





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def __init__(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			data




    def __iter__(       self     ):





        '''simple docstring'''

        for element in self.data:
            yield element





def 							__snake_case							(    lowerCAmelCase_=True	)				->						int:
    SCREAMING_SNAKE_CASE__											=			Accelerator(even_batches=lowerCAmelCase_	)
    assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
    return accelerator


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = False	)				->						Union[str, Any]:
    if iterable:
        SCREAMING_SNAKE_CASE__											=			DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase_	)	)	)
    else:
        SCREAMING_SNAKE_CASE__											=			TensorDataset(torch.as_tensor(range(lowerCAmelCase_	)	)	)

    SCREAMING_SNAKE_CASE__											=			DataLoader(lowerCAmelCase_ ,							batch_size=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			accelerator.prepare(lowerCAmelCase_	)

    return dl


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			create_dataloader(accelerator=lowerCAmelCase_ ,							dataset_size=lowerCAmelCase_ ,							batch_size=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			[len(batch[0]	) for batch in dl]

    if accelerator.process_index == 0:
        assert batch_sizes == process_0_expected_batch_sizes
    elif accelerator.process_index == 1:
        assert batch_sizes == process_1_expected_batch_sizes


def 							__snake_case							(    )				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			create_accelerator()

    # without padding, we would expect a different number of batches
    verify_dataloader_batch_sizes(
        lowerCAmelCase_ ,							dataset_size=3 ,							batch_size=1 ,							process_0_expected_batch_sizes=[1, 1] ,							process_1_expected_batch_sizes=[1, 1] ,							)

    # without padding, we would expect the same number of batches, but different sizes
    verify_dataloader_batch_sizes(
        lowerCAmelCase_ ,							dataset_size=7 ,							batch_size=2 ,							process_0_expected_batch_sizes=[2, 2] ,							process_1_expected_batch_sizes=[2, 2] ,							)


def 							__snake_case							(    )				->						str:
    SCREAMING_SNAKE_CASE__											=			create_accelerator(even_batches=lowerCAmelCase_	)

    verify_dataloader_batch_sizes(
        lowerCAmelCase_ ,							dataset_size=3 ,							batch_size=1 ,							process_0_expected_batch_sizes=[1, 1] ,							process_1_expected_batch_sizes=[1] ,							)

    verify_dataloader_batch_sizes(
        lowerCAmelCase_ ,							dataset_size=7 ,							batch_size=2 ,							process_0_expected_batch_sizes=[2, 2] ,							process_1_expected_batch_sizes=[2, 1] ,							)


def 							__snake_case							(    )				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			create_accelerator(even_batches=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			torch.nn.Linear(1 ,							1	)
    SCREAMING_SNAKE_CASE__											=			accelerator.prepare(lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			create_dataloader(lowerCAmelCase_ ,							dataset_size=3 ,							batch_size=1	)

    SCREAMING_SNAKE_CASE__											=			[]
    with accelerator.join_uneven_inputs([ddp_model]	):
        for batch_idx, batch in enumerate(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			ddp_model(batch[0].float()	)
            SCREAMING_SNAKE_CASE__											=			output.sum()
            loss.backward()
            batch_idxs.append(lowerCAmelCase_	)

    accelerator.wait_for_everyone()

    if accelerator.process_index == 0:
        assert batch_idxs == [0, 1]
    elif accelerator.process_index == 1:
        assert batch_idxs == [0]


def 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    with warnings.catch_warnings(record=lowerCAmelCase_	) as w:
        with accelerator.join_uneven_inputs([Mock()]	):
            pass

        assert issubclass(w[-1].category ,							lowerCAmelCase_	)
        assert "only supported for multi-GPU" in str(w[-1].message	)


def 							__snake_case							(    )				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			True
    SCREAMING_SNAKE_CASE__											=			False
    SCREAMING_SNAKE_CASE__											=			create_accelerator(even_batches=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			torch.nn.Linear(1 ,							1	)
    SCREAMING_SNAKE_CASE__											=			accelerator.prepare(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			create_dataloader(lowerCAmelCase_ ,							dataset_size=3 ,							batch_size=1	)
    SCREAMING_SNAKE_CASE__											=			create_dataloader(lowerCAmelCase_ ,							dataset_size=3 ,							batch_size=1	)

    with accelerator.join_uneven_inputs([ddp_model] ,							even_batches=lowerCAmelCase_	):
        SCREAMING_SNAKE_CASE__											=			train_dl.batch_sampler.even_batches
        SCREAMING_SNAKE_CASE__											=			valid_dl.batch_sampler.even_batches

    assert train_dl_overridden_value == overridden_even_batches
    assert valid_dl_overridden_value == overridden_even_batches
    assert train_dl.batch_sampler.even_batches == default_even_batches
    assert valid_dl.batch_sampler.even_batches == default_even_batches


def 							__snake_case							(    )				->						str:
    SCREAMING_SNAKE_CASE__											=			True
    SCREAMING_SNAKE_CASE__											=			False
    SCREAMING_SNAKE_CASE__											=			create_accelerator(even_batches=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			torch.nn.Linear(1 ,							1	)
    SCREAMING_SNAKE_CASE__											=			accelerator.prepare(lowerCAmelCase_	)
    create_dataloader(lowerCAmelCase_ ,							dataset_size=3 ,							batch_size=1 ,							iterable=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			create_dataloader(lowerCAmelCase_ ,							dataset_size=3 ,							batch_size=1	)

    with warnings.catch_warnings():
        warnings.filterwarnings('''ignore'''	)
        try:
            with accelerator.join_uneven_inputs([ddp_model] ,							even_batches=lowerCAmelCase_	):
                SCREAMING_SNAKE_CASE__											=			batch_dl.batch_sampler.even_batches
        except AttributeError:
            # ensure attribute error is not raised when processing iterable dl
            raise AssertionError

    assert batch_dl_overridden_value == overridden_even_batches
    assert batch_dl.batch_sampler.even_batches == default_even_batches


def 							__snake_case							(    )				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			create_accelerator()
    SCREAMING_SNAKE_CASE__											=			torch.nn.Linear(1 ,							1	)
    SCREAMING_SNAKE_CASE__											=			accelerator.prepare(lowerCAmelCase_	)
    create_dataloader(lowerCAmelCase_ ,							dataset_size=3 ,							batch_size=1 ,							iterable=lowerCAmelCase_	)

    with warnings.catch_warnings(record=lowerCAmelCase_	) as w:
        with accelerator.join_uneven_inputs([ddp_model] ,							even_batches=lowerCAmelCase_	):
            pass

        assert issubclass(w[-1].category ,							lowerCAmelCase_	)
        assert "only supported for map-style datasets" in str(w[-1].message	)


def 							__snake_case							(    )				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			create_accelerator()

    accelerator.print('''Test that even_batches variable ensures uniform batches across processes'''	)
    test_default_ensures_even_batch_sizes()

    accelerator.print('''Run tests with even_batches disabled'''	)
    test_can_disable_even_batches()

    accelerator.print('''Test joining uneven inputs'''	)
    test_can_join_uneven_inputs()

    accelerator.print('''Test overriding even_batches when joining uneven inputs'''	)
    test_join_can_override_even_batches()

    accelerator.print('''Test overriding even_batches for mixed dataloader types'''	)
    test_join_can_override_for_mixed_type_dataloaders()

    accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders'''	)
    test_join_raises_warning_for_iterable_when_overriding_even_batches()

    accelerator.print('''Test join with non DDP distributed raises warning'''	)
    SCREAMING_SNAKE_CASE__											=			accelerator.state.distributed_type
    SCREAMING_SNAKE_CASE__											=			DistributedType.FSDP
    test_join_raises_warning_for_non_ddp_distributed(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			original_state


if __name__ == "__main__":
  main()


'''simple docstring'''





import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def snake_case_     (					lowercase__ = 3		):
  if isinstance(lowercase__   ,       lowercase__		):
    raise TypeError("number of qubits must be a integer."		)
  if number_of_qubits <= 0:
    raise ValueError("number of qubits must be > 0."		)
  if math.floor(lowercase__		) != number_of_qubits:
    raise ValueError("number of qubits must be exact integer."		)
  if number_of_qubits > 1_0:
    raise ValueError("number of qubits too large to simulate(>10)."		)

  UpperCAmelCase__      :					Optional[int]				       =							QuantumRegister(lowercase__   ,       "qr"		)
  UpperCAmelCase__      :					List[Any]				       =							ClassicalRegister(lowercase__   ,       "cr"		)

  UpperCAmelCase__      :					List[str]				       =							QuantumCircuit(lowercase__   ,       lowercase__		)

  UpperCAmelCase__      :					Union[str, Any]				       =							number_of_qubits

  for i in range(lowercase__		):
    quantum_circuit.h(number_of_qubits - i - 1		)
    counter -= 1
    for j in range(lowercase__		):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)   ,       lowercase__   ,       lowercase__		)

  for k in range(number_of_qubits // 2		):
    quantum_circuit.swap(lowercase__   ,       number_of_qubits - k - 1		)

  # measure all the qubits
  quantum_circuit.measure(lowercase__   ,       lowercase__		)
  # simulate with 10000 shots
  UpperCAmelCase__      :					Union[str, Any]				       =							Aer.get_backend("qasm_simulator"		)
  UpperCAmelCase__      :					Optional[int]				       =							execute(lowercase__   ,       lowercase__   ,       shots=1_0_0_0_0		)

  return job.result().get_counts(lowercase__		)


if __name__ == "__main__":
      print(
          F'Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'
      )



