"""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        snake_case :




					"""simple docstring"""

					def __init__(       self     :			List[str]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Any=99					,lowerCamelCase__     :			Optional[Any]=13					,lowerCamelCase__     :			str=7					,lowerCamelCase__     :			int=9					,lowerCamelCase__     :			Optional[Any]=True					,lowerCamelCase__     :			Any=True					,lowerCamelCase__     :			Dict=False					,lowerCamelCase__     :			Dict=32					,lowerCamelCase__     :			Optional[int]=5					,lowerCamelCase__     :			Optional[int]=4					,lowerCamelCase__     :			str=37					,lowerCamelCase__     :			Optional[Any]=8					,lowerCamelCase__     :			Tuple=0.1					,lowerCamelCase__     :			List[str]=0.0_0_2					,lowerCamelCase__     :			Dict=1					,lowerCamelCase__     :			Optional[Any]=0					,lowerCamelCase__     :			int=0					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			List[Any]=None					,):
									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 	__lowerCAmelCase		(       self     :			Tuple    ):
									return TaConfig.from_pretrained('google/umt5-base'    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Optional[Any]=None					,lowerCamelCase__     :			Dict=None					,lowerCamelCase__     :			Optional[int]=None					,lowerCamelCase__     :			List[str]=None					,lowerCamelCase__     :			int=None					,):
									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=lowerCamelCase__    )
									if decoder_head_mask is None:
													UpperCAmelCase__    =							torch.ones(config.num_decoder_layers					,config.num_attention_heads					,device=lowerCamelCase__    )
									if cross_attn_head_mask is None:
													UpperCAmelCase__    =							torch.ones(
													    config.num_decoder_layers					,config.num_attention_heads					,device=lowerCamelCase__    )
									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     :			Dict    ):
									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(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    )
									return config, input_dict







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.prepare_config_and_inputs()
									return config, inputs_dict







					def 	__lowerCAmelCase		(       self     :			str    ):
									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     :			str    ):
									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     :			Dict					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			str					,):
									UpperCAmelCase__    =							UMTaModel(config=lowerCamelCase__    )
									model.to(lowerCamelCase__    )
									model.eval()
									UpperCAmelCase__    =							model(
									    input_ids=lowerCamelCase__					,decoder_input_ids=lowerCamelCase__					,attention_mask=lowerCamelCase__					,decoder_attention_mask=lowerCamelCase__					,)
									UpperCAmelCase__    =							model(input_ids=lowerCamelCase__					,decoder_input_ids=lowerCamelCase__    )
									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(lowerCamelCase__    )					,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     :			List[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			Any					,):
									UpperCAmelCase__    =							UMTaModel(config=lowerCamelCase__    ).get_decoder().to(lowerCamelCase__    ).eval()
									# first forward pass
									UpperCAmelCase__    =							model(lowerCamelCase__					,use_cache=lowerCamelCase__    )
									UpperCAmelCase__    =							model(lowerCamelCase__    )
									UpperCAmelCase__    =							model(lowerCamelCase__					,use_cache=lowerCamelCase__    )

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







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			str					,):
									UpperCAmelCase__    =							UMTaModel(config=lowerCamelCase__    ).to(lowerCamelCase__    ).half().eval()
									UpperCAmelCase__    =							model(**lowerCamelCase__    )['last_hidden_state']
									self.parent.assertFalse(torch.isnan(lowerCamelCase__    ).any().item()    )






@require_torch
class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					__UpperCAmelCase   ,					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     :			Optional[Any]    ):
									UpperCAmelCase__    =							UMTaModelTester(self    )







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







					@unittest.skipIf(torch_device == 'cpu'					,'Cant do half precision'    )
					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							['encoder_attentions', 'decoder_attentions', 'cross_attentions']
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									UpperCAmelCase__    =							config_and_inputs[0]
									UpperCAmelCase__    =							UMTaForConditionalGeneration(lowerCamelCase__    ).eval()
									model.to(lowerCamelCase__    )

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

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

													UpperCAmelCase__    =							model.generate(
													    config_and_inputs[1]['input_ids']					,num_beams=1					,max_length=3					,output_attentions=lowerCamelCase__					,return_dict_in_generate=lowerCamelCase__					,**lowerCamelCase__					,)
													# 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 	__lowerCAmelCase		(       self     :			List[str]    ):
									pass






@require_torch
@require_sentencepiece
@require_tokenizers
class        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 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							UMTaForConditionalGeneration.from_pretrained('google/umt5-small'					,return_dict=lowerCamelCase__    ).to(lowerCamelCase__    )
									UpperCAmelCase__    =							AutoTokenizer.from_pretrained('google/umt5-small'					,use_fast=lowerCamelCase__					,legacy=lowerCamelCase__    )
									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(lowerCamelCase__					,return_tensors='pt'					,padding=lowerCamelCase__    ).input_ids
									# fmt: off
									UpperCAmelCase__    =							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(lowerCamelCase__					,lowerCamelCase__    )

									UpperCAmelCase__    =							model.generate(input_ids.to(lowerCamelCase__    )    )
									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(lowerCamelCase__    )
									self.assertEqual(lowerCamelCase__					,lowerCamelCase__    )




"""simple docstring"""






def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return x if y == 0 else greatest_common_divisor(lowerCamelCase      ,    x % y )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return (x * y) // greatest_common_divisor(lowerCamelCase      ,    lowerCamelCase )



def 						a_		(      lowerCamelCase = 2_0 ):
				UpperCAmelCase__    =							1
				for i in range(1      ,    n + 1 ):
								UpperCAmelCase__    =							lcm(lowerCamelCase      ,    lowerCamelCase )
				return g


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



