




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

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Optional[Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						{
			    'repo_id': str(SCREAMING_SNAKE_CASE ),
			    'repo_sha': str(repo.head.object.hexsha ),
			    'repo_branch': str(repo.active_branch ),
			}

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

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Optional[Any]:


			'''simple docstring'''







			if params.n_gpu <= 0:
						SCREAMING_SNAKE_CASE_								=						0
						SCREAMING_SNAKE_CASE_								=						-1
						SCREAMING_SNAKE_CASE_								=						True
						SCREAMING_SNAKE_CASE_								=						False
						return

			assert torch.cuda.is_available()

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

						SCREAMING_SNAKE_CASE_								=						int(os.environ['WORLD_SIZE'] )
						SCREAMING_SNAKE_CASE_								=						int(os.environ['N_GPU_NODE'] )
						SCREAMING_SNAKE_CASE_								=						int(os.environ['RANK'] )

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

						SCREAMING_SNAKE_CASE_								=						1
						SCREAMING_SNAKE_CASE_								=						0
						SCREAMING_SNAKE_CASE_								=						0
						SCREAMING_SNAKE_CASE_								=						0
						SCREAMING_SNAKE_CASE_								=						1
						SCREAMING_SNAKE_CASE_								=						1
						SCREAMING_SNAKE_CASE_								=						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
			SCREAMING_SNAKE_CASE_								=						params.node_id == 0 and params.local_rank == 0
			SCREAMING_SNAKE_CASE_								=						params.n_nodes > 1

			# summary
			SCREAMING_SNAKE_CASE_								=						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 lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			np.random.seed(args.seed )
			torch.manual_seed(args.seed )
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed )






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


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

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=1.0      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=32      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="relu6"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.8      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=0.0_0_1      ,     SCREAMING_SNAKE_CASE=255      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Dict:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

				if depth_multiplier <= 0:
							raise ValueError('depth_multiplier must be greater than zero.'      )

				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						depth_multiplier
				SCREAMING_SNAKE_CASE_								=						depth_divisible_by
				SCREAMING_SNAKE_CASE_								=						min_depth
				SCREAMING_SNAKE_CASE_								=						expand_ratio
				SCREAMING_SNAKE_CASE_								=						output_stride
				SCREAMING_SNAKE_CASE_								=						first_layer_is_expansion
				SCREAMING_SNAKE_CASE_								=						finegrained_output
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						tf_padding
				SCREAMING_SNAKE_CASE_								=						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						semantic_loss_ignore_index




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				return OrderedDict([('pixel_values', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				if self.task == "image-classification":
							return OrderedDict([('logits', {0: 'batch'})]      )
				else:
							return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					float:




				"""simple docstring"""



				return 1e-4
