



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					:



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



								"""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 					_lowerCamelCase (		self     :						Optional[int])     ->					Any:



								"""simple docstring"""






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



					def 					_lowerCamelCase (		self     :						Tuple				,	A     :						int				,	A     :						Union[str, Any]				,	A     :						List[str]				,	A     :						Any=None				,	A     :						List[Any]=None				,	A     :						List[Any]=None				,	A     :						int=None				,	A     :						Optional[Any]=None				,	)     ->					int:



								"""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 					_lowerCamelCase (		self     :						Dict)     ->					Union[str, Any]:



								"""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 					_lowerCamelCase (		self     :						Any)     ->					Dict:



								"""simple docstring"""






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



					def 					_lowerCamelCase (		self     :						Dict)     ->					Tuple:



								"""simple docstring"""






								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 					_lowerCamelCase (		self     :						Optional[Any])     ->					Optional[int]:



								"""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 					_lowerCamelCase (		self     :						str				,	A     :						List[Any]				,	A     :						Any				,	A     :						List[str]				,	A     :						str				,	A     :						Tuple				,	A     :						str				,	)     ->					Dict:



								"""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 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						Union[str, Any]				,	A     :						Tuple				,	A     :						Optional[Any]				,	A     :						List[str]				,	A     :						str				,	A     :						List[str]				,	)     ->					Any:



								"""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 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						str				,	A     :						Optional[Any]				,	)     ->					Tuple:



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



					def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



								"""simple docstring"""






								_UpperCAmelCase	 =     UMTaModelTester(self)



					@unittest.skip('Test has a segmentation fault on torch 1.8.0')
					def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					int:



								"""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 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[Any]:



								"""simple docstring"""






								_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*A)



					def 					_lowerCamelCase (		self     :						Tuple)     ->					Tuple:



								"""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 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Union[str, Any]:



								"""simple docstring"""






								pass







@require_torch
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(    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     :						str)     ->					Optional[int]:



								"""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(
								    [
								        [ 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)

								_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)





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class     __lowerCAmelCase					(    A       ,		A       ,		unittest.TestCase		):
			UpperCamelCase           =  IFInpaintingPipeline
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCamelCase           =  PipelineTesterMixin.required_optional_params - {'''latents'''}



			def 					_lowerCamelCase (		self     :						List[str])     ->					Tuple:



						"""simple docstring"""






						return self._get_dummy_components()



			def 					_lowerCamelCase (		self     :						Any				,	A     :						int				,	A     :						Dict=0)     ->					Tuple:



						"""simple docstring"""






						if str(A).startswith('mps'):
									_UpperCAmelCase	 =     torch.manual_seed(A)
						else:
									_UpperCAmelCase	 =     torch.Generator(device=A).manual_seed(A)

						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)
						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)

						_UpperCAmelCase	 =     {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}

						return inputs



			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()				,	reason='XFormers attention is only available with CUDA and `xformers` installed'				,	)
			def 					_lowerCamelCase (		self     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



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



						"""simple docstring"""






						self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != 'cuda'				,	reason='float16 requires CUDA')
			def 					_lowerCamelCase (		self     :						List[str])     ->					Any:



						"""simple docstring"""






						super().test_save_load_floataa(expected_max_diff=1E-1)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						self._test_save_load_local()



			def 					_lowerCamelCase (		self     :						int)     ->					Tuple:



						"""simple docstring"""






						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2				,	)
