
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 :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=9       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=5       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_=8       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.0_0_2       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	):


   '''simple docstring'''




   if attention_mask is None:
     lowercase__	: int     		=   input_ids.ne(config.pad_token_id)
   if decoder_attention_mask is None:
     lowercase__	: List[Any]     		=   decoder_input_ids.ne(config.pad_token_id)
   if head_mask is None:
     lowercase__	: Optional[int]     		=   torch.ones(config.num_hidden_layers       ,	config.num_attention_heads       ,	device=SCREAMING_SNAKE_CASE_)
   if decoder_head_mask is None:
     lowercase__	: Any     		=   torch.ones(config.num_decoder_layers       ,	config.num_attention_heads       ,	device=SCREAMING_SNAKE_CASE_)
   if cross_attn_head_mask is None:
     lowercase__	: int     		=   torch.ones(
         config.num_decoder_layers       ,	config.num_attention_heads       ,	device=SCREAMING_SNAKE_CASE_)
   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):


   '''simple docstring'''




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

   lowercase__	: List[str]     		=   self.get_config()
   lowercase__	: List[Any]     		=   config.num_attention_heads
   lowercase__	: Dict     		=   self.prepare_inputs_dict(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   return config, input_dict


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Union[str, Any]     		=   self.prepare_config_and_inputs()
   return config, inputs_dict


 def   lowercase__  (       self):


   '''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):


   '''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       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   UMTaModel(config=SCREAMING_SNAKE_CASE_)
   model.to(SCREAMING_SNAKE_CASE_)
   model.eval()
   lowercase__	: Union[str, Any]     		=   model(
       input_ids=SCREAMING_SNAKE_CASE_       ,	decoder_input_ids=SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	decoder_attention_mask=SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: int     		=   model(input_ids=SCREAMING_SNAKE_CASE_       ,	decoder_input_ids=SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   result.last_hidden_state
   lowercase__	: Optional[int]     		=   result.past_key_values
   lowercase__	: Union[str, 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(SCREAMING_SNAKE_CASE_)       ,	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       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   UMTaModel(config=SCREAMING_SNAKE_CASE_).get_decoder().to(SCREAMING_SNAKE_CASE_).eval()
   # first forward pass
   lowercase__	: Any     		=   model(SCREAMING_SNAKE_CASE_       ,	use_cache=SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   model(SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	use_cache=SCREAMING_SNAKE_CASE_)

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

   lowercase__		,     lowercase__	: Tuple     		=   outputs.to_tuple()

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

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

   lowercase__	: Any     		=   model(SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]
   lowercase__	: List[str]     		=   model(SCREAMING_SNAKE_CASE_       ,	past_key_values=SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]

   # select random slice
   lowercase__	: List[str]     		=   ids_tensor((1,)       ,	output_from_past.shape[-1]).item()
   lowercase__	: Any     		=   output_from_no_past[:, -1, random_slice_idx].detach()
   lowercase__	: Union[str, Any]     		=   output_from_past[:, 0, random_slice_idx].detach()

   # test that outputs are equal for slice
   self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	atol=1E-3))



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Dict     		=   UMTaModel(config=SCREAMING_SNAKE_CASE_).to(SCREAMING_SNAKE_CASE_).half().eval()
   lowercase__	: str     		=   model(**SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]
   self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE_).any().item())



@require_torch
class    _snake_case (						UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Any	   =      (
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 __lowerCAmelCase	:  str	   =      (UMTaForConditionalGeneration,) if is_torch_available() else ()
 __lowerCAmelCase	:  Dict	   =      (
     {
         'conversational': UMTaForConditionalGeneration,
         'feature-extraction': UMTaModel,
         'summarization': UMTaForConditionalGeneration,
         'text2text-generation': UMTaForConditionalGeneration,
         'translation': UMTaForConditionalGeneration,
         'question-answering': UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase	:  Any	   =      True
 __lowerCAmelCase	:  Optional[Any]	   =      False
 __lowerCAmelCase	:  Optional[int]	   =      False
 __lowerCAmelCase	:  List[str]	   =      True
 __lowerCAmelCase	:  Optional[int]	   =      True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 __lowerCAmelCase	:  Tuple	   =      [0.8, 0.9]


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   UMTaModelTester(self)


 @unittest.skip("""Test has a segmentation fault on torch 1.8.0""")
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.model_tester.prepare_config_and_inputs()
   lowercase__	: str     		=   UMTaModel(config_and_inputs[0]).to(SCREAMING_SNAKE_CASE_)
   with tempfile.TemporaryDirectory() as tmpdirname:
     torch.onnx.export(
         SCREAMING_SNAKE_CASE_       ,	(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])       ,	f'{tmpdirname}/t5_test.onnx'       ,	export_params=SCREAMING_SNAKE_CASE_       ,	opset_version=9       ,	input_names=["""input_ids""", """decoder_input_ids"""]       ,	)


 @unittest.skipIf(torch_device == """cpu"""       ,	"""Cant do half precision""")
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
   lowercase__	: Optional[int]     		=   self.model_tester.prepare_config_and_inputs()
   lowercase__	: int     		=   config_and_inputs[0]
   lowercase__	: Tuple     		=   UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE_).eval()
   model.to(SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[int]     		=   {
       """head_mask""": torch.zeros(config.num_layers       ,	config.num_heads       ,	device=SCREAMING_SNAKE_CASE_),
       """decoder_head_mask""": torch.zeros(config.num_decoder_layers       ,	config.num_heads       ,	device=SCREAMING_SNAKE_CASE_),
       """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers       ,	config.num_heads       ,	device=SCREAMING_SNAKE_CASE_),
   }

   for attn_name, (name, mask) in zip(SCREAMING_SNAKE_CASE_       ,	head_masking.items()):
     lowercase__	: str     		=   {name: mask}
     # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
     if name == "head_mask":
       lowercase__	: Optional[int]     		=   torch.ones(
           config.num_decoder_layers       ,	config.num_heads       ,	device=SCREAMING_SNAKE_CASE_)

     lowercase__	: Dict     		=   model.generate(
         config_and_inputs[1]["""input_ids"""]       ,	num_beams=1       ,	max_length=3       ,	output_attentions=SCREAMING_SNAKE_CASE_       ,	return_dict_in_generate=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_       ,	)
     # We check the state of decoder_attentions and cross_attentions just from the last step
     lowercase__	: 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   lowercase__  (       self):


   '''simple docstring'''




   pass



@require_torch
@require_sentencepiece
@require_tokenizers
class    _snake_case (						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   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: Optional[Any]     		=   model.generate(input_ids.to(SCREAMING_SNAKE_CASE_))
   lowercase__	: List[Any]     		=   [
       """<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__	: List[str]     		=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_)
   self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)








def 		UpperCamelCase						(   lowercase_ )		->						float:


  '''simple docstring'''






  if not nums:  # Makes sure that the list is not empty
    raise ValueError("""List is empty""" )

  lowercase__	: int     		=   sum(lowercase_ ) / len(lowercase_ )  # Calculate the average
  return sum(abs(x - average ) for x in nums ) / len(lowercase_ )


if __name__ == "__main__":
     import doctest

     doctest.testmod()






