





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				,						lowerCAmelCase_				,						lowerCAmelCase_=99				,						lowerCAmelCase_=13				,						lowerCAmelCase_=7				,						lowerCAmelCase_=9				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=False				,						lowerCAmelCase_=32				,						lowerCAmelCase_=5				,						lowerCAmelCase_=4				,						lowerCAmelCase_=37				,						lowerCAmelCase_=8				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.0_02				,						lowerCAmelCase_=1				,						lowerCAmelCase_=0				,						lowerCAmelCase_=0				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						)		->							List[Any]:
										_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 	lowerCAmelCase			(		self  )		->							Tuple:
										return TaConfig.from_pretrained('google/umt5-base'  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						)		->							Optional[int]:
										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=lowerCAmelCase_  )
										if decoder_head_mask is None:
															_snake_case									=				torch.ones(config.num_decoder_layers				,						config.num_attention_heads				,						device=lowerCAmelCase_  )
										if cross_attn_head_mask is None:
															_snake_case									=				torch.ones(
															    config.num_decoder_layers				,						config.num_attention_heads				,						device=lowerCAmelCase_  )
										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  )		->							Tuple:
										_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(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )
										return config, input_dict




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case							,     _snake_case									=				self.prepare_config_and_inputs()
										return config, inputs_dict




					def 	lowerCAmelCase			(		self  )		->							int:
										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  )		->							List[Any]:
										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				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						)		->							Dict:
										_snake_case									=				UMTaModel(config=lowerCAmelCase_  )
										model.to(lowerCAmelCase_  )
										model.eval()
										_snake_case									=				model(
										    input_ids=lowerCAmelCase_				,						decoder_input_ids=lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						decoder_attention_mask=lowerCAmelCase_				,						)
										_snake_case									=				model(input_ids=lowerCAmelCase_				,						decoder_input_ids=lowerCAmelCase_  )
										_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(lowerCAmelCase_  )				,						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				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						)		->							List[Any]:
										_snake_case									=				UMTaModel(config=lowerCAmelCase_  ).get_decoder().to(lowerCAmelCase_  ).eval()
										# first forward pass
										_snake_case									=				model(lowerCAmelCase_				,						use_cache=lowerCAmelCase_  )
										_snake_case									=				model(lowerCAmelCase_  )
										_snake_case									=				model(lowerCAmelCase_				,						use_cache=lowerCAmelCase_  )

										self.parent.assertTrue(len(lowerCAmelCase_  ) == len(lowerCAmelCase_  )  )
										self.parent.assertTrue(len(lowerCAmelCase_  ) == len(lowerCAmelCase_  ) + 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(lowerCAmelCase_  )['last_hidden_state']
										_snake_case									=				model(lowerCAmelCase_				,						past_key_values=lowerCAmelCase_  )['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(lowerCAmelCase_				,						lowerCAmelCase_				,						atol=1E-3  )  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						)		->							Dict:
										_snake_case									=				UMTaModel(config=lowerCAmelCase_  ).to(lowerCAmelCase_  ).half().eval()
										_snake_case									=				model(**lowerCAmelCase_  )['last_hidden_state']
										self.parent.assertFalse(torch.isnan(lowerCAmelCase_  ).any().item()  )





@require_torch
class 			UpperCamelCase_		(  _lowerCamelCase							,  _lowerCamelCase							,  _lowerCamelCase							,  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  )		->							Union[str, Any]:
										_snake_case									=				UMTaModelTester(self  )




					@unittest.skip('Test has a segmentation fault on torch 1.8.0'  )
					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										_snake_case									=				UMTaModel(config_and_inputs[0]  ).to(lowerCAmelCase_  )
										with tempfile.TemporaryDirectory() as tmpdirname:
															torch.onnx.export(
															    lowerCAmelCase_				,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				,						F'''{tmpdirname}/t5_test.onnx'''				,						export_params=lowerCAmelCase_				,						opset_version=9				,						input_names=['input_ids', 'decoder_input_ids']				,						)




					@unittest.skipIf(torch_device == 'cpu'				,						'Cant do half precision'  )
					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_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(lowerCAmelCase_  ).eval()
										model.to(lowerCAmelCase_  )

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

										for attn_name, (name, mask) in zip(lowerCAmelCase_				,						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=lowerCAmelCase_  )

															_snake_case									=				model.generate(
															    config_and_inputs[1]['input_ids']				,						num_beams=1				,						max_length=3				,						output_attentions=lowerCAmelCase_				,						return_dict_in_generate=lowerCAmelCase_				,						**lowerCAmelCase_				,						)
															# 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 	lowerCAmelCase			(		self  )		->							int:
										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 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				UMTaForConditionalGeneration.from_pretrained('google/umt5-small'				,						return_dict=lowerCAmelCase_  ).to(lowerCAmelCase_  )
										_snake_case									=				AutoTokenizer.from_pretrained('google/umt5-small'				,						use_fast=lowerCAmelCase_				,						legacy=lowerCAmelCase_  )
										_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(lowerCAmelCase_				,						return_tensors='pt'				,						padding=lowerCAmelCase_  ).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(lowerCAmelCase_				,						lowerCAmelCase_  )

										_snake_case									=				model.generate(input_ids.to(lowerCAmelCase_  )  )
										_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(lowerCAmelCase_  )
										self.assertEqual(lowerCAmelCase_				,						lowerCAmelCase_  )










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class 			UpperCamelCase_		(  unittest.TestCase					):




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										debug_launcher(test_script.main  )






					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										debug_launcher(test_ops.main  )



