






"""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 						snake_case  :





    def __init__(							self     : Any    ,		A     : List[str]    ,		A     : str=9_9    ,		A     : str=1_3    ,		A     : List[str]=7    ,		A     : List[Any]=9    ,		A     : Dict=True    ,		A     : Optional[int]=True    ,		A     : Any=False    ,		A     : Tuple=3_2    ,		A     : int=5    ,		A     : str=4    ,		A     : List[str]=3_7    ,		A     : List[Any]=8    ,		A     : Dict=0.1    ,		A     : Dict=0.0_02    ,		A     : Dict=1    ,		A     : List[Any]=0    ,		A     : Any=0    ,		A     : Union[str, Any]=None    ,		A     : Dict=None    ,		):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : Union[str, Any]    ,		A     : Optional[Any]    ,		A     : Optional[int]    ,		A     : Dict=None    ,		A     : List[Any]=None    ,		A     : Dict=None    ,		A     : Optional[Any]=None    ,		A     : str=None    ,		):


      '''simple docstring'''




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


      '''simple docstring'''




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

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





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      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     lowerCamelCase__				(							self     : Optional[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     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Union[str, Any]    ,		A     : List[Any]    ,		A     : int    ,		A     : Tuple    ,		A     : Optional[Any]    ,		A     : int    ,		):


      '''simple docstring'''




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


      '''simple docstring'''




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

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

      a,					a					:      str    =       outputs.to_tuple()

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

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

      a					:      Dict    =       model(A      )['last_hidden_state']
      a					:      int    =       model(A    ,		past_key_values=A      )['last_hidden_state']

      # select random slice
      a					:      List[str]    =       ids_tensor((1,)    ,		output_from_past.shape[-1]      ).item()
      a					:      Any    =       output_from_no_past[:, -1, random_slice_idx].detach()
      a					:      Optional[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     lowerCamelCase__				(							self     : List[Any]    ,		A     : str    ,		A     : int    ,		):


      '''simple docstring'''




      a					:      List[Any]    =       UMTaModel(config=A      ).to(A      ).half().eval()
      a					:      Optional[int]    =       model(**A      )['last_hidden_state']
      self.parent.assertFalse(torch.isnan(A      ).any().item()      )






@require_torch
class 						snake_case  (  UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    __magic_name__   					=					(UMTaForConditionalGeneration,) if is_torch_available() else ()
    __magic_name__   					=					(
        {
            '''conversational''': UMTaForConditionalGeneration,
            '''feature-extraction''': UMTaModel,
            '''summarization''': UMTaForConditionalGeneration,
            '''text2text-generation''': UMTaForConditionalGeneration,
            '''translation''': UMTaForConditionalGeneration,
            '''question-answering''': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    __magic_name__   					=					True
    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					True
    __magic_name__   					=					True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    __magic_name__   					=					[0.8, 0.9]





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Dict    =       UMTaModelTester(self      )





    @unittest.skip('Test has a segmentation fault on torch 1.8.0'      )
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      List[str]    =       self.model_tester.prepare_config_and_inputs()
      a					:      int    =       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     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      Dict    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*A      )





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




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

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

        a					:      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
        a					:      Any    =       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     lowerCamelCase__				(							self     : Tuple      ):


      '''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     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      int    =       UMTaForConditionalGeneration.from_pretrained('google/umt5-small'    ,		return_dict=A      ).to(A      )
      a					:      Optional[Any]    =       AutoTokenizer.from_pretrained('google/umt5-small'    ,		use_fast=A    ,		legacy=A      )
      a					:      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>.',
      ]
      a					:      Union[str, Any]    =       tokenizer(A    ,		return_tensors='pt'    ,		padding=A      ).input_ids
      # fmt: off
      a					:      List[Any]    =       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(A    ,		A      )

      a					:      List[Any]    =       model.generate(input_ids.to(A      )      )
      a					:      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>',
      ]
      a					:      List[Any]    =       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




