


"""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     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=99    ,				__UpperCAmelCase=13    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=9    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=32    ,				__UpperCAmelCase=5    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase=37    ,				__UpperCAmelCase=8    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=0.002    ,				__UpperCAmelCase=1    ,				__UpperCAmelCase=0    ,				__UpperCAmelCase=0    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				)       ->							Union[str, Any]:
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				encoder_seq_length
          _a    	=				decoder_seq_length
          # For common tests
          _a    	=				self.decoder_seq_length
          _a    	=				is_training
          _a    	=				use_attention_mask
          _a    	=				use_labels
          _a    	=				vocab_size
          _a    	=				hidden_size
          _a    	=				num_hidden_layers
          _a    	=				num_attention_heads
          _a    	=				d_ff
          _a    	=				relative_attention_num_buckets
          _a    	=				dropout_rate
          _a    	=				initializer_factor
          _a    	=				eos_token_id
          _a    	=				pad_token_id
          _a    	=				decoder_start_token_id
          _a    	=				None
          _a    	=				decoder_layers


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          return TaConfig.from_pretrained('''google/umt5-base'''							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				)       ->							Optional[Any]:
          if attention_mask is None:
               _a    	=				input_ids.ne(config.pad_token_id							)
          if decoder_attention_mask is None:
               _a    	=				decoder_input_ids.ne(config.pad_token_id							)
          if head_mask is None:
               _a    	=				torch.ones(config.num_hidden_layers    ,				config.num_attention_heads    ,				device=A_							)
          if decoder_head_mask is None:
               _a    	=				torch.ones(config.num_decoder_layers    ,				config.num_attention_heads    ,				device=A_							)
          if cross_attn_head_mask is None:
               _a    	=				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 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				ids_tensor([self.batch_size, self.encoder_seq_length]    ,				self.vocab_size							)
          _a    	=				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
          _a    	=				input_ids.clamp(self.pad_token_id + 1							)
          _a    	=				decoder_input_ids.clamp(self.pad_token_id + 1							)

          _a    	=				self.get_config()
          _a    	=				config.num_attention_heads
          _a    	=				self.prepare_inputs_dict(A_    ,				A_    ,				A_							)
          return config, input_dict


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a	,      _a    	=				self.prepare_config_and_inputs()
          return config, inputs_dict


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          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 							_UpperCAmelCase      (      self							)       ->							Tuple:
          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    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							Any:
          _a    	=				UMTaModel(config=A_							)
          model.to(A_							)
          model.eval()
          _a    	=				model(
              input_ids=A_    ,				decoder_input_ids=A_    ,				attention_mask=A_    ,				decoder_attention_mask=A_    ,				)
          _a    	=				model(input_ids=A_    ,				decoder_input_ids=A_							)
          _a    	=				result.last_hidden_state
          _a    	=				result.past_key_values
          _a    	=				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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							Tuple:
          _a    	=				UMTaModel(config=A_							).get_decoder().to(A_							).eval()
          # first forward pass
          _a    	=				model(A_    ,				use_cache=A_							)
          _a    	=				model(A_							)
          _a    	=				model(A_    ,				use_cache=A_							)

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

          _a	,      _a    	=				outputs.to_tuple()

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

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

          _a    	=				model(A_							)['''last_hidden_state''']
          _a    	=				model(A_    ,				past_key_values=A_							)['''last_hidden_state''']

          # select random slice
          _a    	=				ids_tensor((1,)    ,				output_from_past.shape[-1]							).item()
          _a    	=				output_from_no_past[:, -1, random_slice_idx].detach()
          _a    	=				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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							Optional[Any]:
          _a    	=				UMTaModel(config=A_							).to(A_							).half().eval()
          _a    	=				model(**A_							)['''last_hidden_state''']
          self.parent.assertFalse(torch.isnan(A_							).any().item()							)







@require_torch
class     __lowerCamelCase  (					_lowercase  ,					_lowercase  ,					_lowercase  ,					unittest.TestCase				):

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


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				UMTaModelTester(self							)


     @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''							)
     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          _a    	=				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 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model_fpaa_forward(*A_							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
          _a    	=				self.model_tester.prepare_config_and_inputs()
          _a    	=				config_and_inputs[0]
          _a    	=				UMTaForConditionalGeneration(A_							).eval()
          model.to(A_							)

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

               _a    	=				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
               _a    	=				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							)       ->							str:
          pass







@require_torch
@require_sentencepiece
@require_tokenizers
class     __lowerCamelCase  (					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 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''    ,				return_dict=A_							).to(A_							)
          _a    	=				AutoTokenizer.from_pretrained('''google/umt5-small'''    ,				use_fast=A_    ,				legacy=A_							)
          _a    	=				[
              '''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>.''',
          ]
          _a    	=				tokenizer(A_    ,				return_tensors='''pt'''    ,				padding=A_							).input_ids
          # fmt: off
          _a    	=				torch.tensor(
              [
                  [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                  [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
              ]							)
          # fmt: on
          torch.testing.assert_allclose(A_    ,				A_							)

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



"""simple docstring"""






from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels