






"""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        a     :







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=9_9							,		_lowerCamelCase=1_3							,		_lowerCamelCase=7							,		_lowerCamelCase=9							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=False							,		_lowerCamelCase=3_2							,		_lowerCamelCase=5							,		_lowerCamelCase=4							,		_lowerCamelCase=3_7							,		_lowerCamelCase=8							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.0_0_2							,		_lowerCamelCase=1							,		_lowerCamelCase=0							,		_lowerCamelCase=0							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		):
      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 			UpperCamelCase_      (			self       ):
      return TaConfig.from_pretrained('google/umt5-base'       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		):
      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 			UpperCamelCase_      (			self       ):
      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 			UpperCamelCase_      (			self       ):
      lowercase    ,     lowercase							    =       self.prepare_config_and_inputs()
      return config, inputs_dict







    def 			UpperCamelCase_      (			self       ):
      return TaConfig(
          vocab_size=1_6_6							,		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       ):
      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							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      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        a     (   a_,				a_,				a_,				unittest.TestCase	):
    UpperCAmelCase_ :     List[str]	     =(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    UpperCAmelCase_ :     Optional[int]	     =(UMTaForConditionalGeneration,) if is_torch_available() else ()
    UpperCAmelCase_ :     List[str]	     =(
        {
            "conversational": UMTaForConditionalGeneration,
            "feature-extraction": UMTaModel,
            "summarization": UMTaForConditionalGeneration,
            "text2text-generation": UMTaForConditionalGeneration,
            "translation": UMTaForConditionalGeneration,
            "question-answering": UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    UpperCAmelCase_ :     Optional[int]	     =True
    UpperCAmelCase_ :     Dict	     =False
    UpperCAmelCase_ :     List[Any]	     =False
    UpperCAmelCase_ :     List[str]	     =True
    UpperCAmelCase_ :     List[str]	     =True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    UpperCAmelCase_ :     Any	     =[0.8, 0.9]







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       UMTaModelTester(self       )







    @unittest.skip('Test has a segmentation fault on torch 1.8.0'       )
    def 			UpperCamelCase_      (			self       ):
      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 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      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 			UpperCamelCase_      (			self       ):
      pass


@require_torch
@require_sentencepiece
@require_tokenizers
class        a     (   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       ):
      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(
          [
              [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
              [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 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       )










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int = 1_00    ):





  '''simple docstring'''





  lowercase							    =       0
  lowercase							    =       0
  for i in range(1		,					n + 1    ):
    sum_of_squares += i**2
    sum_of_ints += i
  return sum_of_ints**2 - sum_of_squares


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


