

import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


SCREAMING_SNAKE_CASE		:Optional[int]						=     get_logger(__name__)


SCREAMING_SNAKE_CASE		:List[Any]						=     Path(__file__).parent / """model_card_template.md"""
SCREAMING_SNAKE_CASE		:Optional[int]						=     uuida().hex
SCREAMING_SNAKE_CASE		:List[Any]						=     os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
SCREAMING_SNAKE_CASE		:Tuple						=     os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
SCREAMING_SNAKE_CASE		:Optional[Any]						=     HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = None			)->						str:



    """simple docstring"""






    UpperCamelCase_			  =						f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
    if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
        return ua + "; telemetry/off"
    if is_torch_available():
        ua += f"; torch/{_torch_version}"
    if is_flax_available():
        ua += f"; jax/{_jax_version}"
        ua += f"; flax/{_flax_version}"
    if is_onnx_available():
        ua += f"; onnxruntime/{_onnxruntime_version}"
    # CI will set this value to True
    if os.environ.get("DIFFUSERS_IS_CI"			,						""			).upper() in ENV_VARS_TRUE_VALUES:
        ua += "; is_ci/true"
    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()			)
    elif isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        ua += "; " + user_agent
    return ua




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_ = None			,						SCREAMING_SNAKE_CASE_ = None			)->						List[str]:



    """simple docstring"""






    if token is None:
        UpperCamelCase_			  =						HfFolder.get_token()
    if organization is None:
        UpperCamelCase_			  =						whoami(SCREAMING_SNAKE_CASE_			)["name"]
        return f"{username}/{model_id}"
    else:
        return f"{organization}/{model_id}"




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    if not is_jinja_available():
        raise ValueError(
            "Modelcard rendering is based on Jinja templates."
            " Please make sure to have `jinja` installed before using `create_model_card`."
            " To install it, please run `pip install Jinja2`."			)

    if hasattr(SCREAMING_SNAKE_CASE_			,						"local_rank"			) and args.local_rank not in [-1, 0]:
        return

    UpperCamelCase_			  =						args.hub_token if hasattr(SCREAMING_SNAKE_CASE_			,						"hub_token"			) else None
    UpperCamelCase_			  =						get_full_repo_name(SCREAMING_SNAKE_CASE_			,						token=SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						ModelCard.from_template(
        card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
            language="en"			,						license="apache-2.0"			,						library_name="diffusers"			,						tags=[]			,						datasets=args.dataset_name			,						metrics=[]			,						)			,						template_path=SCREAMING_SNAKE_CASE_			,						model_name=SCREAMING_SNAKE_CASE_			,						repo_name=SCREAMING_SNAKE_CASE_			,						dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE_			,						"dataset_name"			) else None			,						learning_rate=args.learning_rate			,						train_batch_size=args.train_batch_size			,						eval_batch_size=args.eval_batch_size			,						gradient_accumulation_steps=(
            args.gradient_accumulation_steps if hasattr(SCREAMING_SNAKE_CASE_			,						"gradient_accumulation_steps"			) else None
        )			,						adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE_			,						"adam_beta1"			) else None			,						adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE_			,						"adam_beta2"			) else None			,						adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE_			,						"adam_weight_decay"			) else None			,						adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE_			,						"adam_epsilon"			) else None			,						lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE_			,						"lr_scheduler"			) else None			,						lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE_			,						"lr_warmup_steps"			) else None			,						ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE_			,						"ema_inv_gamma"			) else None			,						ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE_			,						"ema_power"			) else None			,						ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE_			,						"ema_max_decay"			) else None			,						mixed_precision=args.mixed_precision			,						)

    UpperCamelCase_			  =						os.path.join(args.output_dir			,						"README.md"			)
    model_card.save(SCREAMING_SNAKE_CASE_			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_ = None			)->						Optional[Any]:



    """simple docstring"""






    if resolved_file is None or commit_hash is not None:
        return commit_hash
    UpperCamelCase_			  =						str(Path(SCREAMING_SNAKE_CASE_			).as_posix()			)
    UpperCamelCase_			  =						re.search(r"snapshots/([^/]+)/"			,						SCREAMING_SNAKE_CASE_			)
    if search is None:
        return None
    UpperCamelCase_			  =						search.groups()[0]
    return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE_			) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
SCREAMING_SNAKE_CASE		:List[str]						=     os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
SCREAMING_SNAKE_CASE		:Tuple						=     os.path.join(hf_cache_home, """diffusers""")




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = None			,						SCREAMING_SNAKE_CASE_ = None			)->						None:



    """simple docstring"""






    if new_cache_dir is None:
        UpperCamelCase_			  =						DIFFUSERS_CACHE
    if old_cache_dir is None:
        UpperCamelCase_			  =						old_diffusers_cache

    UpperCamelCase_			  =						Path(SCREAMING_SNAKE_CASE_			).expanduser()
    UpperCamelCase_			  =						Path(SCREAMING_SNAKE_CASE_			).expanduser()
    for old_blob_path in old_cache_dir.glob("**/blobs/*"			):
        if old_blob_path.is_file() and not old_blob_path.is_symlink():
            UpperCamelCase_			  =						new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE_			)
            new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE_			,						exist_ok=SCREAMING_SNAKE_CASE_			)
            os.replace(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            try:
                os.symlink(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            except OSError:
                logger.warning(
                    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."			)
    # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


SCREAMING_SNAKE_CASE		:List[str]						=     os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     0
else:
 with open(cache_version_file) as f:
  try:
   SCREAMING_SNAKE_CASE		:List[str]						=     int(f.read())
  except ValueError:
   SCREAMING_SNAKE_CASE		:Optional[int]						=     0

if cache_version < 1:
 SCREAMING_SNAKE_CASE		:List[Any]						=     os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
 if old_cache_is_not_empty:
  logger.warning(
      """The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
      """existing cached models. This is a one-time operation, you can interrupt it or run it """
      """later by calling `diffusers.utils.hub_utils.move_cache()`."""
  )
  try:
   move_cache()
  except Exception as e:
   SCREAMING_SNAKE_CASE		:Optional[Any]						=     """\n""".join(traceback.format_tb(e.__traceback__))
   logger.error(
       F'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
       """file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
       """message and we will do our best to help."""
   )

if cache_version < 1:
 try:
  os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
  with open(cache_version_file, """w""") as f:
   f.write("""1""")
 except Exception:
  logger.warning(
      F'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
      """the directory exists and can be written to."""
  )




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_ = None			)->						str:



    """simple docstring"""






    if variant is not None:
        UpperCamelCase_			  =						weights_name.split("."			)
        UpperCamelCase_			  =						splits[:-1] + [variant] + splits[-1:]
        UpperCamelCase_			  =						".".join(SCREAMING_SNAKE_CASE_			)

    return weights_name




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						*,
    SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			,						)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						str(SCREAMING_SNAKE_CASE_			)
    if os.path.isfile(SCREAMING_SNAKE_CASE_			):
        return pretrained_model_name_or_path
    elif os.path.isdir(SCREAMING_SNAKE_CASE_			):
        if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			):
            # Load from a PyTorch checkpoint
            UpperCamelCase_			  =						os.path.join(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            return model_file
        elif subfolder is not None and os.path.isfile(
            os.path.join(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			):
            UpperCamelCase_			  =						os.path.join(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            return model_file
        else:
            raise EnvironmentError(
                f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."			)
    else:
        # 1. First check if deprecated way of loading from branches is used
        if (
            revision in DEPRECATED_REVISION_ARGS
            and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
            and version.parse(version.parse(SCREAMING_SNAKE_CASE_			).base_version			) >= version.parse("0.20.0"			)
        ):
            try:
                UpperCamelCase_			  =						hf_hub_download(
                    SCREAMING_SNAKE_CASE_			,						filename=_add_variant(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						cache_dir=SCREAMING_SNAKE_CASE_			,						force_download=SCREAMING_SNAKE_CASE_			,						proxies=SCREAMING_SNAKE_CASE_			,						resume_download=SCREAMING_SNAKE_CASE_			,						local_files_only=SCREAMING_SNAKE_CASE_			,						use_auth_token=SCREAMING_SNAKE_CASE_			,						user_agent=SCREAMING_SNAKE_CASE_			,						subfolder=SCREAMING_SNAKE_CASE_			,						revision=revision or commit_hash			,						)
                warnings.warn(
                    f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."			,						SCREAMING_SNAKE_CASE_			,						)
                return model_file
            except:  # noqa: E722
                warnings.warn(
                    f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)}' so that the correct variant file can be added."			,						SCREAMING_SNAKE_CASE_			,						)
        try:
            # 2. Load model file as usual
            UpperCamelCase_			  =						hf_hub_download(
                SCREAMING_SNAKE_CASE_			,						filename=SCREAMING_SNAKE_CASE_			,						cache_dir=SCREAMING_SNAKE_CASE_			,						force_download=SCREAMING_SNAKE_CASE_			,						proxies=SCREAMING_SNAKE_CASE_			,						resume_download=SCREAMING_SNAKE_CASE_			,						local_files_only=SCREAMING_SNAKE_CASE_			,						use_auth_token=SCREAMING_SNAKE_CASE_			,						user_agent=SCREAMING_SNAKE_CASE_			,						subfolder=SCREAMING_SNAKE_CASE_			,						revision=revision or commit_hash			,						)
            return model_file

        except RepositoryNotFoundError:
            raise EnvironmentError(
                f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
                "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
                "login`."			)
        except RevisionNotFoundError:
            raise EnvironmentError(
                f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
                "this model name. Check the model page at "
                f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."			)
        except EntryNotFoundError:
            raise EnvironmentError(
                f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."			)
        except HTTPError as err:
            raise EnvironmentError(
                f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"			)
        except ValueError:
            raise EnvironmentError(
                f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                f" directory containing a file named {weights_name} or"
                " \nCheckout your internet connection or see how to run the library in"
                " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."			)
        except EnvironmentError:
            raise EnvironmentError(
                f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                f"containing a file named {weights_name}"			)



import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.getLogger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_=2			,						SCREAMING_SNAKE_CASE_=3			,						SCREAMING_SNAKE_CASE_=1_6			,						SCREAMING_SNAKE_CASE_ = 1_0			,						SCREAMING_SNAKE_CASE_ = 2			)->						Optional[Any]:



    """simple docstring"""






    def get_dataset(SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						torch.randn(batch_size * n_batches			,						1			)
        return TensorDataset(SCREAMING_SNAKE_CASE_			,						a * x + b + 0.1 * torch.randn(batch_size * n_batches			,						1			)			)

    UpperCamelCase_			  =						get_dataset(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						get_dataset(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						DataLoader(SCREAMING_SNAKE_CASE_			,						shuffle=SCREAMING_SNAKE_CASE_			,						batch_size=SCREAMING_SNAKE_CASE_			,						num_workers=4			)
    UpperCamelCase_			  =						DataLoader(SCREAMING_SNAKE_CASE_			,						shuffle=SCREAMING_SNAKE_CASE_			,						batch_size=SCREAMING_SNAKE_CASE_			,						num_workers=4			)
    return (train_dataloader, valid_dataloader)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						[]
    for epoch in range(SCREAMING_SNAKE_CASE_			):
        # Train quickly
        model.train()
        for batch in dataloader:
            UpperCamelCase_  ,       UpperCamelCase_			  =						batch
            UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
            UpperCamelCase_			  =						torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            accelerator.backward(SCREAMING_SNAKE_CASE_			)
            optimizer.step()
            optimizer.zero_grad()
        rands.append(random.random()			)  # Introduce some randomness
        if scheduler is not None:
            scheduler.step()
    return rands






class 						__magic_name__					(							nn.Module				):






     def __init__(				self	)->     List[Any]:
         super().__init__()
         UpperCamelCase_			  =						nn.Parameter(torch.randn(1	)	)
         UpperCamelCase_			  =						nn.Parameter(torch.randn(1	)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     str:
         return x * self.a + self.b






class 						__magic_name__					(							unittest.TestCase				):






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(total_limit=1		,						project_dir=_lowercase		,						automatic_checkpoint_naming=_lowercase	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()

             # Save second state
             accelerator.save_state()
             self.assertEqual(len(os.listdir(accelerator.project_dir	)	)		,						1	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             # Train baseline
             UpperCamelCase_			  =						Accelerator()
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             UpperCamelCase_			  =						os.path.join(_lowercase		,						"initial"	)
             accelerator.save_state(_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             UpperCamelCase_			  =						train(3		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()

             # Train partially
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						Accelerator()
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             accelerator.load_state(_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						train(2		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save everything
             UpperCamelCase_			  =						os.path.join(_lowercase		,						"checkpoint"	)
             accelerator.save_state(_lowercase	)

             # Load everything back in and make sure all states work
             accelerator.load_state(_lowercase	)
             test_rands += train(1		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase	)

             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             UpperCamelCase_			  =						train(3		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()

             # Train partially
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(iteration=1		,						automatic_checkpoint_naming=_lowercase	)
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             accelerator.load_state(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_0"	)	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						train(2		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_1"	)	)
             test_rands += train(1		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						torch.tensor([1, 2, 3]	)
         UpperCamelCase_			  =						torch.tensor([2, 3, 4]	)
         UpperCamelCase_			  =						DummyModel()
         UpperCamelCase_			  =						torch.optim.Adam(net.parameters()	)
         UpperCamelCase_			  =						Accelerator()
         with self.assertRaises(_lowercase	) as ve:
             accelerator.register_for_checkpointing(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
         UpperCamelCase_			  =						str(ve.exception	)
         self.assertTrue("Item at index 0" in message	)
         self.assertTrue("Item at index 1" in message	)
         self.assertFalse("Item at index 2" in message	)
         self.assertFalse("Item at index 3" in message	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_			  =						torch.optim.lr_scheduler.StepLR(_lowercase		,						step_size=1		,						gamma=0.99	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()
             UpperCamelCase_			  =						scheduler.state_dict()
             train(3		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             self.assertNotEqual(_lowercase		,						scheduler.state_dict()	)

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_0"	)	)
             self.assertEqual(_lowercase		,						scheduler.state_dict()	)






     def 	UpperCAmelCase_				(				self	)->     str:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase		,						total_limit=2	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_			  =						accelerator.prepare(_lowercase	)
             # Save 3 states:
             for _ in range(11	):
                 accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_0"	)	)	)
             self.assertTrue(os.path.exists(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_9"	)	)	)
             self.assertTrue(os.path.exists(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_10"	)	)	)






     @require_cuda
     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__	)]
         execute_subprocess_async(_lowercase		,						env=os.environ.copy()	)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Any						=     """/tmp/accelerate/state_checkpointing"""
 SCREAMING_SNAKE_CASE		:Any						=     DummyModel()
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     torch.optim.Adam(params=model.parameters(), lr=1e-3)
 SCREAMING_SNAKE_CASE		:Dict						=     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:str						=     dummy_dataloaders()
 SCREAMING_SNAKE_CASE		:Tuple						=     ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 SCREAMING_SNAKE_CASE		:int						=     Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
 if accelerator.process_index == 0:
  if os.path.exists(savedir):
   shutil.rmtree(savedir)
  os.makedirs(savedir)
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:Tuple						=     accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:List[str]						=     accelerator.prepare(model, optimizer)
 train(3, model, train_dataloader, optimizer, accelerator, scheduler)
 # Check that the intial optimizer is loaded on the GPU
 for group in optimizer.param_groups:
  SCREAMING_SNAKE_CASE		:Optional[Any]						=     group["""params"""][0].device
  break
 assert param_device.type == accelerator.device.type
 SCREAMING_SNAKE_CASE		:List[str]						=     model.cpu()
 accelerator.wait_for_everyone()
 accelerator.save_state()
 accelerator.wait_for_everyone()

 # Check CPU state
 accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
 for group in optimizer.param_groups:
  SCREAMING_SNAKE_CASE		:Optional[int]						=     group["""params"""][0].device
  break
 assert (
     param_device.type == torch.device("""cpu""").type
 ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

 # Check device state
 model.to(accelerator.device)
 accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
 for group in optimizer.param_groups:
  SCREAMING_SNAKE_CASE		:str						=     group["""params"""][0].device
  break
 assert (
     param_device.type == accelerator.device.type
 ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

 # Check error
 with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
  accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
 accelerator.wait_for_everyone()
 if accelerator.process_index == 0:
  shutil.rmtree(savedir)
 accelerator.wait_for_everyone()
