


'''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    ,						UpperCamelCase    ,						UpperCamelCase=99    ,						UpperCamelCase=13    ,						UpperCamelCase=7    ,						UpperCamelCase=9    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase=False    ,						UpperCamelCase=32    ,						UpperCamelCase=5    ,						UpperCamelCase=4    ,						UpperCamelCase=37    ,						UpperCamelCase=8    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.002    ,						UpperCamelCase=1    ,						UpperCamelCase=0    ,						UpperCamelCase=0    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						):







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






		def 						snake_case	(					self  ):







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






		def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						):







			"""simple docstring"""






			if attention_mask is None:
				lowerCamelCase_  				=						input_ids.ne(config.pad_token_id  )
			if decoder_attention_mask is None:
				lowerCamelCase_  				=						decoder_input_ids.ne(config.pad_token_id  )
			if head_mask is None:
				lowerCamelCase_  				=						torch.ones(config.num_hidden_layers    ,						config.num_attention_heads    ,						device=__UpperCamelCase  )
			if decoder_head_mask is None:
				lowerCamelCase_  				=						torch.ones(config.num_decoder_layers    ,						config.num_attention_heads    ,						device=__UpperCamelCase  )
			if cross_attn_head_mask is None:
				lowerCamelCase_  				=						torch.ones(
				    config.num_decoder_layers    ,						config.num_attention_heads    ,						device=__UpperCamelCase  )
			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 						snake_case	(					self  ):







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

			lowerCamelCase_  				=						self.get_config()
			lowerCamelCase_  				=						config.num_attention_heads
			lowerCamelCase_  				=						self.prepare_inputs_dict(__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase  )
			return config, input_dict






		def 						snake_case	(					self  ):







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






		def 						snake_case	(					self  ):







			"""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 						snake_case	(					self  ):







			"""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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						):







			"""simple docstring"""
			lowerCamelCase_  				=						UMTaModel(config=__UpperCamelCase  )
			model.to(__UpperCamelCase  )
			model.eval()
			lowerCamelCase_  				=						model(
			    input_ids=__UpperCamelCase    ,						decoder_input_ids=__UpperCamelCase    ,						attention_mask=__UpperCamelCase    ,						decoder_attention_mask=__UpperCamelCase    ,						)
			lowerCamelCase_  				=						model(input_ids=__UpperCamelCase    ,						decoder_input_ids=__UpperCamelCase  )
			lowerCamelCase_  				=						result.last_hidden_state
			lowerCamelCase_  				=						result.past_key_values
			lowerCamelCase_  				=						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(__UpperCamelCase  )    ,						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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						):







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

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

			lowerCamelCase_				,lowerCamelCase_  				=						outputs.to_tuple()

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

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

			lowerCamelCase_  				=						model(__UpperCamelCase  )["last_hidden_state"]
			lowerCamelCase_  				=						model(__UpperCamelCase    ,						past_key_values=__UpperCamelCase  )["last_hidden_state"]

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

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







		def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						):







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





@require_torch
class   snake_case					(					_UpperCAmelCase    ,		_UpperCAmelCase    ,		_UpperCAmelCase    ,		unittest.TestCase    ):





		"""simple docstring"""




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






		def 						snake_case	(					self  ):







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






		@unittest.skip("Test has a segmentation fault on torch 1.8.0"  )
		def 						snake_case	(					self  ):







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






		@unittest.skipIf(torch_device == "cpu"    ,						"Cant do half precision"  )
		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase  )






		def 						snake_case	(					self  ):







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

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

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

				lowerCamelCase_  				=						model.generate(
				    config_and_inputs[1]["input_ids"]    ,						num_beams=1    ,						max_length=3    ,						output_attentions=__UpperCamelCase    ,						return_dict_in_generate=__UpperCamelCase    ,						**__UpperCamelCase    ,						)
				# We check the state of decoder_attentions and cross_attentions just from the last step
				lowerCamelCase_  				=						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 						snake_case	(					self  ):







			"""simple docstring"""
			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 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						UMTaForConditionalGeneration.from_pretrained("google/umt5-small"    ,						return_dict=__UpperCamelCase  ).to(__UpperCamelCase  )
			lowerCamelCase_  				=						AutoTokenizer.from_pretrained("google/umt5-small"    ,						use_fast=__UpperCamelCase    ,						legacy=__UpperCamelCase  )
			lowerCamelCase_  				=						[
			    "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>.",
			]
			lowerCamelCase_  				=						tokenizer(__UpperCamelCase    ,						return_tensors="pt"    ,						padding=__UpperCamelCase  ).input_ids
			# fmt: off
			lowerCamelCase_  				=						torch.tensor(
			    [
			        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
			        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
			    ]  )
			# fmt: on
			torch.testing.assert_allclose(__UpperCamelCase    ,						__UpperCamelCase  )

			lowerCamelCase_  				=						model.generate(input_ids.to(__UpperCamelCase  )  )
			lowerCamelCase_  				=						[
			    "<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>",
			]
			lowerCamelCase_  				=						tokenizer.batch_decode(__UpperCamelCase  )
			self.assertEqual(__UpperCamelCase    ,						__UpperCamelCase  )







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int = 1000	):
 return sum(2 * a * ((a - 1) // 2) for a in range(3      ,       n + 1	)	)


if __name__ == "__main__":
     print(solution())



