
"""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 				A__ :

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

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

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

			__lowerCAmelCase    :		Dict		    =   self.get_config()
			__lowerCAmelCase    :		List[Any]		    =   config.num_attention_heads
			__lowerCAmelCase    :		Dict		    =   self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			return config, input_dict

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   self.prepare_config_and_inputs()
			return config, inputs_dict

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

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

			__lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   outputs.to_tuple()

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

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

			__lowerCAmelCase    :		List[str]		    =   model(_SCREAMING_SNAKE_CASE				)['last_hidden_state']
			__lowerCAmelCase    :		Any		    =   model(_SCREAMING_SNAKE_CASE ,	past_key_values=_SCREAMING_SNAKE_CASE				)['last_hidden_state']

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

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







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		List[Any]		    =   UMTaModel(config=_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				).half().eval()
			__lowerCAmelCase    :		List[str]		    =   model(**_SCREAMING_SNAKE_CASE				)['last_hidden_state']
			self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE				).any().item()				)




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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   UMTaModelTester(self				)

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

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
			__lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs()
			__lowerCAmelCase    :		Tuple		    =   config_and_inputs[0]
			__lowerCAmelCase    :		Tuple		    =   UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE				).eval()
			model.to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   {
			    'head_mask': torch.zeros(config.num_layers ,	config.num_heads ,	device=_SCREAMING_SNAKE_CASE				),
			    'decoder_head_mask': torch.zeros(config.num_decoder_layers ,	config.num_heads ,	device=_SCREAMING_SNAKE_CASE				),
			    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers ,	config.num_heads ,	device=_SCREAMING_SNAKE_CASE				),
			}

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

				__lowerCAmelCase    :		Optional[Any]		    =   model.generate(
				    config_and_inputs[1]['input_ids'] ,	num_beams=1 ,	max_length=3 ,	output_attentions=_SCREAMING_SNAKE_CASE ,	return_dict_in_generate=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
				# We check the state of decoder_attentions and cross_attentions just from the last step
				__lowerCAmelCase    :		Union[str, Any]		    =   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				):
			pass




@require_torch
@require_sentencepiece
@require_tokenizers
class 				A__ (			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				):
			__lowerCAmelCase    :		List[str]		    =   UMTaForConditionalGeneration.from_pretrained('google/umt5-small' ,	return_dict=_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   AutoTokenizer.from_pretrained('google/umt5-small' ,	use_fast=_SCREAMING_SNAKE_CASE ,	legacy=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   [
			    '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    :		Union[str, Any]		    =   tokenizer(_SCREAMING_SNAKE_CASE ,	return_tensors='pt' ,	padding=_SCREAMING_SNAKE_CASE				).input_ids
			# fmt: off
			__lowerCAmelCase    :		Optional[Any]		    =   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(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   model.generate(input_ids.to(_SCREAMING_SNAKE_CASE				)				)
			__lowerCAmelCase    :		Optional[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>',
			]
			__lowerCAmelCase    :		Tuple		    =   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)
			self.assertEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Tuple		    =   HfArgumentParser(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   parser.parse_args_into_dataclasses()[0]
	__lowerCAmelCase    :		int		    =   TensorFlowBenchmark(args=_UpperCamelCase	)
	try:
		__lowerCAmelCase    :		Dict		    =   parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		__lowerCAmelCase    :		List[Any]		    =   'Arg --no_{0} is no longer used, please use --no-{0} instead.'
		__lowerCAmelCase    :		Union[str, Any]		    =   ' '.join(str(_UpperCamelCase	).split(' '	)[:-1]	)
		__lowerCAmelCase    :		List[str]		    =   ''
		__lowerCAmelCase    :		Any		    =   eval(str(_UpperCamelCase	).split(' '	)[-1]	)
		__lowerCAmelCase    :		Dict		    =   []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(_UpperCamelCase	)
		if len(_UpperCamelCase	) > 0:
			__lowerCAmelCase    :		Any		    =   full_error_msg + begin_error_msg + str(_UpperCamelCase	)
		raise ValueError(_UpperCamelCase	)
	benchmark.run()


if __name__ == "__main__":
							main()