





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        snake_case_		:







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







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







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

				lowerCamelCase   : Optional[Any]								=self.get_config()
				lowerCamelCase   : Optional[Any]								=config.num_attention_heads
				lowerCamelCase   : Any								=self.prepare_inputs_dict(__lowercase			,     __lowercase			,     __lowercase   )
				return config, input_dict







		def 			__lowercase   (		self   )				->   List[Any]:
				lowerCamelCase   : List[str]								=self.prepare_config_and_inputs()
				return config, inputs_dict







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

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

				lowerCamelCase   : Any								=outputs.to_tuple()

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

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

				lowerCamelCase   : str								=model(__lowercase   )['''last_hidden_state''']
				lowerCamelCase   : Optional[Any]								=model(__lowercase			,     past_key_values=__lowercase   )['''last_hidden_state''']

				# select random slice
				lowerCamelCase   : Optional[int]								=ids_tensor((1,)			,     output_from_past.shape[-1]   ).item()
				lowerCamelCase   : Optional[Any]								=output_from_no_past[:, -1, random_slice_idx].detach()
				lowerCamelCase   : Dict								=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			,     __lowercase			,     __lowercase			,     )				->   Any:
				lowerCamelCase   : Any								=UMTaModel(config=__lowercase   ).to(__lowercase   ).half().eval()
				lowerCamelCase   : Optional[int]								=model(**__lowercase   )['''last_hidden_state''']
				self.parent.assertFalse(torch.isnan(__lowercase   ).any().item()   )

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







		def 			__lowercase   (		self   )				->   Optional[Any]:
				lowerCamelCase   : Any								=UMTaModelTester(self   )







		@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''   )
		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : Union[str, Any]								=self.model_tester.prepare_config_and_inputs()
				lowerCamelCase   : Optional[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   )				->   Optional[int]:
				lowerCamelCase   : Tuple								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*__lowercase   )







		def 			__lowercase   (		self   )				->   str:
				lowerCamelCase   : Optional[Any]								=['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
				lowerCamelCase   : List[Any]								=self.model_tester.prepare_config_and_inputs()
				lowerCamelCase   : str								=config_and_inputs[0]
				lowerCamelCase   : List[str]								=UMTaForConditionalGeneration(__lowercase   ).eval()
				model.to(__lowercase   )

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

						lowerCamelCase   : int								=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   : 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]:
				pass

@require_torch
@require_sentencepiece
@require_tokenizers
class        snake_case_		(			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   )				->   int:
				lowerCamelCase   : List[Any]								=UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''			,     return_dict=__lowercase   ).to(__lowercase   )
				lowerCamelCase   : List[Any]								=AutoTokenizer.from_pretrained('''google/umt5-small'''			,     use_fast=__lowercase			,     legacy=__lowercase   )
				lowerCamelCase   : Optional[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   : Dict								=tokenizer(__lowercase			,     return_tensors='''pt'''			,     padding=__lowercase   ).input_ids
				# fmt: off
				lowerCamelCase   : str								=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(__lowercase			,     __lowercase   )

				lowerCamelCase   : Optional[int]								=model.generate(input_ids.to(__lowercase   )   )
				lowerCamelCase   : List[str]								=[
				    '''<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   : Union[str, Any]								=tokenizer.batch_decode(__lowercase   )
				self.assertEqual(__lowercase			,     __lowercase   )








import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     tuple:
		return (data["data"], data["target"])


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     np.ndarray:
		lowerCamelCase   : Dict								=XGBRegressor(verbosity=0							,       random_state=4_2		)
		xgb.fit(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		# Predict target for test data
		lowerCamelCase   : List[str]								=xgb.predict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : List[Any]								=predictions.reshape(len(SCREAMING_SNAKE_CASE_		)							,       1		)
		return predictions


def 					A__	(       )   ->     None:
		lowerCamelCase   : Union[str, Any]								=fetch_california_housing()
		lowerCamelCase		,   lowerCamelCase   : Optional[Any]								=data_handling(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : str								=train_test_split(
		    SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       test_size=0.2_5							,       random_state=1		)
		lowerCamelCase   : str								=xgboost(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		# Error printing
		print(F"Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}"		)
		print(F"Mean Square Error  : {mean_squared_error(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)}"		)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

