



"""simple docstring"""

# Copyright 2023 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 torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce




def __lowercase    (							_a					):
    return (torch.arange(state.num_processes					) + 1.0 + (state.num_processes * state.process_index)).to(state.device					)




def __lowercase    (							_a					):
    snake_case_				:  str       =			create_tensor(_a					)
    snake_case_				:  Tuple       =			gather(_a					)
    assert gathered_tensor.tolist() == list(range(1	,    state.num_processes**2 + 1					)					)




def __lowercase    (							_a					):
    snake_case_				:  List[Any]       =			[state.process_index]
    snake_case_				:  List[Any]       =			gather_object(_a					)
    assert len(_a					) == state.num_processes, f"{gathered_obj}, {len(_a					)} != {state.num_processes}"
    assert gathered_obj == list(range(state.num_processes					)					), f"{gathered_obj} != {list(range(state.num_processes					)					)}"




def __lowercase    (							_a					):
    snake_case_				:  Optional[int]       =			create_tensor(_a					)
    snake_case_				:  Tuple       =			broadcast(_a					)
    assert broadcasted_tensor.shape == torch.Size([state.num_processes]					)
    assert broadcasted_tensor.tolist() == list(range(1	,    state.num_processes + 1					)					)




def __lowercase    (							_a					):
    # We need to pad the tensor with one more element if we are the main process
    # to ensure that we can pad
    if state.is_main_process:
        snake_case_				:  Optional[int]       =			torch.arange(state.num_processes + 1					).to(state.device					)
    else:
        snake_case_				:  Union[str, Any]       =			torch.arange(state.num_processes					).to(state.device					)
    snake_case_				:  Tuple       =			pad_across_processes(_a					)
    assert padded_tensor.shape == torch.Size([state.num_processes + 1]					)
    if not state.is_main_process:
        assert padded_tensor.tolist() == list(range(0	,    state.num_processes					)					) + [0]




def __lowercase    (							_a					):
    # For now runs on only two processes
    if state.num_processes != 2:
        return
    snake_case_				:  int       =			create_tensor(_a					)
    snake_case_				:  Dict       =			reduce(_a	,    '''sum'''					)
    snake_case_				:  int       =			torch.tensor([4.0, 6]					).to(state.device					)
    assert torch.allclose(_a	,    _a					), f"{reduced_tensor} != {truth_tensor}"




def __lowercase    (							_a					):
    # For now runs on only two processes
    if state.num_processes != 2:
        return
    snake_case_				:  Any       =			create_tensor(_a					)
    snake_case_				:  Union[str, Any]       =			reduce(_a	,    '''mean'''					)
    snake_case_				:  List[Any]       =			torch.tensor([2.0, 3]					).to(state.device					)
    assert torch.allclose(_a	,    _a					), f"{reduced_tensor} != {truth_tensor}"




def __lowercase    (							_a					):
    # For xla_spawn (TPUs)
    main()




def __lowercase    (							):
    snake_case_				:  List[str]       =			PartialState()
    state.print(f"State: {state}"					)
    state.print('''testing gather'''					)
    test_gather(_a					)
    state.print('''testing gather_object'''					)
    test_gather_object(_a					)
    state.print('''testing broadcast'''					)
    test_broadcast(_a					)
    state.print('''testing pad_across_processes'''					)
    test_pad_across_processes(_a					)
    state.print('''testing reduce_sum'''					)
    test_reduce_sum(_a					)
    state.print('''testing reduce_mean'''					)
    test_reduce_mean(_a					)


if __name__ == "__main__":
     main()










"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[List[PIL.Image.Image], np.ndarray]
 _lowerCAmelCase     :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





