
"""simple docstring"""


# 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   __a  (      _lowerCAmelCase	):






       def __init__(  self :							Union[str, Any]       ,      UpperCAmelCase_ :							List[str]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		data






       def __iter__(  self :							List[str]			)->  str:







              """simple docstring"""
              for element in self.data:
                     yield element





def 	lowerCamelCase__			(							UpperCAmelCase_=True				)->							Optional[Any]:







       """simple docstring"""
       UpperCamelCase					 =		Accelerator(even_batches=UpperCAmelCase_				)
       assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
       return accelerator



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_ = False				)->							Optional[Any]:







       """simple docstring"""
       if iterable:
              UpperCamelCase					 =		DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase_				)				)				)
       else:
              UpperCamelCase					 =		TensorDataset(torch.as_tensor(range(UpperCAmelCase_				)				)				)

       UpperCamelCase					 =		DataLoader(UpperCAmelCase_		,      batch_size=UpperCAmelCase_				)
       UpperCamelCase					 =		accelerator.prepare(UpperCAmelCase_				)

       return dl



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      )->							int:







       """simple docstring"""
       UpperCamelCase					 =		create_dataloader(accelerator=UpperCAmelCase_		,      dataset_size=UpperCAmelCase_		,      batch_size=UpperCAmelCase_				)

       UpperCamelCase					 =		[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 	lowerCamelCase__			(							)->							List[Any]:







       """simple docstring"""
       UpperCamelCase					 =		create_accelerator()

       # without padding, we would expect a different number of batches
       verify_dataloader_batch_sizes(
           UpperCAmelCase_		,      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(
           UpperCAmelCase_		,      dataset_size=7		,      batch_size=2		,      process_0_expected_batch_sizes=[2, 2]		,      process_1_expected_batch_sizes=[2, 2]		,      )



def 	lowerCamelCase__			(							)->							List[str]:







       """simple docstring"""
       UpperCamelCase					 =		create_accelerator(even_batches=UpperCAmelCase_				)

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

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



def 	lowerCamelCase__			(							)->							Tuple:







       """simple docstring"""
       UpperCamelCase					 =		create_accelerator(even_batches=UpperCAmelCase_				)

       UpperCamelCase					 =		torch.nn.Linear(1		,      1				)
       UpperCamelCase					 =		accelerator.prepare(UpperCAmelCase_				)

       UpperCamelCase					 =		create_dataloader(UpperCAmelCase_		,      dataset_size=3		,      batch_size=1				)

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

       accelerator.wait_for_everyone()

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



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       with warnings.catch_warnings(record=UpperCAmelCase_				) as w:
              with accelerator.join_uneven_inputs([Mock()]				):
                     pass

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



def 	lowerCamelCase__			(							)->							Tuple:







       """simple docstring"""
       UpperCamelCase					 =		True
       UpperCamelCase					 =		False
       UpperCamelCase					 =		create_accelerator(even_batches=UpperCAmelCase_				)
       UpperCamelCase					 =		torch.nn.Linear(1		,      1				)
       UpperCamelCase					 =		accelerator.prepare(UpperCAmelCase_				)
       UpperCamelCase					 =		create_dataloader(UpperCAmelCase_		,      dataset_size=3		,      batch_size=1				)
       UpperCamelCase					 =		create_dataloader(UpperCAmelCase_		,      dataset_size=3		,      batch_size=1				)

       with accelerator.join_uneven_inputs([ddp_model]		,      even_batches=UpperCAmelCase_				):
              UpperCamelCase					 =		train_dl.batch_sampler.even_batches
              UpperCamelCase					 =		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 	lowerCamelCase__			(							)->							str:







       """simple docstring"""
       UpperCamelCase					 =		True
       UpperCamelCase					 =		False
       UpperCamelCase					 =		create_accelerator(even_batches=UpperCAmelCase_				)
       UpperCamelCase					 =		torch.nn.Linear(1		,      1				)
       UpperCamelCase					 =		accelerator.prepare(UpperCAmelCase_				)
       create_dataloader(UpperCAmelCase_		,      dataset_size=3		,      batch_size=1		,      iterable=UpperCAmelCase_				)
       UpperCamelCase					 =		create_dataloader(UpperCAmelCase_		,      dataset_size=3		,      batch_size=1				)

       with warnings.catch_warnings():
              warnings.filterwarnings("ignore"				)
              try:
                     with accelerator.join_uneven_inputs([ddp_model]		,      even_batches=UpperCAmelCase_				):
                            UpperCamelCase					 =		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 	lowerCamelCase__			(							)->							str:







       """simple docstring"""
       UpperCamelCase					 =		create_accelerator()
       UpperCamelCase					 =		torch.nn.Linear(1		,      1				)
       UpperCamelCase					 =		accelerator.prepare(UpperCAmelCase_				)
       create_dataloader(UpperCAmelCase_		,      dataset_size=3		,      batch_size=1		,      iterable=UpperCAmelCase_				)

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

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



def 	lowerCamelCase__			(							)->							Dict:







       """simple docstring"""
       UpperCamelCase					 =		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"				)
       UpperCamelCase					 =		accelerator.state.distributed_type
       UpperCamelCase					 =		DistributedType.FSDP
       test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase_				)
       UpperCamelCase					 =		original_state


if __name__ == "__main__":
      main()







"""simple docstring"""


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

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)



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


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .pipeline_cycle_diffusion import CycleDiffusionPipeline
      from .pipeline_stable_diffusion import StableDiffusionPipeline
      from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
      from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
      from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
      from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
      from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
      from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
      from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
      from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
      from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
      from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
      from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
      from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
      from .pipeline_stable_unclip import StableUnCLIPPipeline
      from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
      from .safety_checker import StableDiffusionSafetyChecker
      from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

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 StableDiffusionImageVariationPipeline
else:
      from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
      if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          StableDiffusionDepthaImgPipeline,
          StableDiffusionDiffEditPipeline,
          StableDiffusionPixaPixZeroPipeline,
      )
else:
      from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
      from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
      from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
      if not (
          is_torch_available()
          and is_transformers_available()
          and is_k_diffusion_available()
          and is_k_diffusion_version(""">=""", """0.0.12""")
      ):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
      from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
      if not (is_transformers_available() and is_onnx_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_onnx_objects import *  # noqa F403
else:
      from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
      from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
      from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
      from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
      from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
      import flax



      @flax.struct.dataclass
      class   __a  (      _lowerCAmelCase	):
             UpperCamelCase_		:					np.ndarray
             UpperCamelCase_		:					List[bool]





      from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
      from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
      from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
      from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
      from .safety_checker_flax import FlaxStableDiffusionSafetyChecker





