

"""simple docstring"""


from __future__ import annotations




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				list[int]:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0
      SCREAMING_SNAKE_CASE__   :      Any      		=	len(__lowerCAmelCase							) - 1

      while i < j:
            if nums[i] + nums[j] == target:
                  return [i, j]
            elif nums[i] + nums[j] < target:
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	i + 1
            else:
                  SCREAMING_SNAKE_CASE__   :      Any      		=	j - 1

      return []


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(f'{two_pointer([2, 7, 11, 15], 9) = }')







"""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							:






     '''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=0.002	,    _a=1	,    _a=0	,    _a=0	,    _a=None	,    _a=None	,    )  ->      Any:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


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

     def 					_a			(						self	,    _a	,    _a	,    _a	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    )  ->      Any:


           """simple docstring"""


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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ids_tensor([self.batch_size, self.encoder_seq_length]	,    self.vocab_size  )
           SCREAMING_SNAKE_CASE__   :      Optional[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
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	input_ids.clamp(self.pad_token_id + 1  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	decoder_input_ids.clamp(self.pad_token_id + 1  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	config.num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.prepare_inputs_dict(_a	,    _a	,    _a  )
           return config, input_dict

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


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

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


           """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[Any]:


           """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	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    )  ->      Optional[Any]:


           """simple docstring"""


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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	UMTaModel(config=_a  ).get_decoder().to(_a  ).eval()
           # first forward pass
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a	,    use_cache=_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model(_a	,    use_cache=_a  )

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

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	outputs.to_tuple()

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

           # append to next input_ids and
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.cat([input_ids, next_tokens]	,    dim=-1  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	model(_a	,    past_key_values=_a  )["""last_hidden_state"""]

           # select random slice
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	ids_tensor((1,)	,    output_from_past.shape[-1]  ).item()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	output_from_no_past[:, -1, random_slice_idx].detach()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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 					_a			(						self	,    _a	,    _a	,    )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	UMTaModel(config=_a  ).to(_a  ).half().eval()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model(**_a  )["""last_hidden_state"""]
           self.parent.assertFalse(torch.isnan(_a  ).any().item()  )





@require_torch
class      __a							(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	unittest.TestCase):






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

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	UMTaModelTester(self  )

     @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""  )
     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.model_tester.prepare_config_and_inputs()
           SCREAMING_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 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model_fpaa_forward(*_a  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.model_tester.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	config_and_inputs[0]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	UMTaForConditionalGeneration(_a  ).eval()
           model.to(_a  )

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

                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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
                 SCREAMING_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 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           pass





@require_torch
@require_sentencepiece
@require_tokenizers
class      __a							(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 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""	,    return_dict=_a  ).to(_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	AutoTokenizer.from_pretrained("""google/umt5-small"""	,    use_fast=_a	,    legacy=_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[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>.""",
           ]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer(_a	,    return_tensors="""pt"""	,    padding=_a  ).input_ids
           # fmt: off
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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(_a	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model.generate(input_ids.to(_a  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	[
               """<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>""",
           ]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.batch_decode(_a  )
           self.assertEqual(_a	,    _a  )




