



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,
)
__A   			=logging.getLogger(__name__)

def        a						(      _UpperCAmelCase							:					str						):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	git.Repo(search_parent_directories=_UpperCAmelCase						)
		__UpperCAmelCase    :     Dict													=	{
		    '''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        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		if params.n_gpu <= 0:
				__UpperCAmelCase    :     int													=	0
				__UpperCAmelCase    :     int													=	-1
				__UpperCAmelCase    :     List[str]													=	True
				__UpperCAmelCase    :     Tuple													=	False
				return

		assert torch.cuda.is_available()

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

				__UpperCAmelCase    :     List[Any]													=	int(os.environ['''WORLD_SIZE''']						)
				__UpperCAmelCase    :     str													=	int(os.environ['''N_GPU_NODE''']						)
				__UpperCAmelCase    :     str													=	int(os.environ['''RANK''']						)

				# number of nodes / node ID
				__UpperCAmelCase    :     str													=	params.world_size // params.n_gpu_per_node
				__UpperCAmelCase    :     Optional[Any]													=	params.global_rank // params.n_gpu_per_node
				__UpperCAmelCase    :     List[Any]													=	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

				__UpperCAmelCase    :     Dict													=	1
				__UpperCAmelCase    :     Tuple													=	0
				__UpperCAmelCase    :     Tuple													=	0
				__UpperCAmelCase    :     Any													=	0
				__UpperCAmelCase    :     Optional[int]													=	1
				__UpperCAmelCase    :     Optional[Any]													=	1
				__UpperCAmelCase    :     Dict													=	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
		__UpperCAmelCase    :     Union[str, Any]													=	params.node_id == 0 and params.local_rank == 0
		__UpperCAmelCase    :     Union[str, Any]													=	params.n_nodes > 1

		# summary
		__UpperCAmelCase    :     Union[str, Any]													=	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        a						(      _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





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










import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
				import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class 		UpperCAmelCase__			(			unittest.TestCase						,__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	load_tool('''text-to-speech''' )
					self.tool.setup()




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     Union[str, Any]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     Optional[int]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     List[str]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )





