



import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MPNetForMaskedLM,
						    MPNetForMultipleChoice,
						    MPNetForQuestionAnswering,
						    MPNetForSequenceClassification,
						    MPNetForTokenClassification,
						    MPNetModel,
						)


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=13 ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=False ,					__UpperCAmelCase=True ,					__UpperCAmelCase=99 ,					__UpperCAmelCase=64 ,					__UpperCAmelCase=5 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=64 ,					__UpperCAmelCase="gelu" ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=512 ,					__UpperCAmelCase=16 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=None ,					):







											"""simple docstring"""



											a__	:							str      						= parent
											a__	:							Optional[Any]      						= batch_size
											a__	:							List[str]      						= seq_length
											a__	:							List[Any]      						= is_training
											a__	:							List[str]      						= use_input_mask
											a__	:							List[Any]      						= use_token_type_ids
											a__	:							Tuple      						= use_labels
											a__	:							int      						= vocab_size
											a__	:							Optional[int]      						= hidden_size
											a__	:							Tuple      						= num_hidden_layers
											a__	:							str      						= num_attention_heads
											a__	:							str      						= intermediate_size
											a__	:							Dict      						= hidden_act
											a__	:							Dict      						= hidden_dropout_prob
											a__	:							List[Any]      						= attention_probs_dropout_prob
											a__	:							List[Any]      						= max_position_embeddings
											a__	:							List[Any]      						= type_vocab_size
											a__	:							Union[str, Any]      						= type_sequence_label_size
											a__	:							Any      						= initializer_range
											a__	:							Dict      						= num_labels
											a__	:							List[Any]      						= num_choices
											a__	:							List[str]      						= scope



							def 		_A	(							self				):







											"""simple docstring"""



											return MPNetConfig.from_pretrained("microsoft/mpnet-base"				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size				)

											a__	:							Any      						= None
											if self.use_input_mask:
															a__	:							str      						= random_attention_mask([self.batch_size, self.seq_length]				)

											a__	:							Any      						= None
											a__	:							int      						= None
											a__	:							Dict      						= None
											if self.use_labels:
															a__	:							Dict      						= ids_tensor([self.batch_size] ,					self.type_sequence_label_size				)
															a__	:							int      						= ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels				)
															a__	:							str      						= ids_tensor([self.batch_size] ,					self.num_choices				)

											a__	:							Any      						= self.get_config()
											return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



							def 		_A	(							self				):







											"""simple docstring"""



											return MPNetConfig(
											    vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[Any]      						= MPNetModel(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Union[str, Any]      						= model(__UpperCAmelCase ,					__UpperCAmelCase				)
											a__	:							List[str]      						= model(__UpperCAmelCase				)
											self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)				)
											self.parent.assertEqual(result.pooler_output.shape ,					(self.batch_size, self.hidden_size)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[str]      						= MPNetForQuestionAnswering(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							int      						= model(
											    __UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					start_positions=__UpperCAmelCase ,					end_positions=__UpperCAmelCase ,					)
											self.parent.assertEqual(result.start_logits.shape ,					(self.batch_size, self.seq_length)				)
											self.parent.assertEqual(result.end_logits.shape ,					(self.batch_size, self.seq_length)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.num_labels
											a__	:							Union[str, Any]      						= MPNetForSequenceClassification(__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Optional[Any]      						= model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.num_choices
											a__	:							int      						= MPNetForMultipleChoice(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							List[str]      						= input_ids.unsqueeze(1				).expand(-1 ,					self.num_choices ,					-1				).contiguous()
											a__	:							Optional[Any]      						= input_mask.unsqueeze(1				).expand(-1 ,					self.num_choices ,					-1				).contiguous()
											a__	:							Optional[Any]      						= model(
											    __UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					labels=__UpperCAmelCase ,					)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							int      						= self.num_labels
											a__	:							int      						= MPNetForTokenClassification(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Optional[Any]      						= model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.num_labels)				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.prepare_config_and_inputs()
											((a__)						,   (a__)						,   (a__)						,   (a__)						,   (a__)						,   (a__))	:							Tuple      						= config_and_inputs
											a__	:							List[str]      						= {"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict





@require_torch
class 				_a						(       SCREAMING_SNAKE_CASE       ,  SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Dict =       (
							    (
							        MPNetForMaskedLM,
							        MPNetForMultipleChoice,
							        MPNetForQuestionAnswering,
							        MPNetForSequenceClassification,
							        MPNetForTokenClassification,
							        MPNetModel,
							    )
							    if is_torch_available()
							    else ()
							)
							A				:Optional[Any] =       (
							    {
							        "feature-extraction": MPNetModel,
							        "fill-mask": MPNetForMaskedLM,
							        "question-answering": MPNetForQuestionAnswering,
							        "text-classification": MPNetForSequenceClassification,
							        "token-classification": MPNetForTokenClassification,
							        "zero-shot": MPNetForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							A				:List[str] =       False
							A				:List[str] =       True



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= MPNetModelTester(self				)
											a__	:							int      						= ConfigTester(self ,					config_class=__UpperCAmelCase ,					hidden_size=37				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.config_tester.run_common_tests()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_model(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_sequence_classification(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_multiple_choice(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_token_classification(*__UpperCAmelCase				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_question_answering(*__UpperCAmelCase				)





@require_torch
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= MPNetModel.from_pretrained("microsoft/mpnet-base"				)
											a__	:							Union[str, Any]      						= torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]				)
											a__	:							List[Any]      						= model(__UpperCAmelCase				)[0]
											a__	:							Union[str, Any]      						= torch.Size((1, 11, 768)				)
											self.assertEqual(output.shape ,					__UpperCAmelCase				)
											a__	:							Tuple      						= torch.tensor(
											    [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]				)
											# compare the actual values for a slice.
											self.assertTrue(torch.allclose(output[:, :3, :3] ,					__UpperCAmelCase ,					atol=1E-4				)				)







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()


