




'''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			:    Dict				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Tuple=99				,     lowerCAmelCase_			:    int=13				,     lowerCAmelCase_			:    Tuple=7				,     lowerCAmelCase_			:    Tuple=9				,     lowerCAmelCase_			:    int=True				,     lowerCAmelCase_			:    Any=True				,     lowerCAmelCase_			:    Optional[int]=False				,     lowerCAmelCase_			:    List[str]=32				,     lowerCAmelCase_			:    Optional[int]=5				,     lowerCAmelCase_			:    Any=4				,     lowerCAmelCase_			:    Tuple=37				,     lowerCAmelCase_			:    Any=8				,     lowerCAmelCase_			:    int=0.1				,     lowerCAmelCase_			:    Any=0.0_0_2				,     lowerCAmelCase_			:    int=1				,     lowerCAmelCase_			:    Union[str, Any]=0				,     lowerCAmelCase_			:    List[Any]=0				,     lowerCAmelCase_			:    Tuple=None				,     lowerCAmelCase_			:    List[Any]=None				,     )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							parent
														_a      	=							batch_size
														_a      	=							encoder_seq_length
														_a      	=							decoder_seq_length
														# For common tests
														_a      	=							self.decoder_seq_length
														_a      	=							is_training
														_a      	=							use_attention_mask
														_a      	=							use_labels
														_a      	=							vocab_size
														_a      	=							hidden_size
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							d_ff
														_a      	=							relative_attention_num_buckets
														_a      	=							dropout_rate
														_a      	=							initializer_factor
														_a      	=							eos_token_id
														_a      	=							pad_token_id
														_a      	=							decoder_start_token_id
														_a      	=							None
														_a      	=							decoder_layers







							def   __lowerCAmelCase     (    self			:    str   )     ->				Union[str, Any]:


														"""simple docstring"""


														return TaConfig.from_pretrained('''google/umt5-base'''   )







							def   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    List[Any]=None				,     lowerCAmelCase_			:    Optional[Any]=None				,     lowerCAmelCase_			:    str=None				,     lowerCAmelCase_			:    str=None				,     lowerCAmelCase_			:    Union[str, Any]=None				,     )     ->				List[str]:


														"""simple docstring"""


														if attention_mask is None:
																					_a      	=							input_ids.ne(config.pad_token_id   )
														if decoder_attention_mask is None:
																					_a      	=							decoder_input_ids.ne(config.pad_token_id   )
														if head_mask is None:
																					_a      	=							torch.ones(config.num_hidden_layers				,     config.num_attention_heads				,     device=lowerCAmelCase_   )
														if decoder_head_mask is None:
																					_a      	=							torch.ones(config.num_decoder_layers				,     config.num_attention_heads				,     device=lowerCAmelCase_   )
														if cross_attn_head_mask is None:
																					_a      	=							torch.ones(
																					    config.num_decoder_layers				,     config.num_attention_heads				,     device=lowerCAmelCase_   )
														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			:    List[Any]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							ids_tensor([self.batch_size, self.encoder_seq_length]				,     self.vocab_size   )
														_a      	=							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
														_a      	=							input_ids.clamp(self.pad_token_id + 1   )
														_a      	=							decoder_input_ids.clamp(self.pad_token_id + 1   )

														_a      	=							self.get_config()
														_a      	=							config.num_attention_heads
														_a      	=							self.prepare_inputs_dict(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )
														return config, input_dict







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[int]:


														"""simple docstring"""


														_a	,				_a      	=							self.prepare_config_and_inputs()
														return config, inputs_dict







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Union[str, Any]:


														"""simple docstring"""


														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			:    int   )     ->				str:


														"""simple docstring"""


														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			:    str				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    List[Any]				,     )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							UMTaModel(config=lowerCAmelCase_   )
														model.to(lowerCAmelCase_   )
														model.eval()
														_a      	=							model(
														    input_ids=lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_				,     attention_mask=lowerCAmelCase_				,     decoder_attention_mask=lowerCAmelCase_				,     )
														_a      	=							model(input_ids=lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   )
														_a      	=							result.last_hidden_state
														_a      	=							result.past_key_values
														_a      	=							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(lowerCAmelCase_   )				,     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			:    Dict				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Tuple				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							UMTaModel(config=lowerCAmelCase_   ).get_decoder().to(lowerCAmelCase_   ).eval()
														# first forward pass
														_a      	=							model(lowerCAmelCase_				,     use_cache=lowerCAmelCase_   )
														_a      	=							model(lowerCAmelCase_   )
														_a      	=							model(lowerCAmelCase_				,     use_cache=lowerCAmelCase_   )

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

														_a	,				_a      	=							outputs.to_tuple()

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

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

														_a      	=							model(lowerCAmelCase_   )['''last_hidden_state''']
														_a      	=							model(lowerCAmelCase_				,     past_key_values=lowerCAmelCase_   )['''last_hidden_state''']

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

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







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    int				,     )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							UMTaModel(config=lowerCAmelCase_   ).to(lowerCAmelCase_   ).half().eval()
														_a      	=							model(**lowerCAmelCase_   )['''last_hidden_state''']
														self.parent.assertFalse(torch.isnan(lowerCAmelCase_   ).any().item()   )



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







							def   __lowerCAmelCase     (    self			:    str   )     ->				int:


														"""simple docstring"""


														_a      	=							UMTaModelTester(self   )







							@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''   )
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.model_tester.prepare_config_and_inputs()
														_a      	=							UMTaModel(config_and_inputs[0]   ).to(lowerCAmelCase_   )
														with tempfile.TemporaryDirectory() as tmpdirname:
																					torch.onnx.export(
																					    lowerCAmelCase_				,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				,     F'{tmpdirname}/t5_test.onnx'				,     export_params=lowerCAmelCase_				,     opset_version=9				,     input_names=['''input_ids''', '''decoder_input_ids''']				,     )







							@unittest.skipIf(torch_device == '''cpu'''				,     '''Cant do half precision'''   )
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
														_a      	=							self.model_tester.prepare_config_and_inputs()
														_a      	=							config_and_inputs[0]
														_a      	=							UMTaForConditionalGeneration(lowerCAmelCase_   ).eval()
														model.to(lowerCAmelCase_   )

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

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

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


														"""simple docstring"""


														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			:    List[str]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''				,     return_dict=lowerCAmelCase_   ).to(lowerCAmelCase_   )
														_a      	=							AutoTokenizer.from_pretrained('''google/umt5-small'''				,     use_fast=lowerCAmelCase_				,     legacy=lowerCAmelCase_   )
														_a      	=							[
														    '''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>.''',
														]
														_a      	=							tokenizer(lowerCAmelCase_				,     return_tensors='''pt'''				,     padding=lowerCAmelCase_   ).input_ids
														# fmt: off
														_a      	=							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(lowerCAmelCase_				,     lowerCAmelCase_   )

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







'''simple docstring'''


import qiskit
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							_a      	=							qiskit.Aer.get_backend('''aer_simulator''' )

							_a      	=							qiskit.QuantumCircuit(4					,		2 )
							# encode inputs in qubits 0 and 1
							if bita == 1:
														qc_ha.x(0 )
							if bita == 1:
														qc_ha.x(1 )
							qc_ha.barrier()

							# use cnots to write XOR of the inputs on qubit2
							qc_ha.cx(0					,		2 )
							qc_ha.cx(1					,		2 )

							# use ccx / toffoli gate to write AND of the inputs on qubit3
							qc_ha.ccx(0					,		1					,		3 )
							qc_ha.barrier()

							# extract outputs
							qc_ha.measure(2					,		0 )  # extract XOR value
							qc_ha.measure(3					,		1 )  # extract AND value

							# Execute the circuit on the qasm simulator
							_a      	=							qiskit.execute(UpperCamelCase					,		UpperCamelCase					,		shots=1000 )

							# Return the histogram data of the results of the experiment
							return job.result().get_counts(UpperCamelCase )


if __name__ == "__main__":
				_snake_case : Tuple  				=						half_adder(1, 1)
				print(F'''Half Adder Output Qubit Counts: {counts}''')

