"""simple docstring"""







_a  :  Optional[Any]=				[
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






"""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 	UpperCamelCase    :



				def __init__(self       :   Dict			,						_A       :   Union[str, Any]			,						_A       :   Optional[Any]=99			,						_A       :   List[Any]=13			,						_A       :   List[str]=7			,						_A       :   List[Any]=9			,						_A       :   List[Any]=True			,						_A       :   Union[str, Any]=True			,						_A       :   int=False			,						_A       :   Optional[int]=32			,						_A       :   Union[str, Any]=5			,						_A       :   Union[str, Any]=4			,						_A       :   str=37			,						_A       :   List[Any]=8			,						_A       :   Tuple=0.1			,						_A       :   int=0.002			,						_A       :   str=1			,						_A       :   List[str]=0			,						_A       :   Optional[int]=0			,						_A       :   Tuple=None			,						_A       :   int=None			,						)					->			Any:
					__snake_case    :							Optional[Any]   	=				parent
					__snake_case    :							Tuple   	=				batch_size
					__snake_case    :							Dict   	=				encoder_seq_length
					__snake_case    :							Optional[int]   	=				decoder_seq_length
					# For common tests
					__snake_case    :							Dict   	=				self.decoder_seq_length
					__snake_case    :							Dict   	=				is_training
					__snake_case    :							Tuple   	=				use_attention_mask
					__snake_case    :							Optional[int]   	=				use_labels
					__snake_case    :							Any   	=				vocab_size
					__snake_case    :							Tuple   	=				hidden_size
					__snake_case    :							Union[str, Any]   	=				num_hidden_layers
					__snake_case    :							List[Any]   	=				num_attention_heads
					__snake_case    :							Dict   	=				d_ff
					__snake_case    :							List[str]   	=				relative_attention_num_buckets
					__snake_case    :							Optional[Any]   	=				dropout_rate
					__snake_case    :							Any   	=				initializer_factor
					__snake_case    :							Dict   	=				eos_token_id
					__snake_case    :							Optional[Any]   	=				pad_token_id
					__snake_case    :							Optional[int]   	=				decoder_start_token_id
					__snake_case    :							Tuple   	=				None
					__snake_case    :							Optional[Any]   	=				decoder_layers



				def       _lowercase  (self       :   Union[str, Any])					->			Dict:
					return TaConfig.from_pretrained('google/umt5-base')



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

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



				def       _lowercase  (self       :   List[Any])					->			List[str]:
					__snake_case	,       __snake_case    :							List[Any]   	=				self.prepare_config_and_inputs()
					return config, inputs_dict



				def       _lowercase  (self       :   int)					->			Any:
					return TaConfig(
					    vocab_size=1_66			,						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       _lowercase  (self       :   Union[str, Any])					->			str:
					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       _lowercase  (self       :   Dict			,						_A       :   int			,						_A       :   Any			,						_A       :   str			,						_A       :   str			,						_A       :   str			,						_A       :   Dict			,						)					->			List[str]:
					__snake_case    :							Dict   	=				UMTaModel(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							str   	=				model(
					    input_ids=_A			,						decoder_input_ids=_A			,						attention_mask=_A			,						decoder_attention_mask=_A			,						)
					__snake_case    :							List[str]   	=				model(input_ids=_A			,						decoder_input_ids=_A)
					__snake_case    :							Dict   	=				result.last_hidden_state
					__snake_case    :							List[Any]   	=				result.past_key_values
					__snake_case    :							Union[str, 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       _lowercase  (self       :   Union[str, Any]			,						_A       :   List[str]			,						_A       :   Any			,						_A       :   Optional[Any]			,						_A       :   Optional[Any]			,						_A       :   Optional[Any]			,						_A       :   str			,						)					->			Union[str, Any]:
					__snake_case    :							Dict   	=				UMTaModel(config=_A).get_decoder().to(_A).eval()
					# first forward pass
					__snake_case    :							Optional[int]   	=				model(_A			,						use_cache=_A)
					__snake_case    :							str   	=				model(_A)
					__snake_case    :							str   	=				model(_A			,						use_cache=_A)

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

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

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

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

					__snake_case    :							Any   	=				model(_A)['last_hidden_state']
					__snake_case    :							List[str]   	=				model(_A			,						past_key_values=_A)['last_hidden_state']

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







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



				def       _lowercase  (self       :   Optional[int])					->			Union[str, Any]:
					__snake_case    :							Dict   	=				UMTaModelTester(self)



				@unittest.skip('Test has a segmentation fault on torch 1.8.0')
				def       _lowercase  (self       :   int)					->			int:
					__snake_case    :							Optional[Any]   	=				self.model_tester.prepare_config_and_inputs()
					__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       _lowercase  (self       :   Dict)					->			Dict:
					__snake_case    :							str   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model_fpaa_forward(*_A)



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

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

						__snake_case    :							Dict   	=				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
						__snake_case    :							Tuple   	=				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       _lowercase  (self       :   Dict)					->			Tuple:
					pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase    (					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       _lowercase  (self       :   Dict)					->			Optional[Any]:
					__snake_case    :							List[str]   	=				UMTaForConditionalGeneration.from_pretrained('google/umt5-small'			,						return_dict=_A).to(_A)
					__snake_case    :							Tuple   	=				AutoTokenizer.from_pretrained('google/umt5-small'			,						use_fast=_A			,						legacy=_A)
					__snake_case    :							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>.',
					]
					__snake_case    :							int   	=				tokenizer(_A			,						return_tensors='pt'			,						padding=_A).input_ids
					# fmt: off
					__snake_case    :							Tuple   	=				torch.tensor(
					    [
					        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
					        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
					    ])
					# fmt: on
					torch.testing.assert_allclose(_A			,						_A)

					__snake_case    :							int   	=				model.generate(input_ids.to(_A))
					__snake_case    :							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>',
					]
					__snake_case    :							List[Any]   	=				tokenizer.batch_decode(_A)
					self.assertEqual(_A			,						_A)





