


'''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,
)
__A	:	Tuple     =     logging.getLogger(__name__)





def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		snake_case_		:							str			       =						git.Repo(search_parent_directories=lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						{
		    """repo_id""": str(lowerCamelCase_ ),
		    """repo_sha""": str(repo.head.object.hexsha ),
		    """repo_branch""": str(repo.active_branch ),
		}

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





def      UpperCAmelCase      (		lowerCamelCase_  :List[str] ):







		'''simple docstring'''
		if params.n_gpu <= 0:
				snake_case_		:							Union[str, Any]			       =						0
				snake_case_		:							Optional[int]			       =						-1
				snake_case_		:							Any			       =						True
				snake_case_		:							Any			       =						False
				return

		assert torch.cuda.is_available()

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

				snake_case_		:							str			       =						int(os.environ["""WORLD_SIZE"""] )
				snake_case_		:							Optional[Any]			       =						int(os.environ["""N_GPU_NODE"""] )
				snake_case_		:							Union[str, Any]			       =						int(os.environ["""RANK"""] )

				# number of nodes / node ID
				snake_case_		:							List[Any]			       =						params.world_size // params.n_gpu_per_node
				snake_case_		:							Union[str, Any]			       =						params.global_rank // params.n_gpu_per_node
				snake_case_		:							int			       =						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_		:							Any			       =						1
				snake_case_		:							List[str]			       =						0
				snake_case_		:							Any			       =						0
				snake_case_		:							List[Any]			       =						0
				snake_case_		:							List[Any]			       =						1
				snake_case_		:							int			       =						1
				snake_case_		:							Optional[Any]			       =						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_		:							Optional[int]			       =						params.node_id == 0 and params.local_rank == 0
		snake_case_		:							List[Any]			       =						params.n_nodes > 1

		# summary
		snake_case_		:							Dict			       =						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      UpperCAmelCase      (		lowerCamelCase_  :List[str] ):







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



'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch
@require_torch
@require_optimum
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoTokenizer.from_pretrained(_UpperCamelCase			)
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						tokenizer("""This is me"""  ,return_tensors="""pt"""			)

									snake_case_		:							List[str]			       =						model.to_bettertransformer()

									self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									snake_case_		:							List[str]			       =						model.generate(**_UpperCamelCase			)

									snake_case_		:							Dict			       =						model.reverse_bettertransformer()

									self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

											self.assertFalse(
											    any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

											snake_case_		:							Optional[Any]			       =						model_reloaded.generate(**_UpperCamelCase			)
											self.assertTrue(torch.allclose(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						model.to_bettertransformer()

									with tempfile.TemporaryDirectory() as tmpdirname:
											with self.assertRaises(_UpperCamelCase			):
													model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							Any			       =						model.reverse_bettertransformer()
											model.save_pretrained(_UpperCamelCase			)