





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__							:


      def __init__(self			: Dict							,     a__			: Any							,     a__			: Union[str, Any]=99							,     a__			: List[Any]=13							,     a__			: Optional[Any]=7							,     a__			: Union[str, Any]=9							,     a__			: List[Any]=True							,     a__			: Optional[int]=True							,     a__			: Any=False							,     a__			: str=32							,     a__			: Any=5							,     a__			: List[str]=4							,     a__			: Dict=37							,     a__			: List[Any]=8							,     a__			: int=0.1							,     a__			: List[str]=0.0_0_2							,     a__			: Optional[Any]=1							,     a__			: Union[str, Any]=0							,     a__			: Any=0							,     a__			: List[str]=None							,     a__			: List[Any]=None							,     ):





         """simple docstring"""



         __snake_case							=  parent
         __snake_case							=  batch_size
         __snake_case							=  encoder_seq_length
         __snake_case							=  decoder_seq_length
         # For common tests
         __snake_case							=  self.decoder_seq_length
         __snake_case							=  is_training
         __snake_case							=  use_attention_mask
         __snake_case							=  use_labels
         __snake_case							=  vocab_size
         __snake_case							=  hidden_size
         __snake_case							=  num_hidden_layers
         __snake_case							=  num_attention_heads
         __snake_case							=  d_ff
         __snake_case							=  relative_attention_num_buckets
         __snake_case							=  dropout_rate
         __snake_case							=  initializer_factor
         __snake_case							=  eos_token_id
         __snake_case							=  pad_token_id
         __snake_case							=  decoder_start_token_id
         __snake_case							=  None
         __snake_case							=  decoder_layers


      def   a					(self			: Any ):





         """simple docstring"""



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


      def   a					(self			: Union[str, Any]							,     a__			: Optional[int]							,     a__			: Optional[int]							,     a__			: int							,     a__			: Dict=None							,     a__			: Dict=None							,     a__			: str=None							,     a__			: Any=None							,     a__			: Union[str, Any]=None							,     ):





         """simple docstring"""



         if attention_mask is None:
            __snake_case							=  input_ids.ne(config.pad_token_id )
         if decoder_attention_mask is None:
            __snake_case							=  decoder_input_ids.ne(config.pad_token_id )
         if head_mask is None:
            __snake_case							=  torch.ones(config.num_hidden_layers							,     config.num_attention_heads							,     device=_snake_case )
         if decoder_head_mask is None:
            __snake_case							=  torch.ones(config.num_decoder_layers							,     config.num_attention_heads							,     device=_snake_case )
         if cross_attn_head_mask is None:
            __snake_case							=  torch.ones(
                config.num_decoder_layers							,     config.num_attention_heads							,     device=_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   a					(self			: int ):





         """simple docstring"""



         __snake_case							=  ids_tensor([self.batch_size, self.encoder_seq_length]							,     self.vocab_size )
         __snake_case							=  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
         __snake_case							=  input_ids.clamp(self.pad_token_id + 1 )
         __snake_case							=  decoder_input_ids.clamp(self.pad_token_id + 1 )

         __snake_case							=  self.get_config()
         __snake_case							=  config.num_attention_heads
         __snake_case							=  self.prepare_inputs_dict(_snake_case							,     _snake_case							,     _snake_case )
         return config, input_dict


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





         """simple docstring"""



         __snake_case			,      __snake_case							=  self.prepare_config_and_inputs()
         return config, inputs_dict


      def   a					(self			: List[str] ):





         """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   a					(self			: Tuple ):





         """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   a					(self			: Optional[int]							,     a__			: List[str]							,     a__			: Any							,     a__			: List[Any]							,     a__			: Optional[int]							,     a__			: Optional[Any]							,     a__			: Optional[Any]							,     ):





         """simple docstring"""



         __snake_case							=  UMTaModel(config=_snake_case )
         model.to(_snake_case )
         model.eval()
         __snake_case							=  model(
             input_ids=_snake_case							,     decoder_input_ids=_snake_case							,     attention_mask=_snake_case							,     decoder_attention_mask=_snake_case							,     )
         __snake_case							=  model(input_ids=_snake_case							,     decoder_input_ids=_snake_case )
         __snake_case							=  result.last_hidden_state
         __snake_case							=  result.past_key_values
         __snake_case							=  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(_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   a					(self			: Optional[Any]							,     a__			: Optional[Any]							,     a__			: List[str]							,     a__			: List[str]							,     a__			: str							,     a__			: Optional[Any]							,     a__			: int							,     ):





         """simple docstring"""



         __snake_case							=  UMTaModel(config=_snake_case ).get_decoder().to(_snake_case ).eval()
         # first forward pass
         __snake_case							=  model(_snake_case							,     use_cache=_snake_case )
         __snake_case							=  model(_snake_case )
         __snake_case							=  model(_snake_case							,     use_cache=_snake_case )

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

         __snake_case			,      __snake_case							=  outputs.to_tuple()

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

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

         __snake_case							=  model(_snake_case )['''last_hidden_state''']
         __snake_case							=  model(_snake_case							,     past_key_values=_snake_case )['''last_hidden_state''']

         # select random slice
         __snake_case							=  ids_tensor((1,)							,     output_from_past.shape[-1] ).item()
         __snake_case							=  output_from_no_past[:, -1, random_slice_idx].detach()
         __snake_case							=  output_from_past[:, 0, random_slice_idx].detach()

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


      def   a					(self			: str							,     a__			: Any							,     a__			: str							,     ):





         """simple docstring"""



         __snake_case							=  UMTaModel(config=_snake_case ).to(_snake_case ).half().eval()
         __snake_case							=  model(**_snake_case )['''last_hidden_state''']
         self.parent.assertFalse(torch.isnan(_snake_case ).any().item() )






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


      def   a					(self			: str ):





         """simple docstring"""



         __snake_case							=  UMTaModelTester(self )


      @unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
      def   a					(self			: Optional[Any] ):





         """simple docstring"""



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


      @unittest.skipIf(torch_device == '''cpu'''							,     '''Cant do half precision''' )
      def   a					(self			: Tuple ):





         """simple docstring"""



         __snake_case							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_fpaa_forward(*_snake_case )


      def   a					(self			: Dict ):





         """simple docstring"""



         __snake_case							=  ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
         __snake_case							=  self.model_tester.prepare_config_and_inputs()
         __snake_case							=  config_and_inputs[0]
         __snake_case							=  UMTaForConditionalGeneration(_snake_case ).eval()
         model.to(_snake_case )

         __snake_case							=  {
             '''head_mask''': torch.zeros(config.num_layers							,     config.num_heads							,     device=_snake_case ),
             '''decoder_head_mask''': torch.zeros(config.num_decoder_layers							,     config.num_heads							,     device=_snake_case ),
             '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers							,     config.num_heads							,     device=_snake_case ),
         }

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

            __snake_case							=  model.generate(
                config_and_inputs[1]['''input_ids''']							,     num_beams=1							,     max_length=3							,     output_attentions=_snake_case							,     return_dict_in_generate=_snake_case							,     **_snake_case							,     )
            # We check the state of decoder_attentions and cross_attentions just from the last step
            __snake_case							=  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   a					(self			: Optional[int] ):





         """simple docstring"""



         pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_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   a					(self			: Dict ):





         """simple docstring"""



         __snake_case							=  UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''							,     return_dict=_snake_case ).to(_snake_case )
         __snake_case							=  AutoTokenizer.from_pretrained('''google/umt5-small'''							,     use_fast=_snake_case							,     legacy=_snake_case )
         __snake_case							=  [
             '''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>.''',
         ]
         __snake_case							=  tokenizer(_snake_case							,     return_tensors='''pt'''							,     padding=_snake_case ).input_ids
         # fmt: off
         __snake_case							=  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(_snake_case							,     _snake_case )

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



"""simple docstring"""





import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad



class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    10




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







								"""simple docstring"""
								A_								=    [1, 2, 3, 4]
								A_								=    [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
								self.assertEqual(truncate_or_pad(_snake_case							, self.block_size							, 0						)							, _snake_case						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								A_								=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								self.assertEqual(truncate_or_pad(_snake_case							, self.block_size							, 0						)							, _snake_case						)




			def   lowerCamelCase__  (					self    :       str						)   ->			Dict:







								"""simple docstring"""
								A_								=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
								A_								=    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								self.assertEqual(truncate_or_pad(_snake_case							, self.block_size							, 0						)							, _snake_case						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			Dict:







								"""simple docstring"""
								A_								=    "It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this."
								A_			,     A_								=    process_story(_snake_case						)
								self.assertEqual(_snake_case							, []						)




			def   lowerCamelCase__  (					self    :       str						)   ->			str:







								"""simple docstring"""
								A_								=    ""
								A_			,     A_								=    process_story(_snake_case						)
								self.assertEqual(_snake_case							, []						)
								self.assertEqual(_snake_case							, []						)




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







								"""simple docstring"""
								A_								=    (
								    "It was the year of Our Lord one thousand seven hundred and "
								    "seventy-five\n\nSpiritual revelations were conceded to England "
								    "at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
								)
								A_			,     A_								=    process_story(_snake_case						)

								A_								=    [
								    "It was the year of Our Lord one thousand seven hundred and seventy-five.",
								    "Spiritual revelations were conceded to England at that favoured period, as at this.",
								]
								self.assertEqual(_snake_case							, _snake_case						)

								A_								=    ["It was the best of times."]
								self.assertEqual(_snake_case							, _snake_case						)




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







								"""simple docstring"""
								A_								=    torch.tensor([1, 2, 3, 4]						)
								A_								=    torch.tensor([1, 1, 1, 1]						)
								np.testing.assert_array_equal(build_mask(_snake_case							, 0						).numpy()							, expected.numpy()						)




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







								"""simple docstring"""
								A_								=    torch.tensor([1, 2, 3, 4, 23, 23, 23]						)
								A_								=    torch.tensor([1, 1, 1, 1, 0, 0, 0]						)
								np.testing.assert_array_equal(build_mask(_snake_case							, 23						).numpy()							, expected.numpy()						)




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







								"""simple docstring"""
								A_								=    torch.tensor([8, 2, 3, 4, 1, 1, 1]						)
								A_								=    torch.tensor([1, 1, 1, 1, 0, 0, 0]						)
								np.testing.assert_array_equal(build_mask(_snake_case							, 1						).numpy()							, expected.numpy()						)






			def   lowerCamelCase__  (					self    :       int						)   ->			List[str]:







								"""simple docstring"""
								A_								=    101
								A_								=    torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]						)
								A_								=    torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]						)

								A_								=    compute_token_type_ids(_snake_case							, _snake_case						)
								np.testing.assert_array_equal(_snake_case							, _snake_case						)




