code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import argparse
  import datetime
  import json
  import time
  import warnings
  from logging import getLogger
  from pathlib import Path
  from typing import Dict, List
  
  import torch
  from tqdm import tqdm
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params
  
  
  _lowerCamelCase							:		Any										=						getLogger(__name__)
  
  
  _lowerCamelCase							:		Union[str, Any]										=						'cuda' if torch.cuda.is_available() else 'cpu'
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_ = 8		,							lowercase_ = DEFAULT_DEVICE		,							lowercase_=False		,							lowercase_="summarization"		,							lowercase_=None		,							**lowercase_		,							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = Path(lowercase_						).open("w"		,							encoding="utf-8"						)
  		_snake_case			 = str(lowercase_						)
  		_snake_case			 = AutoModelForSeqaSeqLM.from_pretrained(lowercase_						).to(lowercase_						)
  		if fpaa:
  				_snake_case			 = model.half()
  
  		_snake_case			 = AutoTokenizer.from_pretrained(lowercase_						)
  		logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''						)  # if this is wrong, check config.model_type.
  
  		_snake_case			 = time.time()
  		# update config with task specific params
  		use_task_specific_params(lowercase_		,							lowercase_						)
  		if prefix is None:
  				_snake_case			 = prefix or getattr(model.config		,							"prefix"		,							""						) or ""
  		for examples_chunk in tqdm(list(chunks(lowercase_		,							lowercase_						)						)						):
  				_snake_case			 = [prefix + text for text in examples_chunk]
  				_snake_case			 = tokenizer(lowercase_		,							return_tensors="pt"		,							truncation=lowercase_		,							padding="longest"						).to(lowercase_						)
  				_snake_case			 = model.generate(
  				    input_ids=batch.input_ids		,							attention_mask=batch.attention_mask		,							**lowercase_		,							)
  				_snake_case			 = tokenizer.batch_decode(lowercase_		,							skip_special_tokens=lowercase_		,							clean_up_tokenization_spaces=lowercase_						)
  				for hypothesis in dec:
  						fout.write(hypothesis + "\n"						)
  						fout.flush()
  		fout.close()
  		_snake_case			 = int(time.time() - start_time						)  # seconds
  		_snake_case			 = len(lowercase_						)
  		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,							4						)}
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_=True						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = argparse.ArgumentParser()
  		parser.add_argument("model_name"		,							type=lowercase_		,							help="like facebook/bart-large-cnn,t5-base, etc."						)
  		parser.add_argument("input_path"		,							type=lowercase_		,							help="like cnn_dm/test.source"						)
  		parser.add_argument("save_path"		,							type=lowercase_		,							help="where to save summaries"						)
  		parser.add_argument("--reference_path"		,							type=lowercase_		,							required=lowercase_		,							help="like cnn_dm/test.target"						)
  		parser.add_argument("--score_path"		,							type=lowercase_		,							required=lowercase_		,							default="metrics.json"		,							help="where to save metrics"						)
  		parser.add_argument("--device"		,							type=lowercase_		,							required=lowercase_		,							default=lowercase_		,							help="cuda, cuda:1, cpu etc."						)
  		parser.add_argument(
  		    "--prefix"		,							type=lowercase_		,							required=lowercase_		,							default=lowercase_		,							help="will be added to the begininng of src examples"						)
  		parser.add_argument("--task"		,							type=lowercase_		,							default="summarization"		,							help="used for task_specific_params + metrics"						)
  		parser.add_argument("--bs"		,							type=lowercase_		,							default=8		,							required=lowercase_		,							help="batch size"						)
  		parser.add_argument(
  		    "--n_obs"		,							type=lowercase_		,							default=-1		,							required=lowercase_		,							help="How many observations. Defaults to all."						)
  		parser.add_argument("--fp16"		,							action="store_true"						)
  		parser.add_argument("--dump-args"		,							action="store_true"		,							help="print the custom hparams with the results"						)
  		parser.add_argument(
  		    "--info"		,							nargs="?"		,							type=lowercase_		,							const=datetime_now()		,							help=(
  		        "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
  		        " lang=en-ru. If no value is passed, the current datetime string will be used."
  		    )		,							)
  		# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
  		_snake_case ,					_snake_case			 = parser.parse_known_args()
  		_snake_case			 = parse_numeric_n_bool_cl_kwargs(lowercase_						)
  		if parsed_args and verbose:
  				print(f'''parsed the following generate kwargs: {parsed_args}'''						)
  		with open(args.input_path						) as f:
  				_snake_case			 = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
  		if args.n_obs > 0:
  				_snake_case			 = examples[: args.n_obs]
  		Path(args.save_path						).parent.mkdir(exist_ok=lowercase_						)
  		if args.reference_path is None and Path(args.score_path						).exists():
  				warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''						)
  		_snake_case			 = generate_summaries_or_translations(
  		    lowercase_		,							args.save_path		,							args.model_name		,							batch_size=args.bs		,							device=args.device		,							fpaa=args.fpaa		,							task=args.task		,							prefix=args.prefix		,							**lowercase_		,							)
  
  		if args.reference_path is None:
  				return {}
  
  		# Compute scores
  		_snake_case			 = calculate_bleu if "translation" in args.task else calculate_rouge
  		_snake_case			 = [x.rstrip() for x in open(args.save_path						).readlines()]
  		_snake_case			 = [x.rstrip() for x in open(args.reference_path						).readlines()][: len(lowercase_						)]
  		_snake_case			 = score_fn(lowercase_		,							lowercase_						)
  		scores.update(lowercase_						)
  
  		if args.dump_args:
  				scores.update(lowercase_						)
  		if args.info:
  				_snake_case			 = args.info
  
  		if verbose:
  				print(lowercase_						)
  
  		if args.score_path is not None:
  				json.dump(lowercase_		,							open(args.score_path		,							"w"						)						)
  
  		return scores
  
  
  if __name__ == "__main__":
  						# Usage for MT:
  						# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
  						run_generate(verbose=True)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import importlib
  import importlib.metadata
  import os
  import warnings
  from distutils.util import strtobool
  from functools import lru_cache
  
  import torch
  from packaging import version
  from packaging.version import parse
  
  from .environment import parse_flag_from_env
  from .versions import compare_versions, is_torch_version
  
  
  try:
  						import torch_xla.core.xla_model as xm  # noqa: F401
  
  						_lowerCamelCase							:		int										=						True
  except ImportError:
  						_lowerCamelCase							:		List[str]										=						False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  _lowerCamelCase							:		List[Any]										=						torch.distributed.is_available()
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = importlib.util.find_spec(lowercase_						) is not None
  		if package_exists:
  				try:
  						_snake_case			 = importlib.metadata.metadata(lowercase_						)
  						return True
  				except importlib.metadata.PackageNotFoundError:
  						return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _torch_distributed_available
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		try:
  				pass
  		except ImportError:
  				print(
  				    "Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not"
  				    " detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"
  				    " Bindings for PyTorch*."						)
  		return (
  		    importlib.util.find_spec("torch_ccl"						) is not None
  		    or importlib.util.find_spec("oneccl_bindings_for_pytorch"						) is not None
  		)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return importlib.metadata.version("oneccl_bind_pt"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("transformer_engine"						)
  
  
  
  
  
  
  @lru_cache
  def 	_lowercase	(			lowercase_=True						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if torch.cuda.is_available():
  				return False
  		if _tpu_available and check_device:
  				try:
  						# Will raise a RuntimeError if no XLA configuration is found
  						_snake_case			 = xm.xla_device()
  						return True
  				except RuntimeError:
  						return False
  		return _tpu_available
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("deepspeed"						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_=False						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if is_tpu_available():
  				return not ignore_tpu
  		if torch.cuda.is_available():
  				return torch.cuda.is_bfaa_supported()
  		if is_npu_available():
  				return False
  		return True
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = _is_package_available("bitsandbytes"						)
  		if package_exists:
  				_snake_case			 = version.parse(importlib.metadata.version("bitsandbytes"						)						)
  				return compare_versions(lowercase_		,							">="		,							"0.39.0"						)
  		return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = _is_package_available("bitsandbytes"						)
  		if package_exists:
  				_snake_case			 = version.parse(importlib.metadata.version("bitsandbytes"						)						)
  				return compare_versions(lowercase_		,							">="		,							"0.37.2"						)
  		return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("bitsandbytes"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if strtobool(os.environ.get("ACCELERATE_USE_MEGATRON_LM"		,							"False"						)						) == 1:
  				_snake_case			 = importlib.util.find_spec("megatron"						) is not None
  				if package_exists:
  						try:
  								_snake_case			 = parse(importlib.metadata.version("megatron-lm"						)						)
  								return compare_versions(lowercase_		,							">="		,							"2.2.0"						)
  						except Exception as e:
  								warnings.warn(f'''Parse Megatron version failed. Exception:{e}'''						)
  								return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("safetensors"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("transformers"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("datasets"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("aim"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("tensorboard"						) or _is_package_available("tensorboardX"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("wandb"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("comet_ml"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("boto3"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if _is_package_available("rich"						):
  				if "ACCELERATE_DISABLE_RICH" in os.environ:
  						warnings.warn(
  						    "`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`."						)
  						return not parse_flag_from_env("ACCELERATE_DISABLE_RICH"		,							lowercase_						)
  				return parse_flag_from_env("ACCELERATE_ENABLE_RICH"		,							lowercase_						)
  		return False
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("sagemaker"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("tqdm"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return _is_package_available("mlflow"						)
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return is_torch_version(">="		,							"1.12"						) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def get_major_and_minor_from_version(lowercase_						):
  				return str(version.parse(lowercase_						).major						) + "." + str(version.parse(lowercase_						).minor						)
  
  		_snake_case			 = importlib.metadata.version("torch"						)
  		if importlib.util.find_spec("intel_extension_for_pytorch"						) is None:
  				return False
  		_snake_case			 = "N/A"
  		try:
  				_snake_case			 = importlib.metadata.version("intel_extension_for_pytorch"						)
  		except importlib.metadata.PackageNotFoundError:
  				return False
  		_snake_case			 = get_major_and_minor_from_version(_torch_version						)
  		_snake_case			 = get_major_and_minor_from_version(_ipex_version						)
  		if torch_major_and_minor != ipex_major_and_minor:
  				warnings.warn(
  				    f'''Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'''
  				    f''' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'''						)
  				return False
  		return True
  
  
  
  
  
  
  @lru_cache
  def 	_lowercase	(			lowercase_=False						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if importlib.util.find_spec("torch"						) is None or importlib.util.find_spec("torch_npu"						) is None:
  				return False
  
  		import torch
  		import torch_npu  # noqa: F401
  
  		if check_device:
  				try:
  						# Will raise a RuntimeError if no NPU is found
  						_snake_case			 = torch.npu.device_count()
  						return torch.npu.is_available()
  				except RuntimeError:
  						return False
  		return hasattr(lowercase_		,							"npu"						) and torch.npu.is_available()
  
  
  
  
  
  
  @lru_cache
  def 	_lowercase	(			lowercase_=False						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if not parse_flag_from_env("ACCELERATE_USE_XPU"		,							default=lowercase_						):
  				return False
  		if is_ipex_available():
  				import torch
  
  				if is_torch_version("<="		,							"1.12"						):
  						return False
  		else:
  				return False
  
  		import intel_extension_for_pytorch  # noqa: F401
  
  		if check_device:
  				try:
  						# Will raise a RuntimeError if no XPU  is found
  						_snake_case			 = torch.xpu.device_count()
  						return torch.xpu.is_available()
  				except RuntimeError:
  						return False
  		return hasattr(lowercase_		,							"xpu"						) and torch.xpu.is_available()
  
  
  
  
  
  