






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,
)


_UpperCAmelCase			:		Any   				=    getLogger(__name__)





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]     ,					lowercase__						:       str     ,					lowercase__						:       str     ,					lowercase__						:       int = 8     ,					lowercase__						:       int = 10_24     ,					lowercase__						:       List[str]="val"     ,					lowercase__						:       Union[str, Any]=None     ,					lowercase__						:       str=False     ,					lowercase__						:       Optional[Any]="summarization"     ,					lowercase__						:       int=None     ,					lowercase__						:       Any=1     ,					lowercase__						:       Dict = None     ,					lowercase__						:       int=""     ,					**lowercase__						:       str     ,					)		->    Dict:


				'''simple docstring'''




				lowerCAmelCase__      			=						str(_lowercase       )
				assert local_rank is not None
				torch.distributed.init_process_group(backend='''nccl'''     ,					rank=_lowercase       )

				lowerCAmelCase__      			=						Path(_lowercase       )
				lowerCAmelCase__      			=						save_dir.joinpath(f'rank_{local_rank}_output.json'       )
				torch.cuda.set_device(_lowercase       )
				lowerCAmelCase__      			=						AutoModelForSeqaSeqLM.from_pretrained(_lowercase       ).cuda()
				if fpaa:
								lowerCAmelCase__      			=						model.half()
				# determine if we need to increase num_beams
				use_task_specific_params(_lowercase     ,					_lowercase       )  # update config with task specific params
				lowerCAmelCase__      			=						generate_kwargs.pop('''num_beams'''     ,					model.config.num_beams       )  # AttributeError risk?
				if num_return_sequences > num_beams:
								lowerCAmelCase__      			=						num_return_sequences

				lowerCAmelCase__      			=						AutoTokenizer.from_pretrained(_lowercase       )
				logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'       )  # if this is wrong, check config.model_type.

				if max_source_length is None:
								lowerCAmelCase__      			=						tokenizer.model_max_length
				if prefix is None:
								lowerCAmelCase__      			=						prefix or getattr(model.config     ,					'''prefix'''     ,					''''''       ) or ""
				lowerCAmelCase__      			=						SeqaSeqDataset(
				    _lowercase     ,					_lowercase     ,					_lowercase     ,					max_target_length=10_24     ,					type_path=_lowercase     ,					n_obs=_lowercase     ,					prefix=_lowercase     ,					**_lowercase     ,					)
				# 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.
				lowerCAmelCase__      			=						ds.make_sortish_sampler(_lowercase     ,					distributed=_lowercase     ,					add_extra_examples=_lowercase     ,					shuffle=_lowercase       )
				lowerCAmelCase__      			=						DataLoader(_lowercase     ,					sampler=_lowercase     ,					batch_size=_lowercase     ,					collate_fn=ds.collate_fn       )
				lowerCAmelCase__      			=						[]
				for batch in tqdm(_lowercase       ):
								lowerCAmelCase__      			=						model.generate(
								    input_ids=batch['''input_ids'''].to(model.device       )     ,					attention_mask=batch['''attention_mask'''].to(model.device       )     ,					num_return_sequences=_lowercase     ,					num_beams=_lowercase     ,					**_lowercase     ,					)
								lowerCAmelCase__      			=						tokenizer.batch_decode(_lowercase     ,					skip_special_tokens=_lowercase     ,					clean_up_tokenization_spaces=_lowercase       )
								lowerCAmelCase__      			=						batch["ids"]
								if num_return_sequences > 1:
												lowerCAmelCase__      			=						chunks(_lowercase     ,					_lowercase       )  # batch size chunks, each of size num_return_seq
								for i, pred in enumerate(_lowercase       ):
												results.append({'''pred''': pred, '''id''': ids[i].item()}       )
				save_json(_lowercase     ,					_lowercase       )
				return results, sampler.num_replicas





def 	lowerCAmelCase_		()		->    Tuple:


				'''simple docstring'''




				lowerCAmelCase__      			=						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=_lowercase     ,					help='''like cnn_dm/test.source'''       )
				parser.add_argument(
				    '''--model_name'''     ,					type=_lowercase     ,					help='''like facebook/bart-large-cnn,t5-base, etc.'''     ,					default='''sshleifer/distilbart-xsum-12-3'''     ,					)
				parser.add_argument('''--save_dir'''     ,					type=_lowercase     ,					help='''where to save'''     ,					default='''tmp_gen'''       )
				parser.add_argument('''--max_source_length'''     ,					type=_lowercase     ,					default=_lowercase       )
				parser.add_argument(
				    '''--type_path'''     ,					type=_lowercase     ,					default='''test'''     ,					help='''which subset to evaluate typically train/val/test'''       )
				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(
				    '''--local_rank'''     ,					type=_lowercase     ,					default=-1     ,					required=_lowercase     ,					help='''should be passed by distributed.launch'''       )

				parser.add_argument(
				    '''--n_obs'''     ,					type=_lowercase     ,					default=_lowercase     ,					required=_lowercase     ,					help='''How many observations. Defaults to all.'''       )
				parser.add_argument(
				    '''--num_return_sequences'''     ,					type=_lowercase     ,					default=1     ,					required=_lowercase     ,					help='''How many sequences to return'''       )
				parser.add_argument(
				    '''--sync_timeout'''     ,					type=_lowercase     ,					default=6_00     ,					required=_lowercase     ,					help='''How long should master process wait for other processes to finish.'''     ,					)
				parser.add_argument('''--src_lang'''     ,					type=_lowercase     ,					default=_lowercase     ,					required=_lowercase       )
				parser.add_argument('''--tgt_lang'''     ,					type=_lowercase     ,					default=_lowercase     ,					required=_lowercase       )
				parser.add_argument(
				    '''--prefix'''     ,					type=_lowercase     ,					required=_lowercase     ,					default=_lowercase     ,					help='''will be added to the begininng of src examples'''       )
				parser.add_argument('''--fp16'''     ,					action='''store_true'''       )
				parser.add_argument('''--debug'''     ,					action='''store_true'''       )
				lowerCAmelCase__      			=						time.time()
				lowerCAmelCase__      			=						parser.parse_known_args()
				lowerCAmelCase__      			=						parse_numeric_n_bool_cl_kwargs(_lowercase       )
				if generate_kwargs and args.local_rank <= 0:
								print(f'parsed the following generate kwargs: {generate_kwargs}'       )
				lowerCAmelCase__      			=						Path(args.save_dir + '''_tmp'''       )
				Path(_lowercase       ).mkdir(exist_ok=_lowercase       )  # this handles locking.
				lowerCAmelCase__      			=						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.
				lowerCAmelCase__      			=						{}
				if args.src_lang is not None:
								lowerCAmelCase__      			=						args.src_lang
				if args.tgt_lang is not None:
								lowerCAmelCase__      			=						args.tgt_lang

				Path(args.save_dir       ).mkdir(exist_ok=_lowercase       )
				lowerCAmelCase__      			=						eval_data_dir(
				    args.data_dir     ,					_lowercase     ,					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=_lowercase     ,					**_lowercase     ,					)

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

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





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    List:


				'''simple docstring'''




				lowerCAmelCase__      			=						[]
				for partial_result in partial_results:
								records.extend(_lowercase       )
				lowerCAmelCase__      			=						sorted(_lowercase     ,					key=lambda lowercase__       : x["id"]       )
				lowerCAmelCase__      			=						[x["pred"] for x in records]
				return preds





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,					lowercase__						:       Optional[int]     ,					lowercase__						:       str       )		->    List[Dict[str, List]]:


				'''simple docstring'''




				lowerCAmelCase__      			=						time.time()
				logger.info('''waiting for all nodes to finish'''       )
				lowerCAmelCase__      			=						None
				while (time.time() - start_wait) < timeout:
								lowerCAmelCase__      			=						list(save_dir.glob('''rank_*.json'''       )       )
								if len(_lowercase       ) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												lowerCAmelCase__      			=						lmap(_lowercase     ,					_lowercase       )
												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()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase			:		List[Any]   				=    {
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    ["MobileNetV2FeatureExtractor"]
			_UpperCAmelCase			:		Union[str, Any]   				=    ["MobileNetV2ImageProcessor"]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MobileNetV2ForImageClassification",
			    "MobileNetV2ForSemanticSegmentation",
			    "MobileNetV2Model",
			    "MobileNetV2PreTrainedModel",
			    "load_tf_weights_in_mobilenet_v2",
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaForSemanticSegmentation,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





