
'''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					,     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_=0.002					,     lowerCAmelCase_=1					,     lowerCAmelCase_=0					,     lowerCAmelCase_=0					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     ):





								"""simple docstring"""






								_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						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     ):





								"""simple docstring"""






								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=__UpperCamelCase						)
								if decoder_head_mask is None:
									_snake_case     		=	torch.ones(config.num_decoder_layers					,     config.num_attention_heads					,     device=__UpperCamelCase						)
								if cross_attn_head_mask is None:
									_snake_case     		=	torch.ones(
									    config.num_decoder_layers					,     config.num_attention_heads					,     device=__UpperCamelCase						)
								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						):





								"""simple docstring"""






								_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(__UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase						)
								return config, input_dict


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""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						):





								"""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					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	UMTaModel(config=__UpperCamelCase						)
								model.to(__UpperCamelCase						)
								model.eval()
								_snake_case     		=	model(
								    input_ids=__UpperCamelCase					,     decoder_input_ids=__UpperCamelCase					,     attention_mask=__UpperCamelCase					,     decoder_attention_mask=__UpperCamelCase					,     )
								_snake_case     		=	model(input_ids=__UpperCamelCase					,     decoder_input_ids=__UpperCamelCase						)
								_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(__UpperCamelCase						)					,     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_					,     ):





								"""simple docstring"""






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

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




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






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


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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	UMTaModelTester(self						)


							@unittest.skip('Test has a segmentation fault on torch 1.8.0'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								_snake_case     		=	UMTaModel(config_and_inputs[0]						).to(__UpperCamelCase						)
								with tempfile.TemporaryDirectory() as tmpdirname:
									torch.onnx.export(
									    __UpperCamelCase					,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])					,     F'{tmpdirname}/t5_test.onnx'					,     export_params=__UpperCamelCase					,     opset_version=9					,     input_names=['input_ids', 'decoder_input_ids']					,     )


							@unittest.skipIf(torch_device == 'cpu'					,     'Cant do half precision'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_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(__UpperCamelCase						).eval()
								model.to(__UpperCamelCase						)

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

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

									_snake_case     		=	model.generate(
									    config_and_inputs[1]['input_ids']					,     num_beams=1					,     max_length=3					,     output_attentions=__UpperCamelCase					,     return_dict_in_generate=__UpperCamelCase					,     **__UpperCamelCase					,     )
									# 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						):





								"""simple docstring"""






								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						):





								"""simple docstring"""






								_snake_case     		=	UMTaForConditionalGeneration.from_pretrained('google/umt5-small'					,     return_dict=__UpperCamelCase						).to(__UpperCamelCase						)
								_snake_case     		=	AutoTokenizer.from_pretrained('google/umt5-small'					,     use_fast=__UpperCamelCase					,     legacy=__UpperCamelCase						)
								_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(__UpperCamelCase					,     return_tensors='pt'					,     padding=__UpperCamelCase						).input_ids
								# fmt: off
								_snake_case     		=	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(__UpperCamelCase					,     __UpperCamelCase						)

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



'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import GLPNImageProcessor


class 		__UpperCAmelCase   (      unittest.TestCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=7					,     lowerCAmelCase_=3					,     lowerCAmelCase_=18					,     lowerCAmelCase_=30					,     lowerCAmelCase_=4_00					,     lowerCAmelCase_=True					,     lowerCAmelCase_=32					,     lowerCAmelCase_=True					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	num_channels
								_snake_case     		=	image_size
								_snake_case     		=	min_resolution
								_snake_case     		=	max_resolution
								_snake_case     		=	do_resize
								_snake_case     		=	size_divisor
								_snake_case     		=	do_rescale




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= GLPNImageProcessor if is_vision_available() else None


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	GLPNImageProcessingTester(self						)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_resize'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'size_divisor'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'resample'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_rescale'						)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     Image.Image						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_					,     numpify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     np.ndarray						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_					,     torchify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     torch.Tensor						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

