


"""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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


a__				:							Tuple						=     '''Create a default config file for Accelerate with only a few flags set.'''




def    UpperCAmelCase__       (lowerCAmelCase_="no"  ,						lowerCAmelCase_ = default_json_config_file  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				Path(lowerCAmelCase_   )
					path.parent.mkdir(parents=lowerCAmelCase_  ,						exist_ok=lowerCAmelCase_   )
					if path.exists():
										print(
										    f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""   )
										return False
					__SCREAMING_SNAKE_CASE						   =				mixed_precision.lower()
					if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
										raise ValueError(
										    f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""   )
					__SCREAMING_SNAKE_CASE						   =				{
					    "compute_environment": "LOCAL_MACHINE",
					    "mixed_precision": mixed_precision,
					}
					if torch.cuda.is_available():
										__SCREAMING_SNAKE_CASE						   =				torch.cuda.device_count()
										__SCREAMING_SNAKE_CASE						   =				num_gpus
										__SCREAMING_SNAKE_CASE						   =				False
										if num_gpus > 1:
															__SCREAMING_SNAKE_CASE						   =				"MULTI_GPU"
										else:
															__SCREAMING_SNAKE_CASE						   =				"NO"
					elif is_xpu_available() and use_xpu:
										__SCREAMING_SNAKE_CASE						   =				torch.xpu.device_count()
										__SCREAMING_SNAKE_CASE						   =				num_xpus
										__SCREAMING_SNAKE_CASE						   =				False
										if num_xpus > 1:
															__SCREAMING_SNAKE_CASE						   =				"MULTI_XPU"
										else:
															__SCREAMING_SNAKE_CASE						   =				"NO"
					elif is_npu_available():
										__SCREAMING_SNAKE_CASE						   =				torch.npu.device_count()
										__SCREAMING_SNAKE_CASE						   =				num_npus
										__SCREAMING_SNAKE_CASE						   =				False
										if num_npus > 1:
															__SCREAMING_SNAKE_CASE						   =				"MULTI_NPU"
										else:
															__SCREAMING_SNAKE_CASE						   =				"NO"
					else:
										__SCREAMING_SNAKE_CASE						   =				0
										__SCREAMING_SNAKE_CASE						   =				True
										__SCREAMING_SNAKE_CASE						   =				1
										__SCREAMING_SNAKE_CASE						   =				"NO"
					__SCREAMING_SNAKE_CASE						   =				ClusterConfig(**lowerCAmelCase_   )
					config.to_json_file(lowerCAmelCase_   )
					return path




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				parser.add_parser("default"  ,						parents=lowerCAmelCase_  ,						help=lowerCAmelCase_  ,						formatter_class=lowerCAmelCase_   )
					parser.add_argument(
					    "--config_file"  ,						default=lowerCAmelCase_  ,						help=(
					        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
					        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
					        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
					        "with 'huggingface'."
					    )  ,						dest="save_location"  ,						)

					parser.add_argument(
					    "--mixed_precision"  ,						choices=["no", "fp16", "bf16"]  ,						type=lowerCAmelCase_  ,						help="Whether or not to use mixed precision training. "
					    "Choose between FP16 and BF16 (bfloat16) training. "
					    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."  ,						default="no"  ,						)
					parser.set_defaults(func=lowerCAmelCase_   )
					return parser




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				write_basic_config(args.mixed_precision  ,						args.save_location   )
					if config_file:
										print(f"""accelerate configuration saved at {config_file}"""   )





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


A__  :  int								=					logging.get_logger(__name__)

A__  :  Optional[int]								=					{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''data2vec-vision'''




							def __init__(       self     :       Tuple				,				A_     :       List[Any]=7_6_8				,				A_     :       Union[str, Any]=1_2				,				A_     :       Dict=1_2				,				A_     :       List[Any]=3_0_7_2				,				A_     :       Dict="gelu"				,				A_     :       Tuple=0.0				,				A_     :       Dict=0.0				,				A_     :       List[str]=0.02				,				A_     :       List[str]=1e-12				,				A_     :       Tuple=2_2_4				,				A_     :       Dict=1_6				,				A_     :       Optional[int]=3				,				A_     :       Optional[int]=False				,				A_     :       Any=False				,				A_     :       Tuple=False				,				A_     :       Optional[int]=False				,				A_     :       int=0.1				,				A_     :       Union[str, Any]=0.1				,				A_     :       List[Any]=True				,				A_     :       List[Any]=[3, 5, 7, 1_1]				,				A_     :       Union[str, Any]=[1, 2, 3, 6]				,				A_     :       Optional[int]=True				,				A_     :       Any=0.4				,				A_     :       str=2_5_6				,				A_     :       Optional[int]=1				,				A_     :       str=False				,				A_     :       Optional[int]=2_5_5				,				**A_     :       Optional[int]				,				):
									super().__init__(**A_)

									lowerCAmelCase_			:     str									= hidden_size
									lowerCAmelCase_			:     List[str]									= num_hidden_layers
									lowerCAmelCase_			:     Optional[Any]									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Union[str, Any]									= hidden_act
									lowerCAmelCase_			:     List[Any]									= hidden_dropout_prob
									lowerCAmelCase_			:     Tuple									= attention_probs_dropout_prob
									lowerCAmelCase_			:     str									= initializer_range
									lowerCAmelCase_			:     Tuple									= layer_norm_eps

									lowerCAmelCase_			:     List[Any]									= image_size
									lowerCAmelCase_			:     List[Any]									= patch_size
									lowerCAmelCase_			:     Any									= num_channels
									lowerCAmelCase_			:     Any									= use_mask_token
									lowerCAmelCase_			:     Optional[int]									= use_absolute_position_embeddings
									lowerCAmelCase_			:     str									= use_relative_position_bias
									lowerCAmelCase_			:     Optional[Any]									= use_shared_relative_position_bias
									lowerCAmelCase_			:     Dict									= layer_scale_init_value
									lowerCAmelCase_			:     Tuple									= drop_path_rate
									lowerCAmelCase_			:     Optional[int]									= use_mean_pooling
									# decode head attributes (semantic segmentation)
									lowerCAmelCase_			:     Any									= out_indices
									lowerCAmelCase_			:     int									= pool_scales
									# auxiliary head attributes (semantic segmentation)
									lowerCAmelCase_			:     Dict									= use_auxiliary_head
									lowerCAmelCase_			:     str									= auxiliary_loss_weight
									lowerCAmelCase_			:     Optional[Any]									= auxiliary_channels
									lowerCAmelCase_			:     str									= auxiliary_num_convs
									lowerCAmelCase_			:     str									= auxiliary_concat_input
									lowerCAmelCase_			:     str									= semantic_loss_ignore_index





class 				__snake_case						(     UpperCamelCase_						):
							_a			      = version.parse('''1.11'''						)




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







							@property
							def    UpperCAmelCase__			(       self     :       Dict):
									return 1e-4





