



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,
)


lowerCamelCase		       =      getLogger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = 8	,				__UpperCamelCase = 10_24	,				__UpperCamelCase="val"	,				__UpperCamelCase=None	,				__UpperCamelCase=False	,				__UpperCamelCase="summarization"	,				__UpperCamelCase=None	,				__UpperCamelCase=1	,				__UpperCamelCase = None	,				__UpperCamelCase=""	,				**__UpperCamelCase	,				)	->						Dict:
				a__	:							Any      						= str(__UpperCamelCase )
				assert local_rank is not None
				torch.distributed.init_process_group(backend="nccl"	,				rank=__UpperCamelCase )

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

				a__	:							Any      						= AutoTokenizer.from_pretrained(__UpperCamelCase )
				logger.info(F'Inferred tokenizer type: {tokenizer.__class__}' )  # if this is wrong, check config.model_type.

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






def     SCREAMING_SNAKE_CASE(      )	->						List[str]:
				a__	:							List[str]      						= 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=__UpperCamelCase	,				help="like cnn_dm/test.source" )
				parser.add_argument(
				    "--model_name"	,				type=__UpperCamelCase	,				help="like facebook/bart-large-cnn,t5-base, etc."	,				default="sshleifer/distilbart-xsum-12-3"	,				)
				parser.add_argument("--save_dir"	,				type=__UpperCamelCase	,				help="where to save"	,				default="tmp_gen" )
				parser.add_argument("--max_source_length"	,				type=__UpperCamelCase	,				default=__UpperCamelCase )
				parser.add_argument(
				    "--type_path"	,				type=__UpperCamelCase	,				default="test"	,				help="which subset to evaluate typically train/val/test" )
				parser.add_argument("--task"	,				type=__UpperCamelCase	,				default="summarization"	,				help="used for task_specific_params + metrics" )
				parser.add_argument("--bs"	,				type=__UpperCamelCase	,				default=8	,				required=__UpperCamelCase	,				help="batch size" )
				parser.add_argument(
				    "--local_rank"	,				type=__UpperCamelCase	,				default=-1	,				required=__UpperCamelCase	,				help="should be passed by distributed.launch" )

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

				Path(args.save_dir ).mkdir(exist_ok=__UpperCamelCase )
				a__						,   a__	:							Tuple      						= eval_data_dir(
				    args.data_dir	,				__UpperCamelCase	,				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=__UpperCamelCase	,				**__UpperCamelCase	,				)

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

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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List:
				a__	:							Optional[Any]      						= []
				for partial_result in partial_results:
								records.extend(__UpperCamelCase )
				a__	:							Tuple      						= sorted(__UpperCamelCase	,				key=lambda __UpperCamelCase : x["id"] )
				a__	:							Tuple      						= [x["pred"] for x in records]
				return preds






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Dict[str, List]]:
				# WAIT FOR lots of .json files
				a__	:							Optional[Any]      						= time.time()
				logger.info("waiting for all nodes to finish" )
				a__	:							int      						= None
				while (time.time() - start_wait) < timeout:
								a__	:							List[str]      						= list(save_dir.glob("rank_*.json" ) )
								if len(__UpperCamelCase ) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												a__	:							int      						= lmap(__UpperCamelCase	,				__UpperCamelCase )
												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()







import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

lowerCamelCase		       =      logging.getLogger(__name__)


@dataclass
class 				_a						:





							'''simple docstring'''

							A				:Optional[int] =       field(
							    default=1_28       ,  metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Overwrite the cached preprocessed datasets or not."}			)
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "Whether to pad all samples to `max_seq_length`. "
							            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
							            "value if set."
							        )
							    }       ,  )





@dataclass
class 				_a						:





							'''simple docstring'''

							A				:str =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
							A				:str =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Train language if it is different from the evaluation language."}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,  )
							A				:Optional[bool] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}       ,  )
							A				:str =       field(
							    default="main"       ,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}       ,  )






def     SCREAMING_SNAKE_CASE(      )	->						int:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				a__	:							List[str]      						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				a__						,   a__						,   a__	:							Optional[int]      						= parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("run_xnli"	,				__UpperCamelCase )

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,				datefmt="%m/%d/%Y %H:%M:%S"	,				handlers=[logging.StreamHandler(sys.stdout )]	,				)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				a__	:							int      						= training_args.get_process_log_level()
				logger.setLevel(__UpperCamelCase )
				datasets.utils.logging.set_verbosity(__UpperCamelCase )
				transformers.utils.logging.set_verbosity(__UpperCamelCase )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
				    + F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
				logger.info(F'Training/evaluation parameters {training_args}' )

				# Detecting last checkpoint.
				a__	:							Union[str, Any]      						= None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								a__	:							Dict      						= get_last_checkpoint(training_args.output_dir )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
												raise ValueError(
												    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
												    "Use --overwrite_output_dir to overcome." )
								elif last_checkpoint is not None:
												logger.info(
												    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
												    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )

    # Set seed before initializing model.
				set_seed(training_args.seed )

				# In distributed training, the load_dataset function guarantees that only one local process can concurrently
				# download the dataset.
				# Downloading and loading xnli dataset from the hub.
				if training_args.do_train:
								if model_args.train_language is None:
												a__	:							Any      						= load_dataset(
												    "xnli"	,				model_args.language	,				split="train"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								else:
												a__	:							List[str]      						= load_dataset(
												    "xnli"	,				model_args.train_language	,				split="train"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							List[Any]      						= train_dataset.features["label"].names

				if training_args.do_eval:
								a__	:							Tuple      						= load_dataset(
								    "xnli"	,				model_args.language	,				split="validation"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							int      						= eval_dataset.features["label"].names

				if training_args.do_predict:
								a__	:							Dict      						= load_dataset(
								    "xnli"	,				model_args.language	,				split="test"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							Optional[Any]      						= predict_dataset.features["label"].names

				# Labels
				a__	:							List[Any]      						= len(__UpperCamelCase )

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				a__	:							List[Any]      						= AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,				num_labels=__UpperCamelCase	,				idalabel={str(__UpperCamelCase ): label for i, label in enumerate(__UpperCamelCase )}	,				labelaid={label: i for i, label in enumerate(__UpperCamelCase )}	,				finetuning_task="xnli"	,				cache_dir=model_args.cache_dir	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				)
				a__	:							Union[str, Any]      						= AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,				do_lower_case=model_args.do_lower_case	,				cache_dir=model_args.cache_dir	,				use_fast=model_args.use_fast_tokenizer	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				)
				a__	:							Optional[int]      						= AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path	,				from_tf=bool(".ckpt" in model_args.model_name_or_path )	,				config=__UpperCamelCase	,				cache_dir=model_args.cache_dir	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,				)

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								a__	:							Dict      						= "max_length"
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								a__	:							Optional[Any]      						= False

				def preprocess_function(__UpperCamelCase ):
								# Tokenize the texts
								return tokenizer(
								    examples["premise"]	,				examples["hypothesis"]	,				padding=__UpperCamelCase	,				max_length=data_args.max_seq_length	,				truncation=__UpperCamelCase	,				)

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												a__	:							int      						= min(len(__UpperCamelCase )	,				data_args.max_train_samples )
												a__	:							Tuple      						= train_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="train dataset map pre-processing" ):
												a__	:							Optional[int]      						= train_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on train dataset"	,				)
								# Log a few random samples from the training set:
								for index in random.sample(range(len(__UpperCamelCase ) )	,				3 ):
												logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												a__	:							Optional[int]      						= min(len(__UpperCamelCase )	,				data_args.max_eval_samples )
												a__	:							Optional[Any]      						= eval_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="validation dataset map pre-processing" ):
												a__	:							List[Any]      						= eval_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on validation dataset"	,				)

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												a__	:							int      						= min(len(__UpperCamelCase )	,				data_args.max_predict_samples )
												a__	:							List[str]      						= predict_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="prediction dataset map pre-processing" ):
												a__	:							Union[str, Any]      						= predict_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on prediction dataset"	,				)

    # Get the metric function
				a__	:							List[Any]      						= evaluate.load("xnli" )

				# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(__UpperCamelCase ):
								a__	:							Dict      						= p.predictions[0] if isinstance(p.predictions	,				__UpperCamelCase ) else p.predictions
								a__	:							Any      						= np.argmax(__UpperCamelCase	,				axis=1 )
								return metric.compute(predictions=__UpperCamelCase	,				references=p.label_ids )

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								a__	:							List[Any]      						= default_data_collator
				elif training_args.fpaa:
								a__	:							List[Any]      						= DataCollatorWithPadding(__UpperCamelCase	,				pad_to_multiple_of=8 )
				else:
								a__	:							Tuple      						= None

				# Initialize our Trainer
				a__	:							List[str]      						= Trainer(
				    model=__UpperCamelCase	,				args=__UpperCamelCase	,				train_dataset=train_dataset if training_args.do_train else None	,				eval_dataset=eval_dataset if training_args.do_eval else None	,				compute_metrics=__UpperCamelCase	,				tokenizer=__UpperCamelCase	,				data_collator=__UpperCamelCase	,				)

				# Training
				if training_args.do_train:
								a__	:							str      						= None
								if training_args.resume_from_checkpoint is not None:
												a__	:							int      						= training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												a__	:							Optional[int]      						= last_checkpoint
								a__	:							Any      						= trainer.train(resume_from_checkpoint=__UpperCamelCase )
								a__	:							Tuple      						= train_result.metrics
								a__	:							Union[str, Any]      						= (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(__UpperCamelCase )
								)
								a__	:							Optional[int]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics("train"	,				__UpperCamelCase )
								trainer.save_metrics("train"	,				__UpperCamelCase )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info("*** Evaluate ***" )
								a__	:							Optional[int]      						= trainer.evaluate(eval_dataset=__UpperCamelCase )

								a__	:							Tuple      						= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__UpperCamelCase )
								a__	:							List[Any]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.log_metrics("eval"	,				__UpperCamelCase )
								trainer.save_metrics("eval"	,				__UpperCamelCase )

				# Prediction
				if training_args.do_predict:
								logger.info("*** Predict ***" )
								a__						,   a__						,   a__	:							int      						= trainer.predict(__UpperCamelCase	,				metric_key_prefix="predict" )

								a__	:							int      						= (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__UpperCamelCase )
								)
								a__	:							Union[str, Any]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.log_metrics("predict"	,				__UpperCamelCase )
								trainer.save_metrics("predict"	,				__UpperCamelCase )

								a__	:							Dict      						= np.argmax(__UpperCamelCase	,				axis=1 )
								a__	:							str      						= os.path.join(training_args.output_dir	,				"predictions.txt" )
								if trainer.is_world_process_zero():
												with open(__UpperCamelCase	,				"w" ) as writer:
																writer.write("index\tprediction\n" )
																for index, item in enumerate(__UpperCamelCase ):
																				a__	:							int      						= label_list[item]
																				writer.write(F'{index}\t{item}\n' )


if __name__ == "__main__":
						main()


