





'''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   :		int       , lowerCAmelCase__   :		str       , lowerCAmelCase__   :		Union[str, Any]=99       , lowerCAmelCase__   :		Optional[int]=13       , lowerCAmelCase__   :		List[Any]=7       , lowerCAmelCase__   :		Optional[Any]=9       , lowerCAmelCase__   :		List[Any]=True       , lowerCAmelCase__   :		Optional[Any]=True       , lowerCAmelCase__   :		Dict=False       , lowerCAmelCase__   :		Any=32       , lowerCAmelCase__   :		List[str]=5       , lowerCAmelCase__   :		Union[str, Any]=4       , lowerCAmelCase__   :		Any=37       , lowerCAmelCase__   :		Union[str, Any]=8       , lowerCAmelCase__   :		str=0.1       , lowerCAmelCase__   :		List[Any]=0.002       , lowerCAmelCase__   :		Tuple=1       , lowerCAmelCase__   :		List[Any]=0       , lowerCAmelCase__   :		str=0       , lowerCAmelCase__   :		Optional[int]=None       , lowerCAmelCase__   :		List[Any]=None       , )    ->     Optional[Any]:

            '''simple docstring'''




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





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

            '''simple docstring'''




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





       def snake_case__						(							self   :		Dict       , lowerCAmelCase__   :		Union[str, Any]       , lowerCAmelCase__   :		List[Any]       , lowerCAmelCase__   :		Dict       , lowerCAmelCase__   :		Optional[Any]=None       , lowerCAmelCase__   :		Dict=None       , lowerCAmelCase__   :		Dict=None       , lowerCAmelCase__   :		Union[str, Any]=None       , lowerCAmelCase__   :		Tuple=None       , )    ->     Union[str, Any]:

            '''simple docstring'''




            if attention_mask is None:
                 _UpperCamelCase				    =       input_ids.ne(config.pad_token_id     )
            if decoder_attention_mask is None:
                 _UpperCamelCase				    =       decoder_input_ids.ne(config.pad_token_id     )
            if head_mask is None:
                 _UpperCamelCase				    =       torch.ones(config.num_hidden_layers       , config.num_attention_heads       , device=_A     )
            if decoder_head_mask is None:
                 _UpperCamelCase				    =       torch.ones(config.num_decoder_layers       , config.num_attention_heads       , device=_A     )
            if cross_attn_head_mask is None:
                 _UpperCamelCase				    =       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 snake_case__						(							self   :		Optional[int]     )    ->     str:

            '''simple docstring'''




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

            _UpperCamelCase				    =       self.get_config()
            _UpperCamelCase				    =       config.num_attention_heads
            _UpperCamelCase				    =       self.prepare_inputs_dict(_A       , _A       , _A     )
            return config, input_dict





       def snake_case__						(							self   :		str     )    ->     Dict:

            '''simple docstring'''




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





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

            '''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 snake_case__						(							self   :		Union[str, Any]     )    ->     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 snake_case__						(							self   :		List[Any]       , lowerCAmelCase__   :		Any       , lowerCAmelCase__   :		Union[str, Any]       , lowerCAmelCase__   :		List[str]       , lowerCAmelCase__   :		List[str]       , lowerCAmelCase__   :		Tuple       , lowerCAmelCase__   :		List[Any]       , )    ->     int:

            '''simple docstring'''




            _UpperCamelCase				    =       UMTaModel(config=_A     )
            model.to(_A     )
            model.eval()
            _UpperCamelCase				    =       model(
                input_ids=_A       , decoder_input_ids=_A       , attention_mask=_A       , decoder_attention_mask=_A       , )
            _UpperCamelCase				    =       model(input_ids=_A       , decoder_input_ids=_A     )
            _UpperCamelCase				    =       result.last_hidden_state
            _UpperCamelCase				    =       result.past_key_values
            _UpperCamelCase				    =       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 snake_case__						(							self   :		List[Any]       , lowerCAmelCase__   :		Any       , lowerCAmelCase__   :		Optional[int]       , lowerCAmelCase__   :		List[str]       , lowerCAmelCase__   :		Union[str, Any]       , lowerCAmelCase__   :		Dict       , lowerCAmelCase__   :		Union[str, Any]       , )    ->     str:

            '''simple docstring'''




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

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

            _UpperCamelCase    ,				_UpperCamelCase				    =       outputs.to_tuple()

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

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

            _UpperCamelCase				    =       model(_A     )['''last_hidden_state''']
            _UpperCamelCase				    =       model(_A       , past_key_values=_A     )['''last_hidden_state''']

            # select random slice
            _UpperCamelCase				    =       ids_tensor((1,)       , output_from_past.shape[-1]     ).item()
            _UpperCamelCase				    =       output_from_no_past[:, -1, random_slice_idx].detach()
            _UpperCamelCase				    =       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 snake_case__						(							self   :		Union[str, Any]       , lowerCAmelCase__   :		str       , lowerCAmelCase__   :		List[str]       , )    ->     List[str]:

            '''simple docstring'''




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

@require_torch
class 				__lowerCAmelCase     (    a_						,						a_						,						a_						,						unittest.TestCase ):






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





       def snake_case__						(							self   :		int     )    ->     Any:

            '''simple docstring'''




            _UpperCamelCase				    =       UMTaModelTester(self     )





       @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''     )
       def snake_case__						(							self   :		str     )    ->     Tuple:

            '''simple docstring'''




            _UpperCamelCase				    =       self.model_tester.prepare_config_and_inputs()
            _UpperCamelCase				    =       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 snake_case__						(							self   :		List[Any]     )    ->     str:

            '''simple docstring'''




            _UpperCamelCase				    =       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*_A     )





       def snake_case__						(							self   :		int     )    ->     Dict:

            '''simple docstring'''




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

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

                 _UpperCamelCase				    =       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
                 _UpperCamelCase				    =       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 snake_case__						(							self   :		int     )    ->     Tuple:

            '''simple docstring'''




            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 snake_case__						(							self   :		Tuple     )    ->     Dict:

            '''simple docstring'''




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

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










def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						UpperCAmelCase   		=				_modexpt(UpperCamelCase__			,			exponent // 2			,			UpperCamelCase__ ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(UpperCamelCase__			,			exponent - 1			,			UpperCamelCase__ )) % modulo_value





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ = 1777			,			UpperCamelCase__ = 1855			,			UpperCamelCase__ = 8 )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				base
			for _ in range(1			,			UpperCamelCase__ ):
						UpperCAmelCase   		=				_modexpt(UpperCamelCase__			,			UpperCamelCase__			,			10**digits )

			return result


if __name__ == "__main__":
				print(F'{solution() = }')



