




'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCamelCase							:		Optional[int]										=						logging.get_logger(__name__)

_lowerCamelCase							:		Tuple										=						{
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}





class   lowerCamelCase_							( UpperCAmelCase_, UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__					=				"""convnext"""






					def __init__(					self,						__UpperCamelCase=3,						__UpperCamelCase=4,						__UpperCamelCase=4,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase="gelu",						__UpperCamelCase=0.02,						__UpperCamelCase=1E-12,						__UpperCamelCase=1E-6,						__UpperCamelCase=0.0,						__UpperCamelCase=224,						__UpperCamelCase=None,						__UpperCamelCase=None,						**__UpperCamelCase,						):
							super().__init__(**__UpperCamelCase				)

							_snake_case			 = num_channels
							_snake_case			 = patch_size
							_snake_case			 = num_stages
							_snake_case			 = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
							_snake_case			 = [3, 3, 9, 3] if depths is None else depths
							_snake_case			 = hidden_act
							_snake_case			 = initializer_range
							_snake_case			 = layer_norm_eps
							_snake_case			 = layer_scale_init_value
							_snake_case			 = drop_path_rate
							_snake_case			 = image_size
							_snake_case			 = ["stem"] + [f'''stage{idx}''' for idx in range(1,						len(self.depths				) + 1				)]
							_snake_case ,					_snake_case			 = get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase,						out_indices=__UpperCamelCase,						stage_names=self.stage_names				)





class   lowerCamelCase_							( UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__					=				version.parse("""1.11"""							)






					@property
					def        __SCREAMING_SNAKE_CASE			(					self				):
							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]				)






					@property
					def        __SCREAMING_SNAKE_CASE			(					self				):
							return 1E-5











'''simple docstring'''







import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
						import torch_xla.core.xla_model as xm  # noqa: F401

						_lowerCamelCase							:		int										=						True
except ImportError:
						_lowerCamelCase							:		List[str]										=						False


# Cache this result has it's a C FFI call which can be pretty time-consuming
_lowerCamelCase							:		List[Any]										=						torch.distributed.is_available()






def 	_lowercase	(			lowercase_						):



		'''simple docstring'''







		_snake_case			 = importlib.util.find_spec(lowercase_						) is not None
		if package_exists:
				try:
						_snake_case			 = importlib.metadata.metadata(lowercase_						)
						return True
				except importlib.metadata.PackageNotFoundError:
						return False






def 	_lowercase	(			):



		'''simple docstring'''







		return _torch_distributed_available






def 	_lowercase	(			):



		'''simple docstring'''







		try:
				pass
		except ImportError:
				print(
				    "Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not"
				    " detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"
				    " Bindings for PyTorch*."						)
		return (
		    importlib.util.find_spec("torch_ccl"						) is not None
		    or importlib.util.find_spec("oneccl_bindings_for_pytorch"						) is not None
		)






def 	_lowercase	(			):



		'''simple docstring'''







		return importlib.metadata.version("oneccl_bind_pt"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("transformer_engine"						)






@lru_cache
def 	_lowercase	(			lowercase_=True						):



		'''simple docstring'''







		if torch.cuda.is_available():
				return False
		if _tpu_available and check_device:
				try:
						# Will raise a RuntimeError if no XLA configuration is found
						_snake_case			 = xm.xla_device()
						return True
				except RuntimeError:
						return False
		return _tpu_available






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("deepspeed"						)






def 	_lowercase	(			lowercase_=False						):



		'''simple docstring'''







		if is_tpu_available():
				return not ignore_tpu
		if torch.cuda.is_available():
				return torch.cuda.is_bfaa_supported()
		if is_npu_available():
				return False
		return True






def 	_lowercase	(			):



		'''simple docstring'''







		_snake_case			 = _is_package_available("bitsandbytes"						)
		if package_exists:
				_snake_case			 = version.parse(importlib.metadata.version("bitsandbytes"						)						)
				return compare_versions(lowercase_		,							">="		,							"0.39.0"						)
		return False






def 	_lowercase	(			):



		'''simple docstring'''







		_snake_case			 = _is_package_available("bitsandbytes"						)
		if package_exists:
				_snake_case			 = version.parse(importlib.metadata.version("bitsandbytes"						)						)
				return compare_versions(lowercase_		,							">="		,							"0.37.2"						)
		return False






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("bitsandbytes"						)






def 	_lowercase	(			):



		'''simple docstring'''







		if strtobool(os.environ.get("ACCELERATE_USE_MEGATRON_LM"		,							"False"						)						) == 1:
				_snake_case			 = importlib.util.find_spec("megatron"						) is not None
				if package_exists:
						try:
								_snake_case			 = parse(importlib.metadata.version("megatron-lm"						)						)
								return compare_versions(lowercase_		,							">="		,							"2.2.0"						)
						except Exception as e:
								warnings.warn(f'''Parse Megatron version failed. Exception:{e}'''						)
								return False






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("safetensors"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("transformers"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("datasets"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("aim"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("tensorboard"						) or _is_package_available("tensorboardX"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("wandb"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("comet_ml"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("boto3"						)






def 	_lowercase	(			):



		'''simple docstring'''







		if _is_package_available("rich"						):
				if "ACCELERATE_DISABLE_RICH" in os.environ:
						warnings.warn(
						    "`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`."						)
						return not parse_flag_from_env("ACCELERATE_DISABLE_RICH"		,							lowercase_						)
				return parse_flag_from_env("ACCELERATE_ENABLE_RICH"		,							lowercase_						)
		return False






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("sagemaker"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("tqdm"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return _is_package_available("mlflow"						)






def 	_lowercase	(			):



		'''simple docstring'''







		return is_torch_version(">="		,							"1.12"						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()






def 	_lowercase	(			):



		'''simple docstring'''







		def get_major_and_minor_from_version(lowercase_						):
				return str(version.parse(lowercase_						).major						) + "." + str(version.parse(lowercase_						).minor						)

		_snake_case			 = importlib.metadata.version("torch"						)
		if importlib.util.find_spec("intel_extension_for_pytorch"						) is None:
				return False
		_snake_case			 = "N/A"
		try:
				_snake_case			 = importlib.metadata.version("intel_extension_for_pytorch"						)
		except importlib.metadata.PackageNotFoundError:
				return False
		_snake_case			 = get_major_and_minor_from_version(_torch_version						)
		_snake_case			 = get_major_and_minor_from_version(_ipex_version						)
		if torch_major_and_minor != ipex_major_and_minor:
				warnings.warn(
				    f'''Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'''
				    f''' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'''						)
				return False
		return True






@lru_cache
def 	_lowercase	(			lowercase_=False						):



		'''simple docstring'''







		if importlib.util.find_spec("torch"						) is None or importlib.util.find_spec("torch_npu"						) is None:
				return False

		import torch
		import torch_npu  # noqa: F401

		if check_device:
				try:
						# Will raise a RuntimeError if no NPU is found
						_snake_case			 = torch.npu.device_count()
						return torch.npu.is_available()
				except RuntimeError:
						return False
		return hasattr(lowercase_		,							"npu"						) and torch.npu.is_available()






@lru_cache
def 	_lowercase	(			lowercase_=False						):



		'''simple docstring'''







		if not parse_flag_from_env("ACCELERATE_USE_XPU"		,							default=lowercase_						):
				return False
		if is_ipex_available():
				import torch

				if is_torch_version("<="		,							"1.12"						):
						return False
		else:
				return False

		import intel_extension_for_pytorch  # noqa: F401

		if check_device:
				try:
						# Will raise a RuntimeError if no XPU  is found
						_snake_case			 = torch.xpu.device_count()
						return torch.xpu.is_available()
				except RuntimeError:
						return False
		return hasattr(lowercase_		,							"xpu"						) and torch.xpu.is_available()





