


import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE=2					,					SCREAMING_SNAKE_CASE=True					,					SCREAMING_SNAKE_CASE=False					)->  Dict:




      """simple docstring"""

      snake_case_       =      Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE					,					split_batches=SCREAMING_SNAKE_CASE					)
      snake_case_       =      torch.nn.Linear(2					,					4					)
      snake_case_       =      torch.optim.AdamW(model.parameters()					,					lr=1.0					)
      snake_case_       =      torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE					,					max_lr=0.0_1					,					steps_per_epoch=2					,					epochs=1					)
      snake_case_       ,    snake_case_       ,    snake_case_       =      accelerator.prepare(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)

      # Optimizer has stepped
      scheduler.step()
      if step_scheduler_with_optimizer or (num_processes == 1):
            assert (
                scheduler.scheduler.last_epoch == num_processes
            ), f'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
      else:
            assert (
                scheduler.scheduler.last_epoch != num_processes
            ), f'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE=2					,					SCREAMING_SNAKE_CASE=True					,					SCREAMING_SNAKE_CASE=False					)->  Optional[Any]:




      """simple docstring"""

      snake_case_       =      Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE					,					split_batches=SCREAMING_SNAKE_CASE					)
      snake_case_       =      torch.nn.Linear(2					,					4					)
      snake_case_       =      torch.optim.AdamW(model.parameters()					,					lr=1.0					)
      snake_case_       =      torch.optim.lr_scheduler.LambdaLR(SCREAMING_SNAKE_CASE					,					lr_lambda=lambda SCREAMING_SNAKE_CASE					: 1 - n / 10					)
      snake_case_       ,    snake_case_       ,    snake_case_       =      accelerator.prepare(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)

      # Optimizer has stepped
      snake_case_       =      False
      scheduler.step()
      snake_case_       =      1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
      assert (
          scheduler.get_last_lr()[0] == expected_lr
      ), f'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

      # Optimizer has not stepped
      snake_case_       =      True
      scheduler.step()
      if not step_scheduler_with_optimizer:
            snake_case_       =      1 - 2 / 10
      assert (
          scheduler.get_last_lr()[0] == expected_lr
      ), f'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE = 2					)->  str:




      """simple docstring"""

      from transformers import get_linear_schedule_with_warmup

      snake_case_       =      [1, 2, 4]
      for num_steps in steps:
            snake_case_       =      GradientAccumulationPlugin(num_steps=SCREAMING_SNAKE_CASE					,					adjust_scheduler=num_steps > 1					)
            snake_case_       =      Accelerator(gradient_accumulation_plugin=SCREAMING_SNAKE_CASE					)
            snake_case_       =      torch.nn.Linear(2					,					4					)
            snake_case_       =      torch.optim.AdamW(model.parameters()					,					lr=1_0.0					)
            snake_case_       =      get_linear_schedule_with_warmup(optimizer=SCREAMING_SNAKE_CASE					,					num_warmup_steps=0					,					num_training_steps=20					)

            snake_case_       ,    snake_case_       ,    snake_case_       =      accelerator.prepare(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)

            for i in range(10 * num_steps					):
                  with accelerator.accumulate(SCREAMING_SNAKE_CASE					):
                        optimizer.step()
                        scheduler.step()

                  if i == (10 * num_steps - 2):
                        assert (
                            scheduler.get_last_lr()[0] != 0
                        ), f'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
            assert (
                scheduler.get_last_lr()[0] == 0
            ), f'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
            GradientState._reset_state()







@require_cpu
class lowerCAmelCase_						( unittest.TestCase       ):

 '''simple docstring'''

 def 						UpperCamelCase__     (	self					):
       debug_launcher(partial(_UpperCAmelCase		,      num_processes=1					)		,      num_processes=1					)
       debug_launcher(partial(_UpperCAmelCase		,      num_processes=1		,      split_batches=_UpperCAmelCase					)		,      num_processes=1					)



 def 						UpperCamelCase__     (	self					):
       debug_launcher(partial(_UpperCAmelCase		,      num_processes=1					)		,      num_processes=1					)
       debug_launcher(partial(_UpperCAmelCase		,      num_processes=1		,      split_batches=_UpperCAmelCase					)		,      num_processes=1					)



 def 						UpperCamelCase__     (	self					):
       debug_launcher(partial(_UpperCAmelCase		,      num_processes=1		,      step_scheduler_with_optimizer=_UpperCAmelCase					)		,      num_processes=1					)



 def 						UpperCamelCase__     (	self					):
       debug_launcher(partial(_UpperCAmelCase		,      num_processes=1		,      step_scheduler_with_optimizer=_UpperCAmelCase					)		,      num_processes=1					)



 def 						UpperCamelCase__     (	self					):
       AcceleratorState._reset_state(_UpperCAmelCase					)
       debug_launcher(_UpperCAmelCase					)
       debug_launcher(partial(_UpperCAmelCase		,      num_processes=1		,      split_batches=_UpperCAmelCase					)		,      num_processes=1					)



 def 						UpperCamelCase__     (	self					):
       AcceleratorState._reset_state(_UpperCAmelCase					)
       debug_launcher(_UpperCAmelCase					)
       debug_launcher(partial(_UpperCAmelCase		,      num_processes=1		,      split_batches=_UpperCAmelCase					)		,      num_processes=1					)



 def 						UpperCamelCase__     (	self					):
       AcceleratorState._reset_state(_UpperCAmelCase					)
       debug_launcher(partial(_UpperCAmelCase		,      step_scheduler_with_optimizer=_UpperCAmelCase					)					)



 def 						UpperCamelCase__     (	self					):
       AcceleratorState._reset_state(_UpperCAmelCase					)
       debug_launcher(partial(_UpperCAmelCase		,      step_scheduler_with_optimizer=_UpperCAmelCase					)					)





 @require_huggingface_suite
 def 						UpperCamelCase__     (	self					):
       AcceleratorState._reset_state(_UpperCAmelCase					)
       debug_launcher(partial(_UpperCAmelCase		,      num_processes=1					)					)
       debug_launcher(_UpperCAmelCase					)



import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
   from .testing_utils import (
       floats_tensor,
       load_hf_numpy,
       load_image,
       load_numpy,
       load_pt,
       nightly,
       parse_flag_from_env,
       print_tensor_test,
       require_torch_a,
       require_torch_gpu,
       skip_mps,
       slow,
       torch_all_close,
       torch_device,
   )
   from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase			=    get_logger(__name__)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Tuple:




      """simple docstring"""

      if version.parse(SCREAMING_SNAKE_CASE					) < version.parse(SCREAMING_SNAKE_CASE					):
            if "dev" in min_version:
                  snake_case_       =      (
                      '''This example requires a source install from HuggingFace diffusers (see '''
                      '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                  )
            else:
                  snake_case_       =      f'''This example requires a minimum version of {min_version},'''
            error_message += f''' but the version found is {__version__}.\n'''
            raise ImportError(SCREAMING_SNAKE_CASE					)