

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 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase=99				,  lowerCAmelCase=13				,  lowerCAmelCase=7				,  lowerCAmelCase=9				,  lowerCAmelCase=True				,  lowerCAmelCase=True				,  lowerCAmelCase=False				,  lowerCAmelCase=32				,  lowerCAmelCase=5				,  lowerCAmelCase=4				,  lowerCAmelCase=37				,  lowerCAmelCase=8				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.002				,  lowerCAmelCase=1				,  lowerCAmelCase=0				,  lowerCAmelCase=0				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  )    ->					Any:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






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




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  )    ->					int:
        '''simple docstring'''






        if attention_mask is None:
              _lowercase      			=input_ids.ne(config.pad_token_id   )
        if decoder_attention_mask is None:
              _lowercase      			=decoder_input_ids.ne(config.pad_token_id   )
        if head_mask is None:
              _lowercase      			=torch.ones(config.num_hidden_layers				,  config.num_attention_heads				,  device=lowerCAmelCase   )
        if decoder_head_mask is None:
              _lowercase      			=torch.ones(config.num_decoder_layers				,  config.num_attention_heads				,  device=lowerCAmelCase   )
        if cross_attn_head_mask is None:
              _lowercase      			=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     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=ids_tensor([self.batch_size, self.encoder_seq_length]				,  self.vocab_size   )
        _lowercase      			=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
        _lowercase      			=input_ids.clamp(self.pad_token_id + 1   )
        _lowercase      			=decoder_input_ids.clamp(self.pad_token_id + 1   )

        _lowercase      			=self.get_config()
        _lowercase      			=config.num_attention_heads
        _lowercase      			=self.prepare_inputs_dict(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )
        return config, input_dict




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=self.prepare_config_and_inputs()
        return config, inputs_dict




  def     A__	(							self   )    ->					Optional[int]:
        '''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     A__	(							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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  )    ->					int:
        '''simple docstring'''






        _lowercase      			=UMTaModel(config=lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()
        _lowercase      			=model(
            input_ids=lowerCAmelCase				,  decoder_input_ids=lowerCAmelCase				,  attention_mask=lowerCAmelCase				,  decoder_attention_mask=lowerCAmelCase				,  )
        _lowercase      			=model(input_ids=lowerCAmelCase				,  decoder_input_ids=lowerCAmelCase   )
        _lowercase      			=result.last_hidden_state
        _lowercase      			=result.past_key_values
        _lowercase      			=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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  )    ->					str:
        '''simple docstring'''






        _lowercase      			=UMTaModel(config=lowerCAmelCase   ).get_decoder().to(lowerCAmelCase   ).eval()
        # first forward pass
        _lowercase      			=model(lowerCAmelCase				,  use_cache=lowerCAmelCase   )
        _lowercase      			=model(lowerCAmelCase   )
        _lowercase      			=model(lowerCAmelCase				,  use_cache=lowerCAmelCase   )

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

        _lowercase			,			_lowercase      			=outputs.to_tuple()

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

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

        _lowercase      			=model(lowerCAmelCase   )['last_hidden_state']
        _lowercase      			=model(lowerCAmelCase				,  past_key_values=lowerCAmelCase   )['last_hidden_state']

        # select random slice
        _lowercase      			=ids_tensor((1,)				,  output_from_past.shape[-1]   ).item()
        _lowercase      			=output_from_no_past[:, -1, random_slice_idx].detach()
        _lowercase      			=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     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=UMTaModel(config=lowerCAmelCase   ).to(lowerCAmelCase   ).half().eval()
        _lowercase      			=model(**lowerCAmelCase   )['last_hidden_state']
        self.parent.assertFalse(torch.isnan(lowerCAmelCase   ).any().item()   )





@require_torch
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     unittest.TestCase				):
  _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   )    ->					str:
        '''simple docstring'''






        _lowercase      			=UMTaModelTester(self   )




  @unittest.skip('Test has a segmentation fault on torch 1.8.0'   )
  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=self.model_tester.prepare_config_and_inputs()
        _lowercase      			=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     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase   )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=['encoder_attentions', 'decoder_attentions', 'cross_attentions']
        _lowercase      			=self.model_tester.prepare_config_and_inputs()
        _lowercase      			=config_and_inputs[0]
        _lowercase      			=UMTaForConditionalGeneration(lowerCAmelCase   ).eval()
        model.to(lowerCAmelCase   )

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

              _lowercase      			=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
              _lowercase      			=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]:
        '''simple docstring'''






        pass





@require_torch
@require_sentencepiece
@require_tokenizers
class 					__lowerCAmelCase	(   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     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






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

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





import sys

lowercase_   							=  (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)
def 	a (     A__      :  str = N	)       -> int:





      """simple docstring"""



      _lowercase      			=-sys.maxsize - 1
      for i in range(len(A__	) - 12	):
            _lowercase      			=1
            for j in range(13	):
                  product *= int(n[i + j]	)
            if product > largest_product:
                  _lowercase      			=product
      return largest_product


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


