






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 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						List[str]							,	__A  :						Dict							,	__A  :						Optional[int]=9_9							,	__A  :						Any=1_3							,	__A  :						Dict=7							,	__A  :						Tuple=9							,	__A  :						int=True							,	__A  :						Dict=True							,	__A  :						Tuple=False							,	__A  :						Dict=3_2							,	__A  :						List[Any]=5							,	__A  :						int=4							,	__A  :						Tuple=3_7							,	__A  :						Tuple=8							,	__A  :						int=0.1							,	__A  :						int=0.0_0_2							,	__A  :						int=1							,	__A  :						Tuple=0							,	__A  :						Dict=0							,	__A  :						List[Any]=None							,	__A  :						Optional[int]=None							,	):
						snake_case__				:   Any     =						parent
						snake_case__				:   int     =						batch_size
						snake_case__				:   Dict     =						encoder_seq_length
						snake_case__				:   Optional[Any]     =						decoder_seq_length
						# For common tests
						snake_case__				:   str     =						self.decoder_seq_length
						snake_case__				:   Any     =						is_training
						snake_case__				:   int     =						use_attention_mask
						snake_case__				:   Any     =						use_labels
						snake_case__				:   str     =						vocab_size
						snake_case__				:   Union[str, Any]     =						hidden_size
						snake_case__				:   Tuple     =						num_hidden_layers
						snake_case__				:   str     =						num_attention_heads
						snake_case__				:   List[Any]     =						d_ff
						snake_case__				:   List[str]     =						relative_attention_num_buckets
						snake_case__				:   str     =						dropout_rate
						snake_case__				:   int     =						initializer_factor
						snake_case__				:   Optional[Any]     =						eos_token_id
						snake_case__				:   Tuple     =						pad_token_id
						snake_case__				:   List[str]     =						decoder_start_token_id
						snake_case__				:   Optional[Any]     =						None
						snake_case__				:   List[str]     =						decoder_layers



					def  _lowercase  (      self  :						Union[str, Any] ):
						return TaConfig.from_pretrained("google/umt5-base" )



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

						snake_case__				:   Optional[Any]     =						self.get_config()
						snake_case__				:   List[str]     =						config.num_attention_heads
						snake_case__				:   Union[str, Any]     =						self.prepare_inputs_dict(__A							,	__A							,	__A )
						return config, input_dict



					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   int     =						self.prepare_config_and_inputs()
						return config, inputs_dict



					def  _lowercase  (      self  :						str ):
						return TaConfig(
						    vocab_size=1_6_6							,	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  :						Tuple ):
						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  :						List[str]							,	__A  :						Optional[int]							,	__A  :						Optional[int]							,	__A  :						List[Any]							,	__A  :						str							,	__A  :						Any							,	__A  :						List[str]							,	):
						snake_case__				:   List[Any]     =						UMTaModel(config=__A )
						model.to(__A )
						model.eval()
						snake_case__				:   List[Any]     =						model(
						    input_ids=__A							,	decoder_input_ids=__A							,	attention_mask=__A							,	decoder_attention_mask=__A							,	)
						snake_case__				:   List[str]     =						model(input_ids=__A							,	decoder_input_ids=__A )
						snake_case__				:   int     =						result.last_hidden_state
						snake_case__				:   Optional[Any]     =						result.past_key_values
						snake_case__				:   Optional[Any]     =						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  _lowercase  (      self  :						List[str]							,	__A  :						Tuple							,	__A  :						Dict							,	__A  :						int							,	__A  :						List[str]							,	__A  :						Optional[int]							,	__A  :						Any							,	):
						snake_case__				:   Optional[Any]     =						UMTaModel(config=__A ).get_decoder().to(__A ).eval()
						# first forward pass
						snake_case__				:   Tuple     =						model(__A							,	use_cache=__A )
						snake_case__				:   List[str]     =						model(__A )
						snake_case__				:   List[str]     =						model(__A							,	use_cache=__A )

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

						snake_case__				:   Union[str, Any]     =						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[Any]     =						torch.cat([input_ids, next_tokens]							,	dim=-1 )

						snake_case__				:   Tuple     =						model(__A )["last_hidden_state"]
						snake_case__				:   Tuple     =						model(__A							,	past_key_values=__A )["last_hidden_state"]

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



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






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



					def  _lowercase  (      self  :						int ):
						snake_case__				:   Tuple     =						UMTaModelTester(self )



					@unittest.skip("Test has a segmentation fault on torch 1.8.0" )
					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Optional[int]     =						self.model_tester.prepare_config_and_inputs()
						snake_case__				:   Tuple     =						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  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   Dict     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*__A )



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Union[str, Any]     =						["encoder_attentions", "decoder_attentions", "cross_attentions"]
						snake_case__				:   List[Any]     =						self.model_tester.prepare_config_and_inputs()
						snake_case__				:   List[Any]     =						config_and_inputs[0]
						snake_case__				:   Optional[int]     =						UMTaForConditionalGeneration(__A ).eval()
						model.to(__A )

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

							snake_case__				:   str     =						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
							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  :						List[str] ):
						pass



@require_torch
@require_sentencepiece
@require_tokenizers
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					@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  :						Dict ):
						snake_case__				:   List[Any]     =						UMTaForConditionalGeneration.from_pretrained("google/umt5-small"							,	return_dict=__A ).to(__A )
						snake_case__				:   Dict     =						AutoTokenizer.from_pretrained("google/umt5-small"							,	use_fast=__A							,	legacy=__A )
						snake_case__				:   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>.",
						]
						snake_case__				:   List[str]     =						tokenizer(__A							,	return_tensors="pt"							,	padding=__A ).input_ids
						# fmt: off
						snake_case__				:   Optional[int]     =						torch.tensor(
						    [
						        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
						    ] )
						# fmt: on
						torch.testing.assert_allclose(__A							,	__A )

						snake_case__				:   Dict     =						model.generate(input_ids.to(__A ) )
						snake_case__				:   Optional[int]     =						[
						    "<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__				:   str     =						tokenizer.batch_decode(__A )
						self.assertEqual(__A							,	__A )











import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							dict     ):
	return (data["data"], data["target"])







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							np.ndarray  ,						snake_case_      :							np.ndarray     ):
	snake_case__				:   Optional[int]     =						XGBClassifier()
	classifier.fit(snake_case_  ,						snake_case_     )
	return classifier







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						load_iris()
	snake_case__,		snake_case__				:   str     =						data_handling(snake_case_     )
	snake_case__,		snake_case__,		snake_case__,		snake_case__				:   int     =						train_test_split(
	    snake_case_  ,						snake_case_  ,						test_size=0.25     )

	snake_case__				:   Dict     =						iris["target_names"]

	# Create an XGBoost Classifier from the training data
	snake_case__				:   Dict     =						xgboost(snake_case_  ,						snake_case_     )

	# Display the confusion matrix of the classifier with both training and test sets
	ConfusionMatrixDisplay.from_estimator(
	    snake_case_  ,						snake_case_  ,						snake_case_  ,						display_labels=snake_case_  ,						cmap="Blues"  ,						normalize="true"  ,						)
	plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
	plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()



