained('''google/umt5-small'''							,						use_fast=__a							,						legacy=__a)
							_UpperCamelCase									=    [
							    '''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>.''',
							]
							_UpperCamelCase									=    tokenizer(__a							,						return_tensors='''pt'''							,						padding=__a).input_ids
							# fmt: off
							_UpperCamelCase									=    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)

							_UpperCamelCase									=    model.generate(input_ids.to(__a))
							_UpperCamelCase									=    [
							    '''<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>''',
							]
							_UpperCamelCase									=    tokenizer.batch_decode(__a)
							self.assertEqual(__a							,						__a)








"""simple docstring"""





from __future__ import annotations

from functools import lru_cache
from math import ceil

_a								=						100

_a								=						set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a								=						42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
				if prime not in primes:
								continue
				primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))



@lru_cache(maxsize=1_00      )
def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					if number_to_partition < 0:
										return set()
					elif number_to_partition == 0:
										return {1}

					_UpperCamelCase									=    set()
					_UpperCamelCase									=    42
					_UpperCamelCase									=    42

					for prime in primes:
										if prime > number_to_partition:
															continue
										for sub in partition(number_to_partition - prime      ):
															ret.add(sub * prime      )

					return ret



def  lowerCamelCase__						(     __snake_case = 50_00      )				->						int | None:


					"""simple docstring"""





					for number_to_partition in range(1,	__snake_case      ):
										if len(partition(__snake_case      )      ) > number_unique_partitions:
															return number_to_partition
					return None


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






