



# 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 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

 return (torch.arange(state.num_processes					) + 1.0 + (state.num_processes * state.process_index)).to(state.device					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int]					):






 '''simple docstring'''

 a						=		create_tensor(UpperCAmelCase__					)
 a						=		gather(UpperCAmelCase__					)
 assert gathered_tensor.tolist() == list(range(1 ,					state.num_processes**2 + 1					)					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str]					):






 '''simple docstring'''

 a						=		[state.process_index]
 a						=		gather_object(UpperCAmelCase__					)
 assert len(UpperCAmelCase__					) == state.num_processes, F"""{gathered_obj}, {len(UpperCAmelCase__					)} != {state.num_processes}"""
 assert gathered_obj == list(range(state.num_processes					)					), F"""{gathered_obj} != {list(range(state.num_processes					)					)}"""


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

 a						=		create_tensor(UpperCAmelCase__					)
 a						=		broadcast(UpperCAmelCase__					)
 assert broadcasted_tensor.shape == torch.Size([state.num_processes]					)
 assert broadcasted_tensor.tolist() == list(range(1 ,					state.num_processes + 1					)					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[Any]					):






 '''simple docstring'''

 if state.is_main_process:
  a						=		torch.arange(state.num_processes + 1					).to(state.device					)
 else:
  a						=		torch.arange(state.num_processes					).to(state.device					)
 a						=		pad_across_processes(UpperCAmelCase__					)
 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 	UpperCAmelCase__      (      UpperCAmelCase__ :List[Any]					):






 '''simple docstring'''

 if state.num_processes != 2:
  return
 a						=		create_tensor(UpperCAmelCase__					)
 a						=		reduce(UpperCAmelCase__ ,					"sum"					)
 a						=		torch.tensor([4.0, 6]					).to(state.device					)
 assert torch.allclose(UpperCAmelCase__ ,					UpperCAmelCase__					), F"""{reduced_tensor} != {truth_tensor}"""


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[Any]					):






 '''simple docstring'''

 if state.num_processes != 2:
  return
 a						=		create_tensor(UpperCAmelCase__					)
 a						=		reduce(UpperCAmelCase__ ,					"mean"					)
 a						=		torch.tensor([2.0, 3]					).to(state.device					)
 assert torch.allclose(UpperCAmelCase__ ,					UpperCAmelCase__					), F"""{reduced_tensor} != {truth_tensor}"""


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any					):






 '''simple docstring'''

 main()


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		PartialState()
 state.print(F"""State: {state}"""					)
 state.print("testing gather"					)
 test_gather(UpperCAmelCase__					)
 state.print("testing gather_object"					)
 test_gather_object(UpperCAmelCase__					)
 state.print("testing broadcast"					)
 test_broadcast(UpperCAmelCase__					)
 state.print("testing pad_across_processes"					)
 test_pad_across_processes(UpperCAmelCase__					)
 state.print("testing reduce_sum"					)
 test_reduce_sum(UpperCAmelCase__					)
 state.print("testing reduce_mean"					)
 test_reduce_mean(UpperCAmelCase__					)


if __name__ == "__main__":
     main()






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel

