





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        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       List[str]	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       List[str]=99	,			UpperCAmelCase_    :       List[Any]=13	,			UpperCAmelCase_    :       Tuple=7	,			UpperCAmelCase_    :       Tuple=9	,			UpperCAmelCase_    :       Any=True	,			UpperCAmelCase_    :       Optional[Any]=True	,			UpperCAmelCase_    :       Tuple=False	,			UpperCAmelCase_    :       str=32	,			UpperCAmelCase_    :       Union[str, Any]=5	,			UpperCAmelCase_    :       List[Any]=4	,			UpperCAmelCase_    :       Optional[Any]=37	,			UpperCAmelCase_    :       List[Any]=8	,			UpperCAmelCase_    :       Dict=0.1	,			UpperCAmelCase_    :       List[Any]=0.002	,			UpperCAmelCase_    :       str=1	,			UpperCAmelCase_    :       List[Any]=0	,			UpperCAmelCase_    :       str=0	,			UpperCAmelCase_    :       List[Any]=None	,			UpperCAmelCase_    :       Any=None	,			)	->Dict:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->int:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Optional[Any]=None	,			UpperCAmelCase_    :       Optional[int]=None	,			UpperCAmelCase_    :       Tuple=None	,			UpperCAmelCase_    :       Optional[Any]=None	,			UpperCAmelCase_    :       int=None	,			)	->int:



   '''simple docstring'''



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



   '''simple docstring'''



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

   lowerCamelCase__:				List[Any]        =self.get_config()
   lowerCamelCase__:				int        =config.num_attention_heads
   lowerCamelCase__:				int        =self.prepare_inputs_dict(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)
   return config, input_dict

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				str        =self.prepare_config_and_inputs()
   return config, inputs_dict

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



   '''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 							SCREAMING_SNAKE_CASE_ (self    :       str)	->int:



   '''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 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       List[str]	,			)	->Optional[Any]:



   '''simple docstring'''



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



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =UMTaModel(config=UpperCAmelCase_).get_decoder().to(UpperCAmelCase_).eval()
   # first forward pass
   lowerCamelCase__:				Any        =model(UpperCAmelCase_	,			use_cache=UpperCAmelCase_)
   lowerCamelCase__:				Dict        =model(UpperCAmelCase_)
   lowerCamelCase__:				Dict        =model(UpperCAmelCase_	,			use_cache=UpperCAmelCase_)

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

   lowerCamelCase__  , lowerCamelCase__:				Dict        =outputs.to_tuple()

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

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

   lowerCamelCase__:				Union[str, Any]        =model(UpperCAmelCase_)["last_hidden_state"]
   lowerCamelCase__:				List[Any]        =model(UpperCAmelCase_	,			past_key_values=UpperCAmelCase_)["last_hidden_state"]

   # select random slice
   lowerCamelCase__:				Any        =ids_tensor((1,)	,			output_from_past.shape[-1]).item()
   lowerCamelCase__:				Any        =output_from_no_past[:, -1, random_slice_idx].detach()
   lowerCamelCase__:				Union[str, Any]        =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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       str	,			)	->int:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =UMTaModel(config=UpperCAmelCase_).to(UpperCAmelCase_).half().eval()
   lowerCamelCase__:				Optional[Any]        =model(**UpperCAmelCase_)["last_hidden_state"]
   self.parent.assertFalse(torch.isnan(UpperCAmelCase_).any().item())



@require_torch
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =UMTaModelTester(self)

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



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self.model_tester.prepare_config_and_inputs()
   lowerCamelCase__:				List[str]        =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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Optional[Any]:



   '''simple docstring'''



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

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

     lowerCamelCase__:				Union[str, Any]        =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__:				int        =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 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Any:



   '''simple docstring'''



   pass



@require_torch
@require_sentencepiece
@require_tokenizers
class        _SCREAMING_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 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Any:



   '''simple docstring'''



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

   lowerCamelCase__:				Any        =model.generate(input_ids.to(UpperCAmelCase_))
   lowerCamelCase__:				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>",
   ]
   lowerCamelCase__:				List[Any]        =tokenizer.batch_decode(UpperCAmelCase_)
   self.assertEqual(UpperCAmelCase_	,			UpperCAmelCase_)








def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Tuple:







  """simple docstring"""





  assert x is not None
  assert y is not None

  lowerCamelCase__:				Any        =len(__a		)
  lowerCamelCase__:				int        =len(__a		)

  # declaring the array for storing the dp values
  lowerCamelCase__:				List[Any]        =[[0] * (n + 1) for _ in range(m + 1		)]  # noqa: E741

  for i in range(1     ,   m + 1		):
    for j in range(1     ,   n + 1		):
      lowerCamelCase__:				str        =1 if x[i - 1] == y[j - 1] else 0

      lowerCamelCase__:				str        =max(l[i - 1][j]     ,   l[i][j - 1]     ,   l[i - 1][j - 1] + match		)

  lowerCamelCase__:				Any        =""
  lowerCamelCase__  , lowerCamelCase__:				str        =m, n
  while i > 0 and j > 0:
    lowerCamelCase__:				Union[str, Any]        =1 if x[i - 1] == y[j - 1] else 0

    if l[i][j] == l[i - 1][j - 1] + match:
      if match == 1:
        lowerCamelCase__:				Any        =x[i - 1] + seq
      i -= 1
      j -= 1
    elif l[i][j] == l[i - 1][j]:
      i -= 1
    else:
      j -= 1

  return l[m][n], seq


if __name__ == "__main__":
     __A     =    "AGGTAB"
     __A     =    "GXTXAYB"
     __A     =    4
     __A     =    "GTAB"

     __A ,     __A     =    longest_common_subsequence(a, b)
     print("len =", ln, ", sub-sequence =", subseq)
     import doctest

     doctest.testmod()

