






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			,						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__=0.0_02			,						a__=1			,						a__=0			,						a__=0			,						a__=None			,						a__=None			,						) ->      Dict:






            '''simple docstring'''





            __snake_case			:Tuple          =						parent
            __snake_case			:Union[str, Any]          =						batch_size
            __snake_case			:List[str]          =						encoder_seq_length
            __snake_case			:Any          =						decoder_seq_length
            # For common tests
            __snake_case			:str          =						self.decoder_seq_length
            __snake_case			:Dict          =						is_training
            __snake_case			:Optional[int]          =						use_attention_mask
            __snake_case			:Dict          =						use_labels
            __snake_case			:Any          =						vocab_size
            __snake_case			:Tuple          =						hidden_size
            __snake_case			:int          =						num_hidden_layers
            __snake_case			:Optional[int]          =						num_attention_heads
            __snake_case			:Optional[int]          =						d_ff
            __snake_case			:int          =						relative_attention_num_buckets
            __snake_case			:Optional[Any]          =						dropout_rate
            __snake_case			:Optional[Any]          =						initializer_factor
            __snake_case			:Optional[Any]          =						eos_token_id
            __snake_case			:Any          =						pad_token_id
            __snake_case			:Tuple          =						decoder_start_token_id
            __snake_case			:str          =						None
            __snake_case			:List[str]          =						decoder_layers



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__=None			,						a__=None			,						a__=None			,						a__=None			,						a__=None			,						) ->      Optional[int]:






            '''simple docstring'''





            if attention_mask is None:
                 __snake_case			:Optional[Any]          =						input_ids.ne(config.pad_token_id    )
            if decoder_attention_mask is None:
                 __snake_case			:Optional[int]          =						decoder_input_ids.ne(config.pad_token_id    )
            if head_mask is None:
                 __snake_case			:Any          =						torch.ones(config.num_hidden_layers			,						config.num_attention_heads			,						device=a__    )
            if decoder_head_mask is None:
                 __snake_case			:List[str]          =						torch.ones(config.num_decoder_layers			,						config.num_attention_heads			,						device=a__    )
            if cross_attn_head_mask is None:
                 __snake_case			:Tuple          =						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 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						ids_tensor([self.batch_size, self.encoder_seq_length]			,						self.vocab_size    )
            __snake_case			:int          =						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
            __snake_case			:Union[str, Any]          =						input_ids.clamp(self.pad_token_id + 1    )
            __snake_case			:int          =						decoder_input_ids.clamp(self.pad_token_id + 1    )

            __snake_case			:List[str]          =						self.get_config()
            __snake_case			:List[str]          =						config.num_attention_heads
            __snake_case			:int          =						self.prepare_inputs_dict(a__			,						a__			,						a__    )
            return config, input_dict



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case       , __snake_case			:int          =						self.prepare_config_and_inputs()
            return config, inputs_dict



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






            '''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 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						UMTaModel(config=a__    )
            model.to(a__    )
            model.eval()
            __snake_case			:int          =						model(
                input_ids=a__			,						decoder_input_ids=a__			,						attention_mask=a__			,						decoder_attention_mask=a__			,						)
            __snake_case			:str          =						model(input_ids=a__			,						decoder_input_ids=a__    )
            __snake_case			:Tuple          =						result.last_hidden_state
            __snake_case			:str          =						result.past_key_values
            __snake_case			:Dict          =						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 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:Any          =						UMTaModel(config=a__    ).get_decoder().to(a__    ).eval()
            # first forward pass
            __snake_case			:Optional[Any]          =						model(a__			,						use_cache=a__    )
            __snake_case			:Any          =						model(a__    )
            __snake_case			:Optional[Any]          =						model(a__			,						use_cache=a__    )

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

            __snake_case       , __snake_case			:Optional[Any]          =						outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            __snake_case			:Optional[Any]          =						ids_tensor((self.batch_size, 1)			,						config.vocab_size    )

            # append to next input_ids and
            __snake_case			:List[Any]          =						torch.cat([input_ids, next_tokens]			,						dim=-1    )

            __snake_case			:Dict          =						model(a__    )["""last_hidden_state"""]
            __snake_case			:Any          =						model(a__			,						past_key_values=a__    )["""last_hidden_state"""]

            # select random slice
            __snake_case			:Optional[int]          =						ids_tensor((1,)			,						output_from_past.shape[-1]    ).item()
            __snake_case			:List[str]          =						output_from_no_past[:, -1, random_slice_idx].detach()
            __snake_case			:Tuple          =						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 					__lowercase						(							self			,						a__			,						a__			,						) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						UMTaModel(config=a__    ).to(a__    ).half().eval()
            __snake_case			:Optional[int]          =						model(**a__    )["""last_hidden_state"""]
            self.parent.assertFalse(torch.isnan(a__    ).any().item()    )




@require_torch
class 			snake_case__   (							lowercase_	,    lowercase_	,    lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= (
           (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
       )
       lowerCamelCase							:				Optional[Any] 				= (UMTaForConditionalGeneration,) if is_torch_available() else ()
       lowerCamelCase							:				Tuple 				= (
           {
               "conversational": UMTaForConditionalGeneration,
               "feature-extraction": UMTaModel,
               "summarization": UMTaForConditionalGeneration,
               "text2text-generation": UMTaForConditionalGeneration,
               "translation": UMTaForConditionalGeneration,
               "question-answering": UMTaForQuestionAnswering,
           }
           if is_torch_available()
           else {}
       )
       lowerCamelCase							:				int 				= True
       lowerCamelCase							:				Optional[int] 				= False
       lowerCamelCase							:				str 				= False
       lowerCamelCase							:				Optional[Any] 				= True
       lowerCamelCase							:				Optional[Any] 				= True
       # The small UMT5 model needs higher percentages for CPU/MP tests
       lowerCamelCase							:				List[Any] 				= [0.8, 0.9]



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:List[str]          =						UMTaModelTester(self    )



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






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.model_tester.prepare_config_and_inputs()
            __snake_case			:Dict          =						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 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*a__    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:List[Any]          =						["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
            __snake_case			:str          =						self.model_tester.prepare_config_and_inputs()
            __snake_case			:Dict          =						config_and_inputs[0]
            __snake_case			:Optional[Any]          =						UMTaForConditionalGeneration(a__    ).eval()
            model.to(a__    )

            __snake_case			:str          =						{
                """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()    ):
                 __snake_case			:Union[str, Any]          =						{name: mask}
                 # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                 if name == "head_mask":
                      __snake_case			:Tuple          =						torch.ones(
                          config.num_decoder_layers			,						config.num_heads			,						device=a__    )

                 __snake_case			:List[str]          =						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
                 __snake_case			:List[str]          =						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 					__lowercase						(							self    ) ->      str:






            '''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 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:str          =						UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""			,						return_dict=a__    ).to(a__    )
            __snake_case			:str          =						AutoTokenizer.from_pretrained("""google/umt5-small"""			,						use_fast=a__			,						legacy=a__    )
            __snake_case			:List[Any]          =						[
                """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>.""",
            ]
            __snake_case			:Any          =						tokenizer(a__			,						return_tensors="""pt"""			,						padding=a__    ).input_ids
            # fmt: off
            __snake_case			:str          =						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__    )

            __snake_case			:int          =						model.generate(input_ids.to(a__    )    )
            __snake_case			:str          =						[
                """<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>""",
            ]
            __snake_case			:str          =						tokenizer.batch_decode(a__    )
            self.assertEqual(a__			,						a__    )










import os






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]          =						os.path.dirname(os.path.realpath(snake_case__  )  )
     __snake_case			:Union[str, Any]          =						os.path.join(snake_case__	,"""triangle.txt"""  )

     with open(snake_case__  ) as f:
          __snake_case			:int          =						f.readlines()

     __snake_case			:int          =						[]
     for line in triangle:
          __snake_case			:List[Any]          =						[]
          for number in line.strip().split(""" """  ):
               numbers_from_line.append(int(snake_case__  )  )
          a.append(snake_case__  )

     for i in range(1	,len(snake_case__  )  ):
          for j in range(len(a[i]  )  ):
               __snake_case			:Union[str, Any]          =						a[i - 1][j] if j != len(a[i - 1]  ) else 0
               __snake_case			:Dict          =						a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(snake_case__	,snake_case__  )
     return max(a[-1]  )


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


