



'''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


lowercase_      :				List[Any]   					=    logging.get_logger(__name__)

lowercase_      :				Any   					=    {
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''beit'''







	def __init__(    self ,				_lowerCAmelCase=8192 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-12 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=False ,				_lowerCAmelCase=False ,				_lowerCAmelCase=False ,				_lowerCAmelCase=False ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=[3, 5, 7, 11] ,				_lowerCAmelCase=[1, 2, 3, 6] ,				_lowerCAmelCase=True ,				_lowerCAmelCase=0.4 ,				_lowerCAmelCase=256 ,				_lowerCAmelCase=1 ,				_lowerCAmelCase=False ,				_lowerCAmelCase=255 ,				**_lowerCAmelCase ,				)    ->    Any:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)

							lowercase        =							vocab_size
							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							intermediate_size
							lowercase        =							hidden_act
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps

							lowercase        =							image_size
							lowercase        =							patch_size
							lowercase        =							num_channels
							lowercase        =							use_mask_token
							lowercase        =							use_absolute_position_embeddings
							lowercase        =							use_relative_position_bias
							lowercase        =							use_shared_relative_position_bias
							lowercase        =							layer_scale_init_value
							lowercase        =							drop_path_rate
							lowercase        =							use_mean_pooling
							# decode head attributes (semantic segmentation)
							lowercase        =							out_indices
							lowercase        =							pool_scales
							# auxiliary head attributes (semantic segmentation)
							lowercase        =							use_auxiliary_head
							lowercase        =							auxiliary_loss_weight
							lowercase        =							auxiliary_channels
							lowercase        =							auxiliary_num_convs
							lowercase        =							auxiliary_concat_input
							lowercase        =							semantic_loss_ignore_index




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					version.parse('''1.11'''			)







	@property
	def   _a   (    self						)    ->    Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






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







	@property
	def   _a   (    self						)    ->    float:
							'''simple docstring'''






							return 1E-4







'''simple docstring'''


import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
lowercase_      :				Tuple   					=    logging.getLogger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							git.Repo(search_parent_directories=lowercase_						)
						lowercase        =							{
						    """repo_id""": str(lowercase_						),
						    """repo_sha""": str(repo.head.object.hexsha						),
						    """repo_branch""": str(repo.active_branch						),
						}

						with open(os.path.join(lowercase_ ,  """git_log.json"""						) ,  """w"""						) as f:
												json.dump(lowercase_ ,  lowercase_ ,  indent=4						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						if params.n_gpu <= 0:
												lowercase        =							0
												lowercase        =							-1
												lowercase        =							True
												lowercase        =							False
												return

						assert torch.cuda.is_available()

						logger.info("""Initializing GPUs"""						)
						if params.n_gpu > 1:
												assert params.local_rank != -1

												lowercase        =							int(os.environ["""WORLD_SIZE"""]						)
												lowercase        =							int(os.environ["""N_GPU_NODE"""]						)
												lowercase        =							int(os.environ["""RANK"""]						)

												# number of nodes / node ID
												lowercase        =							params.world_size // params.n_gpu_per_node
												lowercase        =							params.global_rank // params.n_gpu_per_node
												lowercase        =							True

												assert params.n_nodes == int(os.environ["""N_NODES"""]						)
												assert params.node_id == int(os.environ["""NODE_RANK"""]						)

						# local job (single GPU)
						else:
												assert params.local_rank == -1

												lowercase        =							1
												lowercase        =							0
												lowercase        =							0
												lowercase        =							0
												lowercase        =							1
												lowercase        =							1
												lowercase        =							False

						# sanity checks
						assert params.n_nodes >= 1
						assert 0 <= params.node_id < params.n_nodes
						assert 0 <= params.local_rank <= params.global_rank < params.world_size
						assert params.world_size == params.n_nodes * params.n_gpu_per_node

						# define whether this is the master process / if we are in multi-node distributed mode
						lowercase        =							params.node_id == 0 and params.local_rank == 0
						lowercase        =							params.n_nodes > 1

						# summary
						lowercase        =							F"""--- Global rank: {params.global_rank} - """
						logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes						)
						logger.info(PREFIX + """Node ID        : %i""" % params.node_id						)
						logger.info(PREFIX + """Local rank     : %i""" % params.local_rank						)
						logger.info(PREFIX + """World size     : %i""" % params.world_size						)
						logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node						)
						logger.info(PREFIX + """Master         : %s""" % str(params.is_master						)						)
						logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node						)						)
						logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu						)						)
						logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()						)

						# set GPU device
						torch.cuda.set_device(params.local_rank						)

						# initialize multi-GPU
						if params.multi_gpu:
												logger.info("""Initializing PyTorch distributed"""						)
												torch.distributed.init_process_group(
												    init_method="""env://""" ,  backend="""nccl""" ,  )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any]						):
						np.random.seed(args.seed						)
						torch.manual_seed(args.seed						)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed						)


