




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__				:     List[str] 		=						logging.getLogger(__name__)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     git.Repo(search_parent_directories=UpperCamelCase_							)
	snake_case_		    =     {
	    "repo_id": str(UpperCamelCase_							),
	    "repo_sha": str(repo.head.object.hexsha							),
	    "repo_branch": str(repo.active_branch							),
	}

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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

	assert torch.cuda.is_available()

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

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

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

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

	# summary
	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 						lowerCamelCase__    (		_A							):







	'''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 ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   int				       ="""xmod"""
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[int]=30522       ,   UpperCAmelCase_							:   Union[str, Any]=768       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=3072       ,   UpperCAmelCase_							:   Dict="gelu"       ,   UpperCAmelCase_							:   str=0.1       ,   UpperCAmelCase_							:   Dict=0.1       ,   UpperCAmelCase_							:   Optional[Any]=512       ,   UpperCAmelCase_							:   Any=2       ,   UpperCAmelCase_							:   Optional[Any]=0.02       ,   UpperCAmelCase_							:   Optional[Any]=1e-1_2       ,   UpperCAmelCase_							:   Any=1       ,   UpperCAmelCase_							:   Dict=0       ,   UpperCAmelCase_							:   Optional[int]=2       ,   UpperCAmelCase_							:   Tuple="absolute"       ,   UpperCAmelCase_							:   Union[str, Any]=True       ,   UpperCAmelCase_							:   Dict=None       ,   UpperCAmelCase_							:   Union[str, Any]=False       ,   UpperCAmelCase_							:   int=2       ,   UpperCAmelCase_							:   int=False       ,   UpperCAmelCase_							:   Optional[int]=True       ,   UpperCAmelCase_							:   List[Any]=True       ,   UpperCAmelCase_							:   Union[str, Any]=("en_XX",)       ,   UpperCAmelCase_							:   int=None       ,   **UpperCAmelCase_							:   str       ,   ):
						super().__init__(pad_token_id=UpperCAmelCase_       ,   bos_token_id=UpperCAmelCase_       ,   eos_token_id=UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= vocab_size
						SCREAMING_SNAKE_CASE__													= hidden_size
						SCREAMING_SNAKE_CASE__													= num_hidden_layers
						SCREAMING_SNAKE_CASE__													= num_attention_heads
						SCREAMING_SNAKE_CASE__													= hidden_act
						SCREAMING_SNAKE_CASE__													= intermediate_size
						SCREAMING_SNAKE_CASE__													= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__													= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__													= max_position_embeddings
						SCREAMING_SNAKE_CASE__													= type_vocab_size
						SCREAMING_SNAKE_CASE__													= initializer_range
						SCREAMING_SNAKE_CASE__													= layer_norm_eps
						SCREAMING_SNAKE_CASE__													= position_embedding_type
						SCREAMING_SNAKE_CASE__													= use_cache
						SCREAMING_SNAKE_CASE__													= classifier_dropout
						SCREAMING_SNAKE_CASE__													= pre_norm
						SCREAMING_SNAKE_CASE__													= adapter_reduction_factor
						SCREAMING_SNAKE_CASE__													= adapter_layer_norm
						SCREAMING_SNAKE_CASE__													= adapter_reuse_layer_norm
						SCREAMING_SNAKE_CASE__													= ln_before_adapter
						SCREAMING_SNAKE_CASE__													= list(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= default_language





class 						lowercase__  (						_UpperCAmelCase  ):
	@property
	def      A_				(			self							:   List[Any]							):
						if self.task == "multiple-choice":
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)





