_lowercase:	List[str]       			=		[
    '''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







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	:	Union[str, Any]  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Dict=99  ,  lowercase__	:	List[Any]=13  ,  lowercase__	:	Union[str, Any]=7  ,  lowercase__	:	int=9  ,  lowercase__	:	List[str]=True  ,  lowercase__	:	str=True  ,  lowercase__	:	Any=False  ,  lowercase__	:	int=32  ,  lowercase__	:	Any=5  ,  lowercase__	:	int=4  ,  lowercase__	:	int=37  ,  lowercase__	:	List[str]=8  ,  lowercase__	:	Optional[int]=0.1  ,  lowercase__	:	List[Any]=0.0_0_2  ,  lowercase__	:	str=1  ,  lowercase__	:	List[str]=0  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	List[str]=None  ,  lowercase__	:	Any=None  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							encoder_seq_length
										_lowerCAmelCase							=							decoder_seq_length
										# For common tests
										_lowerCAmelCase							=							self.decoder_seq_length
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_attention_mask
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							d_ff
										_lowerCAmelCase							=							relative_attention_num_buckets
										_lowerCAmelCase							=							dropout_rate
										_lowerCAmelCase							=							initializer_factor
										_lowerCAmelCase							=							eos_token_id
										_lowerCAmelCase							=							pad_token_id
										_lowerCAmelCase							=							decoder_start_token_id
										_lowerCAmelCase							=							None
										_lowerCAmelCase							=							decoder_layers





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										return TaConfig.from_pretrained('google/umt5-base'						)





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

										_lowerCAmelCase							=							self.get_config()
										_lowerCAmelCase							=							config.num_attention_heads
										_lowerCAmelCase							=							self.prepare_inputs_dict(lowercase__  ,  lowercase__  ,  lowercase__						)
										return config, input_dict





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self.prepare_config_and_inputs()
										return config, inputs_dict





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										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   SCREAMING_SNAKE_CASE__     (       self	:	Optional[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   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	List[str]  ,  lowercase__	:	str  ,  lowercase__	:	Any  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Dict  ,  ):
										_lowerCAmelCase							=							UMTaModel(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(
										    input_ids=lowercase__  ,  decoder_input_ids=lowercase__  ,  attention_mask=lowercase__  ,  decoder_attention_mask=lowercase__  ,  )
										_lowerCAmelCase							=							model(input_ids=lowercase__  ,  decoder_input_ids=lowercase__						)
										_lowerCAmelCase							=							result.last_hidden_state
										_lowerCAmelCase							=							result.past_key_values
										_lowerCAmelCase							=							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(lowercase__						)  ,  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   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	Tuple  ,  lowercase__	:	Tuple  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	str  ,  lowercase__	:	List[Any]  ,  ):
										_lowerCAmelCase							=							UMTaModel(config=lowercase__						).get_decoder().to(lowercase__						).eval()
										# first forward pass
										_lowerCAmelCase							=							model(lowercase__  ,  use_cache=lowercase__						)
										_lowerCAmelCase							=							model(lowercase__						)
										_lowerCAmelCase							=							model(lowercase__  ,  use_cache=lowercase__						)

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

										_lowerCAmelCase			,		_lowerCAmelCase							=							outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										_lowerCAmelCase							=							ids_tensor((self.batch_size, 1)  ,  config.vocab_size						)

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

										_lowerCAmelCase							=							model(lowercase__						)['last_hidden_state']
										_lowerCAmelCase							=							model(lowercase__  ,  past_key_values=lowercase__						)['last_hidden_state']

										# select random slice
										_lowerCAmelCase							=							ids_tensor((1,)  ,  output_from_past.shape[-1]						).item()
										_lowerCAmelCase							=							output_from_no_past[:, -1, random_slice_idx].detach()
										_lowerCAmelCase							=							output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowercase__  ,  lowercase__  ,  atol=1e-3						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Dict  ,  ):
										_lowerCAmelCase							=							UMTaModel(config=lowercase__						).to(lowercase__						).half().eval()
										_lowerCAmelCase							=							model(**lowercase__						)['last_hidden_state']
										self.parent.assertFalse(torch.isnan(lowercase__						).any().item()						)







@require_torch
class        lowerCamelCase__				(   UpperCAmelCase       ,UpperCAmelCase       ,UpperCAmelCase       ,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   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							UMTaModelTester(self						)





			@unittest.skip('Test has a segmentation fault on torch 1.8.0'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										_lowerCAmelCase							=							UMTaModel(config_and_inputs[0]						).to(lowercase__						)
										with tempfile.TemporaryDirectory() as tmpdirname:
																	torch.onnx.export(
																	    lowercase__  ,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])  ,  f'{tmpdirname}/t5_test.onnx'  ,  export_params=lowercase__  ,  opset_version=9  ,  input_names=['input_ids', 'decoder_input_ids']  ,  )





			@unittest.skipIf(torch_device == 'cpu'  ,  'Cant do half precision'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model_fpaa_forward(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							['encoder_attentions', 'decoder_attentions', 'cross_attentions']
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										_lowerCAmelCase							=							config_and_inputs[0]
										_lowerCAmelCase							=							UMTaForConditionalGeneration(lowercase__						).eval()
										model.to(lowercase__						)

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

										for attn_name, (name, mask) in zip(lowercase__  ,  head_masking.items()						):
																	_lowerCAmelCase							=							{name: mask}
																	# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																	if name == "head_mask":
																								_lowerCAmelCase							=							torch.ones(
																								    config.num_decoder_layers  ,  config.num_heads  ,  device=lowercase__						)

																	_lowerCAmelCase							=							model.generate(
																	    config_and_inputs[1]['input_ids']  ,  num_beams=1  ,  max_length=3  ,  output_attentions=lowercase__  ,  return_dict_in_generate=lowercase__  ,  **lowercase__  ,  )
																	# We check the state of decoder_attentions and cross_attentions just from the last step
																	_lowerCAmelCase							=							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   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										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   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							UMTaForConditionalGeneration.from_pretrained('google/umt5-small'  ,  return_dict=lowercase__						).to(lowercase__						)
										_lowerCAmelCase							=							AutoTokenizer.from_pretrained('google/umt5-small'  ,  use_fast=lowercase__  ,  legacy=lowercase__						)
										_lowerCAmelCase							=							[
										    '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>.',
										]
										_lowerCAmelCase							=							tokenizer(lowercase__  ,  return_tensors='pt'  ,  padding=lowercase__						).input_ids
										# fmt: off
										_lowerCAmelCase							=							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(lowercase__  ,  lowercase__						)

										_lowerCAmelCase							=							model.generate(input_ids.to(lowercase__						)						)
										_lowerCAmelCase							=							[
										    '<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>',
										]
										_lowerCAmelCase							=							tokenizer.batch_decode(lowercase__						)
										self.assertEqual(lowercase__  ,  lowercase__						)






