





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    ,						__lowerCamelCase    ,						__lowerCamelCase=9_9    ,						__lowerCamelCase=1_3    ,						__lowerCamelCase=7    ,						__lowerCamelCase=9    ,						__lowerCamelCase=True    ,						__lowerCamelCase=True    ,						__lowerCamelCase=False    ,						__lowerCamelCase=3_2    ,						__lowerCamelCase=5    ,						__lowerCamelCase=4    ,						__lowerCamelCase=3_7    ,						__lowerCamelCase=8    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=0.002    ,						__lowerCamelCase=1    ,						__lowerCamelCase=0    ,						__lowerCamelCase=0    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							parent
            _SCREAMING_SNAKE_CASE       :	int						    =							batch_size
            _SCREAMING_SNAKE_CASE       :	str						    =							encoder_seq_length
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							decoder_seq_length
            # For common tests
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.decoder_seq_length
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							is_training
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							use_attention_mask
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							use_labels
            _SCREAMING_SNAKE_CASE       :	int						    =							vocab_size
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							hidden_size
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							num_hidden_layers
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							num_attention_heads
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							d_ff
            _SCREAMING_SNAKE_CASE       :	Any						    =							relative_attention_num_buckets
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							dropout_rate
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							initializer_factor
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							eos_token_id
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							pad_token_id
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							decoder_start_token_id
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							None
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							decoder_layers




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            return TaConfig.from_pretrained("google/umt5-base"							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						)			->     Tuple:
            if attention_mask is None:
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							input_ids.ne(config.pad_token_id							)
            if decoder_attention_mask is None:
                 _SCREAMING_SNAKE_CASE       :	int						    =							decoder_input_ids.ne(config.pad_token_id							)
            if head_mask is None:
                 _SCREAMING_SNAKE_CASE       :	str						    =							torch.ones(config.num_hidden_layers    ,						config.num_attention_heads    ,						device=_A							)
            if decoder_head_mask is None:
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.ones(config.num_decoder_layers    ,						config.num_attention_heads    ,						device=_A							)
            if cross_attn_head_mask is None:
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							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							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							ids_tensor([self.batch_size, self.encoder_seq_length]    ,						self.vocab_size							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							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
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							input_ids.clamp(self.pad_token_id + 1							)
            _SCREAMING_SNAKE_CASE       :	int						    =							decoder_input_ids.clamp(self.pad_token_id + 1							)

            _SCREAMING_SNAKE_CASE       :	str						    =							self.get_config()
            _SCREAMING_SNAKE_CASE       :	str						    =							config.num_attention_heads
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.prepare_inputs_dict(_A    ,						_A    ,						_A							)
            return config, input_dict




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




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            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     UpperCamelCase_			(      self							)			->     Dict:
            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    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							UMTaModel(config=_A							)
            model.to(_A							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model(
                input_ids=_A    ,						decoder_input_ids=_A    ,						attention_mask=_A    ,						decoder_attention_mask=_A    ,						)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(input_ids=_A    ,						decoder_input_ids=_A							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							result.last_hidden_state
            _SCREAMING_SNAKE_CASE       :	Dict						    =							result.past_key_values
            _SCREAMING_SNAKE_CASE       :	Any						    =							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    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							UMTaModel(config=_A							).get_decoder().to(_A							).eval()
            # first forward pass
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							model(_A    ,						use_cache=_A							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							model(_A							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							model(_A    ,						use_cache=_A							)

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

            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[Any]						    =							outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							ids_tensor((self.batch_size, 1)    ,						config.vocab_size							)

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

            _SCREAMING_SNAKE_CASE       :	str						    =							model(_A							)["last_hidden_state"]
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(_A    ,						past_key_values=_A							)["last_hidden_state"]

            # select random slice
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							ids_tensor((1,)    ,						output_from_past.shape[-1]							).item()
            _SCREAMING_SNAKE_CASE       :	str						    =							output_from_no_past[:, -1, random_slice_idx].detach()
            _SCREAMING_SNAKE_CASE       :	Union[str, 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     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     str:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							UMTaModel(config=_A							).to(_A							).half().eval()
            _SCREAMING_SNAKE_CASE       :	int						    =							model(**_A							)["last_hidden_state"]
            self.parent.assertFalse(torch.isnan(_A							).any().item()							)





@require_torch
class 			lowerCAmelCase__( __lowercase  ,     __lowercase  ,     __lowercase  ,     unittest.TestCase    ):






       '''simple docstring'''


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




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							UMTaModelTester(self							)




       @unittest.skip("Test has a segmentation fault on torch 1.8.0"							)
       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.model_tester.prepare_config_and_inputs()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							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							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*_A							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							["encoder_attentions", "decoder_attentions", "cross_attentions"]
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.model_tester.prepare_config_and_inputs()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							config_and_inputs[0]
            _SCREAMING_SNAKE_CASE       :	str						    =							UMTaForConditionalGeneration(_A							).eval()
            model.to(_A							)

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

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

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							model.generate(input_ids.to(_A							)							)
            _SCREAMING_SNAKE_CASE       :	str						    =							[
                "<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>",
            ]
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							tokenizer.batch_decode(_A							)
            self.assertEqual(_A    ,						_A							)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LiltForQuestionAnswering',
           'LiltForSequenceClassification',
           'LiltForTokenClassification',
           'LiltModel',
           'LiltPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)