




'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.xglm.modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						)



@require_tf
class 		SCREAMING_SNAKE_CASE__  :
			__SCREAMING_SNAKE_CASE    =			XGLMConfig
			__SCREAMING_SNAKE_CASE    =			{}
			__SCREAMING_SNAKE_CASE    =			"""gelu"""





			def __init__(      self						:    int      ,		a_						:    List[Any]      ,		a_						:    Dict=14      ,		a_						:    int=7      ,		a_						:    List[Any]=True      ,		a_						:    Optional[int]=True      ,		a_						:    Optional[int]=True      ,		a_						:    int=99      ,		a_						:    Optional[int]=32      ,		a_						:    Tuple=2      ,		a_						:    Any=4      ,		a_						:    Union[str, Any]=37      ,		a_						:    Any="gelu"      ,		a_						:    str=0.1      ,		a_						:    Any=0.1      ,		a_						:    str=512      ,		a_						:    List[Any]=0.02      ,		):



						"""simple docstring"""




						__snake_case			=		parent
						__snake_case			=		batch_size
						__snake_case			=		seq_length
						__snake_case			=		is_training
						__snake_case			=		use_input_mask
						__snake_case			=		use_labels
						__snake_case			=		vocab_size
						__snake_case			=		d_model
						__snake_case			=		num_hidden_layers
						__snake_case			=		num_attention_heads
						__snake_case			=		ffn_dim
						__snake_case			=		activation_function
						__snake_case			=		activation_dropout
						__snake_case			=		attention_dropout
						__snake_case			=		max_position_embeddings
						__snake_case			=		initializer_range
						__snake_case			=		None
						__snake_case			=		0
						__snake_case			=		2
						__snake_case			=		1





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						return XGLMConfig.from_pretrained("facebook/xglm-564M"							)





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		tf.clip_by_value(
						    ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size							)      ,		clip_value_min=0      ,		clip_value_max=3							)

						__snake_case			=		None
						if self.use_input_mask:
									__snake_case			=		random_attention_mask([self.batch_size, self.seq_length]							)

						__snake_case			=		self.get_config()

						__snake_case			=		floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,		2							)

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						)





			def 							A (      self						:    str							):



						"""simple docstring"""




						return XGLMConfig(
						    vocab_size=self.vocab_size      ,		d_model=self.hidden_size      ,		num_layers=self.num_hidden_layers      ,		attention_heads=self.num_attention_heads      ,		ffn_dim=self.ffn_dim      ,		activation_function=self.activation_function      ,		activation_dropout=self.activation_dropout      ,		attention_dropout=self.attention_dropout      ,		max_position_embeddings=self.max_position_embeddings      ,		initializer_range=self.initializer_range      ,		use_cache=a_      ,		bos_token_id=self.bos_token_id      ,		eos_token_id=self.eos_token_id      ,		pad_token_id=self.pad_token_id      ,		return_dict=a_      ,		)






			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		self.prepare_config_and_inputs()

						(
						    (
						    __snake_case
						)		,     (
						    __snake_case
						)		,     (
						    __snake_case
						)		,     (
						    __snake_case
						)		,     
						)			=		config_and_inputs

						__snake_case			=		{
						    "input_ids": input_ids,
						    "head_mask": head_mask,
						}

						return config, inputs_dict







@require_tf
class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     _UpperCamelCase				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			__SCREAMING_SNAKE_CASE    =			(TFXGLMForCausalLM,) if is_tf_available() else ()
			__SCREAMING_SNAKE_CASE    =			(
			    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
			)
			__SCREAMING_SNAKE_CASE    =			False
			__SCREAMING_SNAKE_CASE    =			False
			__SCREAMING_SNAKE_CASE    =			False





			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		TFXGLMModelTester(self							)
						__snake_case			=		ConfigTester(self      ,		config_class=a_      ,		n_embd=37							)





			def 							A (      self						:    str							):



						"""simple docstring"""




						self.config_tester.run_common_tests()





			@slow
			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__snake_case			=		TFXGLMModel.from_pretrained(a_							)
									self.assertIsNotNone(a_							)






			@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."							)
			def 							A (      self						:    Dict							):



						"""simple docstring"""




						super().test_resize_token_embeddings()







@require_tf
class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			@slow
			def 							A (      self						:    List[Any]      ,		a_						:    Dict=True							):



						"""simple docstring"""




						__snake_case			=		TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"							)
						__snake_case			=		tf.convert_to_tensor([[2, 268, 9_865]]      ,		dtype=tf.intaa							)  # The dog
						# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
						# fmt: off
						__snake_case			=		[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
						# fmt: on
						__snake_case			=		model.generate(a_      ,		do_sample=a_      ,		num_beams=1							)
						if verify_outputs:
									self.assertListEqual(output_ids[0].numpy().tolist()      ,		a_							)





			@slow
			def 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		XGLMTokenizer.from_pretrained("facebook/xglm-564M"							)
						__snake_case			=		TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"							)

						tf.random.set_seed(0							)
						__snake_case			=		tokenizer("Today is a nice day and"      ,		return_tensors="tf"							)
						__snake_case			=		tokenized.input_ids
						# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
						with tf.device(":/CPU:0"							):
									__snake_case			=		model.generate(a_      ,		do_sample=a_      ,		seed=[7, 0]							)
						__snake_case			=		tokenizer.decode(output_ids[0]      ,		skip_special_tokens=a_							)

						__snake_case			=		(
						    "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
						)
						self.assertEqual(a_      ,		a_							)






			@slow
			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"							)
						__snake_case			=		XGLMTokenizer.from_pretrained("facebook/xglm-564M"							)

						__snake_case			=		"left"

						# use different length sentences to test batching
						__snake_case			=		[
						    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
						    "left-padding, such as in batched generation. The output for the sequence below should be the same "
						    "regardless of whether left padding is applied or not. When",
						    "Hello, my dog is a little",
						]

						__snake_case			=		tokenizer(a_      ,		return_tensors="tf"      ,		padding=a_							)
						__snake_case			=		inputs["input_ids"]

						__snake_case			=		model.generate(input_ids=a_      ,		attention_mask=inputs["attention_mask"]      ,		max_new_tokens=12							)

						__snake_case			=		tokenizer(sentences[0]      ,		return_tensors="tf"							).input_ids
						__snake_case			=		model.generate(input_ids=a_      ,		max_new_tokens=12							)

						__snake_case			=		tokenizer(sentences[1]      ,		return_tensors="tf"							).input_ids
						__snake_case			=		model.generate(input_ids=a_      ,		max_new_tokens=12							)

						__snake_case			=		tokenizer.batch_decode(a_      ,		skip_special_tokens=a_							)
						__snake_case			=		tokenizer.decode(output_non_padded[0]      ,		skip_special_tokens=a_							)
						__snake_case			=		tokenizer.decode(output_padded[0]      ,		skip_special_tokens=a_							)

						__snake_case			=		[
						    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
						    "left-padding, such as in batched generation. The output for the sequence below should be the same "
						    "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
						    "a single",
						    "Hello, my dog is a little bit of a shy one, but he is very friendly",
						]
						self.assertListEqual(a_      ,		a_							)
						self.assertListEqual(a_      ,		[non_padded_sentence, padded_sentence]							)









'''simple docstring'''
import math

import qiskit

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1							,    __UpperCamelCase					:       int = 1		)					->	qiskit.result.counts.Counts:







     """simple docstring"""
     if (
         isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
         or isinstance(__UpperCamelCase							,    __UpperCamelCase		)
     ):
          raise TypeError('inputs must be integers.'		)

     if (input_a < 0) or (input_a < 0) or (carry_in < 0):
          raise ValueError('inputs must be positive.'		)

     if (
         (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != input_a)
         or (math.floor(__UpperCamelCase		) != carry_in)
     ):
          raise ValueError('inputs must be exact integers.'		)

     if (input_a > 2) or (input_a > 2) or (carry_in > 2):
          raise ValueError('inputs must be less or equal to 2.'		)

     # build registers
     _A				=							qiskit.QuantumRegister(4							,    'qr'		)
     _A				=							qiskit.ClassicalRegister(2							,    'cr'		)
     # list the entries
     _A				=							[input_a, input_a, carry_in]

     _A				=							qiskit.QuantumCircuit(__UpperCamelCase							,    __UpperCamelCase		)

     for i in range(0							,    3		):
          if entry[i] == 2:
               quantum_circuit.h(__UpperCamelCase		)  # for hadamard entries
          elif entry[i] == 1:
               quantum_circuit.x(__UpperCamelCase		)  # for 1 entries
          elif entry[i] == 0:
               quantum_circuit.i(__UpperCamelCase		)  # for 0 entries

    # build the circuit
     quantum_circuit.ccx(0							,    1							,    3		)  # ccx = toffoli gate
     quantum_circuit.cx(0							,    1		)
     quantum_circuit.ccx(1							,    2							,    3		)
     quantum_circuit.cx(1							,    2		)
     quantum_circuit.cx(0							,    1		)

     quantum_circuit.measure([2, 3]							,    __UpperCamelCase		)  # measure the last two qbits

     _A				=							qiskit.Aer.get_backend('aer_simulator'		)
     _A				=							qiskit.execute(__UpperCamelCase							,    __UpperCamelCase							,    shots=1_0_0_0		)

     return job.result().get_counts(__UpperCamelCase		)


if __name__ == "__main__":
    print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')





