

"""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        _lowercase					:







     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_=0.0_0_2 ,						UpperCamelCase_=1 ,						UpperCamelCase_=0 ,						UpperCamelCase_=0 ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						):
      __magic_name__     =      parent
      __magic_name__     =      batch_size
      __magic_name__     =      encoder_seq_length
      __magic_name__     =      decoder_seq_length
      # For common tests
      __magic_name__     =      self.decoder_seq_length
      __magic_name__     =      is_training
      __magic_name__     =      use_attention_mask
      __magic_name__     =      use_labels
      __magic_name__     =      vocab_size
      __magic_name__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      num_attention_heads
      __magic_name__     =      d_ff
      __magic_name__     =      relative_attention_num_buckets
      __magic_name__     =      dropout_rate
      __magic_name__     =      initializer_factor
      __magic_name__     =      eos_token_id
      __magic_name__     =      pad_token_id
      __magic_name__     =      decoder_start_token_id
      __magic_name__     =      None
      __magic_name__     =      decoder_layers







     def 	lowerCAmelCase__    (      self       ):
      return TaConfig.from_pretrained('''google/umt5-base'''       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						):
      if attention_mask is None:
       __magic_name__     =      input_ids.ne(config.pad_token_id       )
      if decoder_attention_mask is None:
       __magic_name__     =      decoder_input_ids.ne(config.pad_token_id       )
      if head_mask is None:
       __magic_name__     =      torch.ones(config.num_hidden_layers ,						config.num_attention_heads ,						device=UpperCamelCase_       )
      if decoder_head_mask is None:
       __magic_name__     =      torch.ones(config.num_decoder_layers ,						config.num_attention_heads ,						device=UpperCamelCase_       )
      if cross_attn_head_mask is None:
       __magic_name__     =      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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ids_tensor([self.batch_size, self.encoder_seq_length] ,						self.vocab_size       )
      __magic_name__     =      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
      __magic_name__     =      input_ids.clamp(self.pad_token_id + 1       )
      __magic_name__     =      decoder_input_ids.clamp(self.pad_token_id + 1       )

      __magic_name__     =      self.get_config()
      __magic_name__     =      config.num_attention_heads
      __magic_name__     =      self.prepare_inputs_dict(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
      return config, input_dict







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      self.prepare_config_and_inputs()
      return config, inputs_dict







     def 	lowerCAmelCase__    (      self       ):
      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 	lowerCAmelCase__    (      self       ):
      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 ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						):
      __magic_name__     =      UMTaModel(config=UpperCamelCase_       )
      model.to(UpperCamelCase_       )
      model.eval()
      __magic_name__     =      model(
          input_ids=UpperCamelCase_ ,						decoder_input_ids=UpperCamelCase_ ,						attention_mask=UpperCamelCase_ ,						decoder_attention_mask=UpperCamelCase_ ,						)
      __magic_name__     =      model(input_ids=UpperCamelCase_ ,						decoder_input_ids=UpperCamelCase_       )
      __magic_name__     =      result.last_hidden_state
      __magic_name__     =      result.past_key_values
      __magic_name__     =      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 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						):
      __magic_name__     =      UMTaModel(config=UpperCamelCase_       ).get_decoder().to(UpperCamelCase_       ).eval()
      # first forward pass
      __magic_name__     =      model(UpperCamelCase_ ,						use_cache=UpperCamelCase_       )
      __magic_name__     =      model(UpperCamelCase_       )
      __magic_name__     =      model(UpperCamelCase_ ,						use_cache=UpperCamelCase_       )

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

      __magic_name__   ,						__magic_name__     =      outputs.to_tuple()

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

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

      __magic_name__     =      model(UpperCamelCase_       )['''last_hidden_state''']
      __magic_name__     =      model(UpperCamelCase_ ,						past_key_values=UpperCamelCase_       )['''last_hidden_state''']

      # select random slice
      __magic_name__     =      ids_tensor((1,) ,						output_from_past.shape[-1]       ).item()
      __magic_name__     =      output_from_no_past[:, -1, random_slice_idx].detach()
      __magic_name__     =      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 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						):
      __magic_name__     =      UMTaModel(config=UpperCamelCase_       ).to(UpperCamelCase_       ).half().eval()
      __magic_name__     =      model(**UpperCamelCase_       )['''last_hidden_state''']
      self.parent.assertFalse(torch.isnan(UpperCamelCase_       ).any().item()       )


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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      UMTaModelTester(self       )







     @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''       )
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      __magic_name__     =      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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      __magic_name__     =      config_and_inputs[0]
      __magic_name__     =      UMTaForConditionalGeneration(UpperCamelCase_       ).eval()
      model.to(UpperCamelCase_       )

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

       __magic_name__     =      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
       __magic_name__     =      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       ):
      pass


@require_torch
@require_sentencepiece
@require_tokenizers
class        _lowercase					(     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       ):
      __magic_name__     =      UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''' ,						return_dict=UpperCamelCase_       ).to(UpperCamelCase_       )
      __magic_name__     =      AutoTokenizer.from_pretrained('''google/umt5-small''' ,						use_fast=UpperCamelCase_ ,						legacy=UpperCamelCase_       )
      __magic_name__     =      [
          '''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>.''',
      ]
      __magic_name__     =      tokenizer(UpperCamelCase_ ,						return_tensors='''pt''' ,						padding=UpperCamelCase_       ).input_ids
      # fmt: off
      __magic_name__     =      torch.tensor(
          [
              [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
              [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
          ]       )
      # fmt: on
      torch.testing.assert_allclose(UpperCamelCase_ ,						UpperCamelCase_       )

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




"""simple docstring"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       FunnelTokenizer
     _lowerCamelCase     =       FunnelTokenizerFast
     _lowerCamelCase     =       True
     _lowerCamelCase     =       True







     def 	lowerCAmelCase__    (      self       ):
      super().setUp()

      __magic_name__     =      [
          '''<unk>''',
          '''<cls>''',
          '''<sep>''',
          '''want''',
          '''##want''',
          '''##ed''',
          '''wa''',
          '''un''',
          '''runn''',
          '''##ing''',
          ''',''',
          '''low''',
          '''lowest''',
      ]
      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return FunnelTokenizer.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return FunnelTokenizerFast.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      '''UNwant\u00E9d,running'''
      __magic_name__     =      '''unwanted, running'''
      return input_text, output_text







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.tokenizer_class(self.vocab_file       )

      __magic_name__     =      tokenizer.tokenize('''UNwant\u00E9d,running'''       )
      self.assertListEqual(UpperCamelCase_ ,						['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']       )
      self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_       ) ,						[7, 4, 5, 10, 8, 9]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizers(do_lower_case=UpperCamelCase_       )
      for tokenizer in tokenizers:
       __magic_name__     =      tokenizer('''UNwant\u00E9d,running'''       )
       __magic_name__     =      len(inputs['''input_ids''']       ) - 1
       self.assertListEqual(inputs['''token_type_ids'''] ,						[2] + [0] * sentence_len       )

       __magic_name__     =      tokenizer('''UNwant\u00E9d,running''' ,						'''UNwant\u00E9d,running'''       )
       self.assertListEqual(inputs['''token_type_ids'''] ,						[2] + [0] * sentence_len + [1] * sentence_len       )

