



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



			def 				__lowercase					(			self							:				Tuple		)							->					Optional[Any]:
									return TaConfig.from_pretrained("google/umt5-base"		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				List[str]   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Tuple=None   ,	_lowercase							:				List[Any]=None   ,	_lowercase							:				int=None   ,	_lowercase							:				Any=None   ,	_lowercase							:				Dict=None   ,	)							->					List[str]:

									if attention_mask is None:
															snake_case   :			int						    = input_ids.ne(config.pad_token_id		)
									if decoder_attention_mask is None:
															snake_case   :			Any						    = decoder_input_ids.ne(config.pad_token_id		)
									if head_mask is None:
															snake_case   :			Tuple						    = torch.ones(config.num_hidden_layers   ,	config.num_attention_heads   ,	device=_lowercase		)
									if decoder_head_mask is None:
															snake_case   :			Union[str, Any]						    = torch.ones(config.num_decoder_layers   ,	config.num_attention_heads   ,	device=_lowercase		)
									if cross_attn_head_mask is None:
															snake_case   :			Optional[Any]						    = 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 				__lowercase					(			self							:				List[Any]		)							->					Union[str, Any]:
									snake_case   :			int						    = ids_tensor([self.batch_size, self.encoder_seq_length]   ,	self.vocab_size		)
									snake_case   :			Any						    = 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   :			int						    = input_ids.clamp(self.pad_token_id + 1		)
									snake_case   :			Tuple						    = decoder_input_ids.clamp(self.pad_token_id + 1		)

									snake_case   :			Any						    = self.get_config()
									snake_case   :			Union[str, Any]						    = config.num_attention_heads
									snake_case   :			Dict						    = self.prepare_inputs_dict(_lowercase   ,	_lowercase   ,	_lowercase		)
									return config, input_dict



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					int:
									snake_case   :			Tuple						    = self.prepare_config_and_inputs()
									return config, inputs_dict



			def 				__lowercase					(			self							:				int		)							->					List[Any]:
									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 				__lowercase					(			self							:				List[Any]		)							->					int:
									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 				__lowercase					(			self							:				Dict   ,	_lowercase							:				List[str]   ,	_lowercase							:				Dict   ,	_lowercase							:				int   ,	_lowercase							:				Tuple   ,	_lowercase							:				List[str]   ,	_lowercase							:				Union[str, Any]   ,	)							->					Union[str, Any]:
									snake_case   :			int						    = UMTaModel(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			List[str]						    = model(
									    input_ids=_lowercase   ,	decoder_input_ids=_lowercase   ,	attention_mask=_lowercase   ,	decoder_attention_mask=_lowercase   ,	)
									snake_case   :			Any						    = model(input_ids=_lowercase   ,	decoder_input_ids=_lowercase		)
									snake_case   :			Optional[Any]						    = result.last_hidden_state
									snake_case   :			List[str]						    = result.past_key_values
									snake_case   :			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(_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 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Tuple   ,	_lowercase							:				Tuple   ,	_lowercase							:				List[Any]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Optional[int]   ,	)							->					Union[str, Any]:
									snake_case   :			int						    = UMTaModel(config=_lowercase		).get_decoder().to(_lowercase		).eval()
									# first forward pass
									snake_case   :			int						    = model(_lowercase   ,	use_cache=_lowercase		)
									snake_case   :			Dict						    = model(_lowercase		)
									snake_case   :			str						    = model(_lowercase   ,	use_cache=_lowercase		)

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

									snake_case   :			Dict						    = outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									snake_case   :			Optional[Any]						    = ids_tensor((self.batch_size, 1)   ,	config.vocab_size		)

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

									snake_case   :			str						    = model(_lowercase		)["last_hidden_state"]
									snake_case   :			List[str]						    = model(_lowercase   ,	past_key_values=_lowercase		)["last_hidden_state"]

									# select random slice
									snake_case   :			Tuple						    = ids_tensor((1,)   ,	output_from_past.shape[-1]		).item()
									snake_case   :			List[Any]						    = output_from_no_past[:, -1, random_slice_idx].detach()
									snake_case   :			Any						    = 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 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				int   ,	)							->					int:
									snake_case   :			str						    = UMTaModel(config=_lowercase		).to(_lowercase		).half().eval()
									snake_case   :			Union[str, Any]						    = model(**_lowercase		)["last_hidden_state"]
									self.parent.assertFalse(torch.isnan(_lowercase		).any().item()		)



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



			def 				__lowercase					(			self							:				Tuple		)							->					Tuple:
									snake_case   :			Dict						    = UMTaModelTester(self		)



			@unittest.skip("Test has a segmentation fault on torch 1.8.0"		)
			def 				__lowercase					(			self							:				int		)							->					Optional[int]:
									snake_case   :			str						    = self.model_tester.prepare_config_and_inputs()
									snake_case   :			int						    = 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 				__lowercase					(			self							:				Dict		)							->					int:
									snake_case   :			int						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model_fpaa_forward(*_lowercase		)



			def 				__lowercase					(			self							:				Tuple		)							->					str:
									snake_case   :			int						    = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
									snake_case   :			int						    = self.model_tester.prepare_config_and_inputs()
									snake_case   :			Union[str, Any]						    = config_and_inputs[0]
									snake_case   :			Any						    = UMTaForConditionalGeneration(_lowercase		).eval()
									model.to(_lowercase		)

									snake_case   :			List[Any]						    = {
									    "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()		):
															snake_case   :			Optional[int]						    = {name: mask}
															# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
															if name == "head_mask":
																					snake_case   :			List[Any]						    = torch.ones(
																					    config.num_decoder_layers   ,	config.num_heads   ,	device=_lowercase		)

															snake_case   :			Optional[Any]						    = 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
															snake_case   :			int						    = 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 				__lowercase					(			self							:				Optional[int]		)							->					int:
									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 				__lowercase					(			self							:				List[str]		)							->					str:
									snake_case   :			str						    = UMTaForConditionalGeneration.from_pretrained("google/umt5-small"   ,	return_dict=_lowercase		).to(_lowercase		)
									snake_case   :			int						    = AutoTokenizer.from_pretrained("google/umt5-small"   ,	use_fast=_lowercase   ,	legacy=_lowercase		)
									snake_case   :			int						    = [
									    "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   :			Dict						    = tokenizer(_lowercase   ,	return_tensors="pt"   ,	padding=_lowercase		).input_ids
									# fmt: off
									snake_case   :			int						    = torch.tensor(
									    [
									        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
									        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
									        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
									    ]		)
									# fmt: on
									torch.testing.assert_allclose(_lowercase   ,	_lowercase		)

									snake_case   :			Dict						    = model.generate(input_ids.to(_lowercase		)		)
									snake_case   :			List[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>",
									]
									snake_case   :			Dict						    = tokenizer.batch_decode(_lowercase		)
									self.assertEqual(_lowercase   ,	_lowercase		)






from math import factorial, pi

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_sin() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   int					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_cos() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   str					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(maclaurin_sin(10))
			print(maclaurin_sin(-10))
			print(maclaurin_sin(10, 15))
			print(maclaurin_sin(-10, 15))

			print(maclaurin_cos(5))
			print(maclaurin_cos(-5))
			print(maclaurin_cos(10, 15))
			print(maclaurin_cos(-10, 15))