import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
    import os

    import jax.numpy as jnp
    from jax import jit

    from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
    from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

    __lowercase							 =					'''0.12'''  # assumed parallelism: 8


if is_torch_available():
    import torch



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None	):
    '''simple docstring'''







    if rng is None:
        __UpperCamelCase     :Union[str, Any]			  =		random.Random()

    __UpperCamelCase     :int			  =		1
    for dim in shape:
        total_dims *= dim

    __UpperCamelCase     :str			  =		[]
    for _ in range(SCREAMING_SNAKE_CASE	):
        values.append(rng.randint(0  ,			vocab_size - 1	)	)

    __UpperCamelCase     :List[str]			  =		np.array(SCREAMING_SNAKE_CASE  ,			dtype=jnp.intaa	).reshape(SCREAMING_SNAKE_CASE	)

    return output



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		ids_tensor(SCREAMING_SNAKE_CASE  ,			vocab_size=2  ,			rng=SCREAMING_SNAKE_CASE	)
    # make sure that at least one token is attended to for each batch
    __UpperCamelCase     :List[Any]			  =		1
    return attn_mask


@require_flax
class 	lowerCamelCase_     :






    '''simple docstring'''






    a__	:						List[Any]						=      None
    a__	:						Union[str, Any]						=      ()







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		self.model_tester.prepare_config_and_inputs_for_common()

        # cut to half length & take max batch_size 3
        __UpperCamelCase     :List[str]			  =		2
        __UpperCamelCase     :Tuple			  =		inputs['''input_ids'''].shape[-1] // 2
        __UpperCamelCase     :Optional[Any]			  =		inputs['''input_ids'''][:max_batch_size, :sequence_length]

        __UpperCamelCase     :str			  =		jnp.ones_like(__lowercase)
        __UpperCamelCase     :int			  =		attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        __UpperCamelCase     :Dict			  =		input_ids.shape[-1] + 5
        if config.eos_token_id is not None and config.pad_token_id is None:
            # hack to allow generate for models such as GPT2 as is done in `generate()`
            __UpperCamelCase     :List[str]			  =		config.eos_token_id
        return config, input_ids, attention_mask, max_length







    @is_pt_flax_cross_test
    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self._get_input_ids_and_config()
        __UpperCamelCase     :Any			  =		False
        __UpperCamelCase     :Tuple			  =		max_length
        __UpperCamelCase     :Optional[int]			  =		0

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :List[Any]			  =		model_class(__lowercase)

            __UpperCamelCase     :Dict			  =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
            __UpperCamelCase     :List[str]			  =		getattr(__lowercase , __lowercase)
            __UpperCamelCase     :Tuple			  =		pt_model_class(__lowercase).eval()
            __UpperCamelCase     :Tuple			  =		load_flax_weights_in_pytorch_model(__lowercase , flax_model.params)

            __UpperCamelCase     :int			  =		flax_model.generate(__lowercase).sequences
            __UpperCamelCase     :List[str]			  =		pt_model.generate(torch.tensor(__lowercase , dtype=torch.long))

            if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                __UpperCamelCase     :Any			  =		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

            self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist())







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		self._get_input_ids_and_config()
        __UpperCamelCase     :Tuple			  =		False
        __UpperCamelCase     :str			  =		max_length

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :Union[str, Any]			  =		model_class(__lowercase)

            __UpperCamelCase     :Optional[Any]			  =		model.generate(__lowercase).sequences
            self.assertEqual(generation_outputs.shape[-1] , __lowercase)

            __UpperCamelCase     :Dict			  =		jit(model.generate)
            __UpperCamelCase     :str			  =		jit_generate(__lowercase).sequences

            self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		self._get_input_ids_and_config()
        __UpperCamelCase     :int			  =		True
        __UpperCamelCase     :List[Any]			  =		max_length

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :List[str]			  =		model_class(__lowercase)

            __UpperCamelCase     :List[str]			  =		model.generate(__lowercase).sequences
            self.assertEqual(generation_outputs.shape[-1] , __lowercase)

            __UpperCamelCase     :int			  =		jit(model.generate)
            __UpperCamelCase     :List[Any]			  =		jit_generate(__lowercase).sequences

            self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		self._get_input_ids_and_config()
        __UpperCamelCase     :Any			  =		False
        __UpperCamelCase     :Any			  =		max_length
        __UpperCamelCase     :Any			  =		2

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :Optional[int]			  =		model_class(__lowercase)

            __UpperCamelCase     :Tuple			  =		model.generate(__lowercase).sequences
            self.assertEqual(generation_outputs.shape[-1] , __lowercase)

            __UpperCamelCase     :Optional[int]			  =		jit(model.generate)
            __UpperCamelCase     :Dict			  =		jit_generate(__lowercase).sequences

            self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self._get_input_ids_and_config()
        __UpperCamelCase     :Any			  =		False
        __UpperCamelCase     :Any			  =		max_length
        __UpperCamelCase     :int			  =		2
        __UpperCamelCase     :List[str]			  =		2

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :Any			  =		model_class(__lowercase)

            __UpperCamelCase     :Union[str, Any]			  =		model.generate(__lowercase).sequences
            self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		self._get_input_ids_and_config()
        __UpperCamelCase     :Tuple			  =		True
        __UpperCamelCase     :int			  =		max_length
        __UpperCamelCase     :Tuple			  =		0.8
        __UpperCamelCase     :Union[str, Any]			  =		10
        __UpperCamelCase     :List[Any]			  =		0.3
        __UpperCamelCase     :Any			  =		1
        __UpperCamelCase     :str			  =		8
        __UpperCamelCase     :Dict			  =		9

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :str			  =		model_class(__lowercase)

            __UpperCamelCase     :Any			  =		model.generate(__lowercase).sequences
            self.assertEqual(generation_outputs.shape[-1] , __lowercase)

            __UpperCamelCase     :Union[str, Any]			  =		jit(model.generate)
            __UpperCamelCase     :Union[str, Any]			  =		jit_generate(__lowercase).sequences

            self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self._get_input_ids_and_config()
        __UpperCamelCase     :int			  =		max_length
        __UpperCamelCase     :str			  =		1
        __UpperCamelCase     :Any			  =		8
        __UpperCamelCase     :str			  =		9

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :Union[str, Any]			  =		model_class(__lowercase)

            __UpperCamelCase     :str			  =		model.generate(__lowercase).sequences
            self.assertEqual(generation_outputs.shape[-1] , __lowercase)

            __UpperCamelCase     :List[Any]			  =		jit(model.generate)
            __UpperCamelCase     :Union[str, Any]			  =		jit_generate(__lowercase).sequences

            self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self._get_input_ids_and_config()
        __UpperCamelCase     :Union[str, Any]			  =		max_length
        __UpperCamelCase     :Union[str, Any]			  =		2
        __UpperCamelCase     :List[str]			  =		1
        __UpperCamelCase     :Tuple			  =		8
        __UpperCamelCase     :Optional[int]			  =		9

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :List[Any]			  =		model_class(__lowercase)

            __UpperCamelCase     :str			  =		model.generate(__lowercase).sequences
            self.assertEqual(generation_outputs.shape[-1] , __lowercase)

            __UpperCamelCase     :List[str]			  =		jit(model.generate)
            __UpperCamelCase     :Dict			  =		jit_generate(__lowercase).sequences

            self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		self._get_input_ids_and_config()

        # pad attention mask on the left
        __UpperCamelCase     :List[Any]			  =		attention_mask.at[(0, 0)].set(0)

        __UpperCamelCase     :int			  =		False
        __UpperCamelCase     :List[str]			  =		max_length

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :Tuple			  =		model_class(__lowercase)

            __UpperCamelCase     :int			  =		model.generate(__lowercase , attention_mask=__lowercase).sequences
            self.assertEqual(generation_outputs.shape[-1] , __lowercase)

            __UpperCamelCase     :Optional[Any]			  =		jit(model.generate)
            __UpperCamelCase     :str			  =		jit_generate(__lowercase , attention_mask=__lowercase).sequences

            self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		self._get_input_ids_and_config()

        # pad attention mask on the left
        __UpperCamelCase     :int			  =		attention_mask.at[(0, 0)].set(0)

        __UpperCamelCase     :int			  =		True
        __UpperCamelCase     :int			  =		max_length

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :Union[str, Any]			  =		model_class(__lowercase)

            __UpperCamelCase     :str			  =		model.generate(__lowercase , attention_mask=__lowercase).sequences
            self.assertEqual(generation_outputs.shape[-1] , __lowercase)

            __UpperCamelCase     :Any			  =		jit(model.generate)
            __UpperCamelCase     :int			  =		jit_generate(__lowercase , attention_mask=__lowercase).sequences

            self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		self._get_input_ids_and_config()

        # pad attention mask on the left
        __UpperCamelCase     :Dict			  =		attention_mask.at[(0, 0)].set(0)

        __UpperCamelCase     :Optional[Any]			  =		2
        __UpperCamelCase     :List[str]			  =		max_length

        for model_class in self.all_generative_model_classes:
            __UpperCamelCase     :Union[str, Any]			  =		model_class(__lowercase)

            __UpperCamelCase     :List[Any]			  =		model.generate(__lowercase , attention_mask=__lowercase).sequences
            self.assertEqual(generation_outputs.shape[-1] , __lowercase)

            __UpperCamelCase     :int			  =		jit(model.generate)
            __UpperCamelCase     :int			  =		jit_generate(__lowercase , attention_mask=__lowercase).sequences

            self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())


@require_flax
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Optional[int]			  =		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''')
        __UpperCamelCase     :Any			  =		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')

        __UpperCamelCase     :Optional[Any]			  =		'''Hello world'''
        __UpperCamelCase     :int			  =		tokenizer(__lowercase , return_tensors='''np''').input_ids

        # typos are quickly detected (the correct argument is `do_sample`)
        with self.assertRaisesRegex(__lowercase , '''do_samples'''):
            model.generate(__lowercase , do_samples=__lowercase)

        # arbitrary arguments that will not be used anywhere are also not accepted
        with self.assertRaisesRegex(__lowercase , '''foo'''):
            __UpperCamelCase     :List[Any]			  =		{'''foo''': '''bar'''}
            model.generate(__lowercase , **__lowercase)






import math

import qiskit



def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1	):
    '''simple docstring'''







    if (
        isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        or isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        or isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    ):
        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(SCREAMING_SNAKE_CASE	) != input_a)
        or (math.floor(SCREAMING_SNAKE_CASE	) != input_a)
        or (math.floor(SCREAMING_SNAKE_CASE	) != 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
    __UpperCamelCase     :List[str]			  =		qiskit.QuantumRegister(4  ,			'''qr'''	)
    __UpperCamelCase     :str			  =		qiskit.ClassicalRegister(2  ,			'''cr'''	)
    # list the entries
    __UpperCamelCase     :Tuple			  =		[input_a, input_a, carry_in]

    __UpperCamelCase     :Optional[int]			  =		qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    for i in range(0  ,			3	):
        if entry[i] == 2:
            quantum_circuit.h(SCREAMING_SNAKE_CASE	)  # for hadamard entries
        elif entry[i] == 1:
            quantum_circuit.x(SCREAMING_SNAKE_CASE	)  # for 1 entries
        elif entry[i] == 0:
            quantum_circuit.i(SCREAMING_SNAKE_CASE	)  # 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]  ,			SCREAMING_SNAKE_CASE	)  # measure the last two qbits

    __UpperCamelCase     :Optional[Any]			  =		qiskit.Aer.get_backend('''aer_simulator'''	)
    __UpperCamelCase     :Tuple			  =		qiskit.execute(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			shots=1_000	)

    return job.result().get_counts(SCREAMING_SNAKE_CASE	)


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





