






from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
		from .deepspeed import (
		    DeepSpeedEngineWrapper,
		    DeepSpeedOptimizerWrapper,
		    DeepSpeedSchedulerWrapper,
		    DummyOptim,
		    DummyScheduler,
		    HfDeepSpeedConfig,
		)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers




"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


SCREAMING_SNAKE_CASE :    List[str]									=				getLogger(__name__)






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      str     ,   _snake_case						:      str     ,   _snake_case						:      int = 8     ,   _snake_case						:      int = 1_024     ,   _snake_case						:      Any="val"     ,   _snake_case						:      Tuple=None     ,   _snake_case						:      Any=False     ,   _snake_case						:      str="summarization"     ,   _snake_case						:      Dict=None     ,   _snake_case						:      Optional[Any]=1     ,   _snake_case						:      Dict = None     ,   _snake_case						:      List[Any]=""     ,   **_snake_case						:      int     ,   )     ->Dict:
	"""simple docstring"""





	__snake_case	:						int						=				str(_snake_case     )
	assert local_rank is not None
	torch.distributed.init_process_group(backend='''nccl'''     ,   rank=_snake_case     )

	__snake_case	:						Optional[Any]						=				Path(_snake_case     )
	__snake_case	:						str						=				save_dir.joinpath(f"""rank_{local_rank}_output.json"""     )
	torch.cuda.set_device(_snake_case     )
	__snake_case	:						Tuple						=				AutoModelForSeqaSeqLM.from_pretrained(_snake_case     ).cuda()
	if fpaa:
		__snake_case	:						List[str]						=				model.half()
	# determine if we need to increase num_beams
	use_task_specific_params(_snake_case     ,   _snake_case     )  # update config with task specific params
	__snake_case	:						Dict						=				generate_kwargs.pop('''num_beams'''     ,   model.config.num_beams     )  # AttributeError risk?
	if num_return_sequences > num_beams:
		__snake_case	:						Optional[Any]						=				num_return_sequences

	__snake_case	:						Dict						=				AutoTokenizer.from_pretrained(_snake_case     )
	logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

	if max_source_length is None:
		__snake_case	:						List[str]						=				tokenizer.model_max_length
	if prefix is None:
		__snake_case	:						List[str]						=				prefix or getattr(model.config     ,   '''prefix'''     ,   ''''''     ) or ''''''
	__snake_case	:						List[str]						=				SeqaSeqDataset(
	    _snake_case     ,   _snake_case     ,   _snake_case     ,   max_target_length=1_024     ,   type_path=_snake_case     ,   n_obs=_snake_case     ,   prefix=_snake_case     ,   **_snake_case     ,   )
	# I set shuffle=True for a more accurate progress bar.
	# If all the longest samples are first, the prog bar estimate is too high at the beginning.
	__snake_case	:						Union[str, Any]						=				ds.make_sortish_sampler(_snake_case     ,   distributed=_snake_case     ,   add_extra_examples=_snake_case     ,   shuffle=_snake_case     )
	__snake_case	:						List[Any]						=				DataLoader(_snake_case     ,   sampler=_snake_case     ,   batch_size=_snake_case     ,   collate_fn=ds.collate_fn     )
	__snake_case	:						Union[str, Any]						=				[]
	for batch in tqdm(_snake_case     ):
		__snake_case	:						Tuple						=				model.generate(
		    input_ids=batch['''input_ids'''].to(model.device     )     ,   attention_mask=batch['''attention_mask'''].to(model.device     )     ,   num_return_sequences=_snake_case     ,   num_beams=_snake_case     ,   **_snake_case     ,   )
		__snake_case	:						List[Any]						=				tokenizer.batch_decode(_snake_case     ,   skip_special_tokens=_snake_case     ,   clean_up_tokenization_spaces=_snake_case     )
		__snake_case	:						List[str]						=				batch['''ids''']
		if num_return_sequences > 1:
			__snake_case	:						Dict						=				chunks(_snake_case     ,   _snake_case     )  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(_snake_case     ):
			results.append({'''pred''': pred, '''id''': ids[i].item()}     )
	save_json(_snake_case     ,   _snake_case     )
	return results, sampler.num_replicas






def 		lowercase							(     )     ->int:
	"""simple docstring"""





	__snake_case	:						Any						=				argparse.ArgumentParser(
	    epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''     )
	parser.add_argument('''--data_dir'''     ,   type=_snake_case     ,   help='''like cnn_dm/test.source'''     )
	parser.add_argument(
	    '''--model_name'''     ,   type=_snake_case     ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''     ,   default='''sshleifer/distilbart-xsum-12-3'''     ,   )
	parser.add_argument('''--save_dir'''     ,   type=_snake_case     ,   help='''where to save'''     ,   default='''tmp_gen'''     )
	parser.add_argument('''--max_source_length'''     ,   type=_snake_case     ,   default=_snake_case     )
	parser.add_argument(
	    '''--type_path'''     ,   type=_snake_case     ,   default='''test'''     ,   help='''which subset to evaluate typically train/val/test'''     )
	parser.add_argument('''--task'''     ,   type=_snake_case     ,   default='''summarization'''     ,   help='''used for task_specific_params + metrics'''     )
	parser.add_argument('''--bs'''     ,   type=_snake_case     ,   default=8     ,   required=_snake_case     ,   help='''batch size'''     )
	parser.add_argument(
	    '''--local_rank'''     ,   type=_snake_case     ,   default=-1     ,   required=_snake_case     ,   help='''should be passed by distributed.launch'''     )

	parser.add_argument(
	    '''--n_obs'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     ,   help='''How many observations. Defaults to all.'''     )
	parser.add_argument(
	    '''--num_return_sequences'''     ,   type=_snake_case     ,   default=1     ,   required=_snake_case     ,   help='''How many sequences to return'''     )
	parser.add_argument(
	    '''--sync_timeout'''     ,   type=_snake_case     ,   default=600     ,   required=_snake_case     ,   help='''How long should master process wait for other processes to finish.'''     ,   )
	parser.add_argument('''--src_lang'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     )
	parser.add_argument('''--tgt_lang'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     )
	parser.add_argument(
	    '''--prefix'''     ,   type=_snake_case     ,   required=_snake_case     ,   default=_snake_case     ,   help='''will be added to the begininng of src examples'''     )
	parser.add_argument('''--fp16'''     ,   action='''store_true'''     )
	parser.add_argument('''--debug'''     ,   action='''store_true'''     )
	__snake_case	:						str						=				time.time()
	__snake_case	,							__snake_case	:						Any						=				parser.parse_known_args()
	__snake_case	:						List[Any]						=				parse_numeric_n_bool_cl_kwargs(_snake_case     )
	if generate_kwargs and args.local_rank <= 0:
		print(f"""parsed the following generate kwargs: {generate_kwargs}"""     )
	__snake_case	:						List[Any]						=				Path(args.save_dir + '''_tmp'''     )
	Path(_snake_case     ).mkdir(exist_ok=_snake_case     )  # this handles locking.
	__snake_case	:						Optional[int]						=				list(json_save_dir.glob('''rank_*.json'''     )     )
	if intermediate_files:
		raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""     )
		# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
	__snake_case	:						Dict						=				{}
	if args.src_lang is not None:
		__snake_case	:						Dict						=				args.src_lang
	if args.tgt_lang is not None:
		__snake_case	:						Dict						=				args.tgt_lang

	Path(args.save_dir     ).mkdir(exist_ok=_snake_case     )
	__snake_case	,							__snake_case	:						List[Any]						=				eval_data_dir(
	    args.data_dir     ,   _snake_case     ,   args.model_name     ,   type_path=args.type_path     ,   bs=args.bs     ,   fpaa=args.fpaa     ,   task=args.task     ,   local_rank=args.local_rank     ,   n_obs=args.n_obs     ,   max_source_length=args.max_source_length     ,   num_return_sequences=args.num_return_sequences     ,   prefix=args.prefix     ,   dataset_kwargs=_snake_case     ,   **_snake_case     ,   )

	if args.local_rank <= 0:
		__snake_case	:						int						=				Path(args.save_dir     )
		save_dir.mkdir(exist_ok=_snake_case     )
		__snake_case	:						Optional[Any]						=				gather_results_from_each_node(_snake_case     ,   _snake_case     ,   args.sync_timeout     )
		__snake_case	:						str						=				combine_partial_results(_snake_case     )
		if args.num_return_sequences > 1:
			__snake_case	:						List[Any]						=				save_dir.joinpath('''pseudolabel_results.json'''     )
			print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""     )
			save_json(_snake_case     ,   _snake_case     )
			return
		__snake_case	:						Tuple						=				Path(args.data_dir     ).joinpath(args.type_path + '''.target'''     )
		with open(_snake_case     ) as f:
			__snake_case	:						Optional[Any]						=				[x.rstrip() for x in f.readlines()][: len(_snake_case     )]

		# Calculate metrics, save metrics,  and save _generations.txt
		__snake_case	:						List[str]						=				'''translation''' in args.task
		__snake_case	:						List[Any]						=				calculate_bleu if calc_bleu else calculate_rouge
		__snake_case	:						Dict						=				'''bleu''' if calc_bleu else '''rouge'''
		__snake_case	:						Dict						=				score_fn(_snake_case     ,   _snake_case     )
		__snake_case	:						int						=				len(_snake_case     )
		__snake_case	:						Dict						=				time.time() - start_time
		__snake_case	:						Optional[Any]						=				round(runtime / metrics['''n_obs''']     ,   4     )
		__snake_case	:						List[Any]						=				num_replicas
		# TODO(@stas00): add whatever metadata to metrics
		__snake_case	:						int						=				save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""     )
		save_json(_snake_case     ,   _snake_case     ,   indent=_snake_case     )
		print(_snake_case     )
		write_txt_file(_snake_case     ,   save_dir.joinpath(f"""{args.type_path}_generations.txt"""     )     )
		if args.debug:
			write_txt_file(_snake_case     ,   save_dir.joinpath(f"""{args.type_path}.target"""     )     )
		else:
			shutil.rmtree(_snake_case     )






def 		lowercase							(     _snake_case						:      Union[str, Any]     )     ->List:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				[]
	for partial_result in partial_results:
		records.extend(_snake_case     )
	__snake_case	:						List[str]						=				sorted(_snake_case     ,   key=lambda _snake_case     : x["id"]     )
	__snake_case	:						Tuple						=				[x['''pred'''] for x in records]
	return preds






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      List[str]     ,   _snake_case						:      List[Any]     )     ->List[Dict[str, List]]:
	"""simple docstring"""





	__snake_case	:						List[str]						=				time.time()
	logger.info('''waiting for all nodes to finish'''     )
	__snake_case	:						List[str]						=				None
	while (time.time() - start_wait) < timeout:
		__snake_case	:						Any						=				list(save_dir.glob('''rank_*.json'''     )     )
		if len(_snake_case     ) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			__snake_case	:						Tuple						=				lmap(_snake_case     ,   _snake_case     )
			return json_data
		except JSONDecodeError:
			continue
	else:
		raise TimeoutError('''Rank 0 gave up on waiting for other processes'''     )
	# Unreachable


if __name__ == "__main__":
			# Usage for MT:
			run_generate()





