





import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
							from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder





def       _UpperCamelCase       (a__				:Optional[int]				,     a__				:List[str]				,     a__				:Dict				,     a__				:str=None				,     a__				:Any=None				,     a__				:List[Any]=None				,     a__				:List[Any]=None				,     a__				:List[Any]=None				,     ):






							"""simple docstring"""





							if attention_mask is None:
														UpperCamelCase__			       =     input_ids.ne(config.pad_token_id    )
							if decoder_attention_mask is None:
														UpperCamelCase__			       =     decoder_input_ids.ne(config.pad_token_id    )
							if head_mask is None:
														UpperCamelCase__			       =     torch.ones(config.encoder_layers				,     config.encoder_attention_heads				,     device=a__    )
							if decoder_head_mask is None:
														UpperCamelCase__			       =     torch.ones(config.decoder_layers				,     config.decoder_attention_heads				,     device=a__    )
							if cross_attn_head_mask is None:
														UpperCamelCase__			       =     torch.ones(config.decoder_layers				,     config.decoder_attention_heads				,     device=a__    )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

class     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=13					,	__lowerCAmelCase=7					,	__lowerCAmelCase=True					,	__lowerCAmelCase=False					,	__lowerCAmelCase=99					,	__lowerCAmelCase=16					,	__lowerCAmelCase=2					,	__lowerCAmelCase=4					,	__lowerCAmelCase=4					,	__lowerCAmelCase="relu"					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=20					,	__lowerCAmelCase=2					,	__lowerCAmelCase=1					,	__lowerCAmelCase=0					,	):
														UpperCamelCase__			       =     parent
														UpperCamelCase__			       =     batch_size
														UpperCamelCase__			       =     seq_length
														UpperCamelCase__			       =     is_training
														UpperCamelCase__			       =     use_labels
														UpperCamelCase__			       =     vocab_size
														UpperCamelCase__			       =     hidden_size
														UpperCamelCase__			       =     num_hidden_layers
														UpperCamelCase__			       =     num_attention_heads
														UpperCamelCase__			       =     intermediate_size
														UpperCamelCase__			       =     hidden_act
														UpperCamelCase__			       =     hidden_dropout_prob
														UpperCamelCase__			       =     attention_probs_dropout_prob
														UpperCamelCase__			       =     encoder_layerdrop
														UpperCamelCase__			       =     decoder_layerdrop
														UpperCamelCase__			       =     max_position_embeddings
														UpperCamelCase__			       =     eos_token_id
														UpperCamelCase__			       =     pad_token_id
														UpperCamelCase__			       =     bos_token_id






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size					)
														UpperCamelCase__			       =     self.eos_token_id  # Eos Token
														UpperCamelCase__			       =     ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size					)

														# we need to clamp the input ids here to avoid having pad token in between
														# this is because for M2M100 the position_ids are prepared such that
														# all pad tokens have pos id = 2 and rest are between 2..seq_length
														# and the seq_length here is seq_length - num_pad_tokens
														# but when using past, there is no way of knowing if the past input ids had
														# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
														# position_ids being off by num_pad_tokens in past input
														UpperCamelCase__			       =     input_ids.clamp(self.pad_token_id + 1					)
														UpperCamelCase__			       =     decoder_input_ids.clamp(self.pad_token_id + 1					)

														UpperCamelCase__			       =     self.get_config()
														UpperCamelCase__			       =     prepare_mam_aaa_inputs_dict(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)
														return config, inputs_dict






							def 			_lowerCamelCase						(  self					):
														return MaMaaaConfig(
														    vocab_size=self.vocab_size					,	d_model=self.hidden_size					,	encoder_layers=self.num_hidden_layers					,	decoder_layers=self.num_hidden_layers					,	encoder_attention_heads=self.num_attention_heads					,	decoder_attention_heads=self.num_attention_heads					,	encoder_ffn_dim=self.intermediate_size					,	decoder_ffn_dim=self.intermediate_size					,	dropout=self.hidden_dropout_prob					,	attention_dropout=self.attention_probs_dropout_prob					,	encoder_layerdrop=self.encoder_layerdrop					,	decoder_layerdrop=self.decoder_layerdrop					,	max_position_embeddings=self.max_position_embeddings					,	eos_token_id=self.eos_token_id					,	bos_token_id=self.bos_token_id					,	pad_token_id=self.pad_token_id					,	)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__			       =     self.prepare_config_and_inputs()
														return config, inputs_dict






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     MaMaaaModel(config=__lowerCAmelCase					).get_decoder().to(__lowerCAmelCase					).eval()
														UpperCamelCase__			       =     inputs_dict["""input_ids"""]
														UpperCamelCase__			       =     inputs_dict["""attention_mask"""]
														UpperCamelCase__			       =     inputs_dict["""head_mask"""]

														# first forward pass
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	head_mask=__lowerCAmelCase					,	use_cache=__lowerCAmelCase					)

														UpperCamelCase__       ,       UpperCamelCase__			       =     outputs.to_tuple()

														# create hypothetical multiple next token and extent to next_input_ids
														UpperCamelCase__			       =     ids_tensor((self.batch_size, 3)					,	config.vocab_size					)
														UpperCamelCase__			       =     ids_tensor((self.batch_size, 3)					,	2					)

														# append to next input_ids and
														UpperCamelCase__			       =     torch.cat([input_ids, next_tokens]					,	dim=-1					)
														UpperCamelCase__			       =     torch.cat([attention_mask, next_attn_mask]					,	dim=-1					)

														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					)["""last_hidden_state"""]
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	past_key_values=__lowerCAmelCase					)[
														    """last_hidden_state"""
														]

														# select random slice
														UpperCamelCase__			       =     ids_tensor((1,)					,	output_from_past.shape[-1]					).item()
														UpperCamelCase__			       =     output_from_no_past[:, -3:, random_slice_idx].detach()
														UpperCamelCase__			       =     output_from_past[:, :, random_slice_idx].detach()

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1E-2					)					)







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     MaMaaaModel(config=__lowerCAmelCase					).to(__lowerCAmelCase					).eval()
														UpperCamelCase__			       =     model(**__lowerCAmelCase					)

														UpperCamelCase__			       =     outputs.encoder_last_hidden_state
														UpperCamelCase__			       =     outputs.last_hidden_state

														with tempfile.TemporaryDirectory() as tmpdirname:
																					UpperCamelCase__			       =     model.get_encoder()
																					encoder.save_pretrained(__lowerCAmelCase					)
																					UpperCamelCase__			       =     MaMaaaEncoder.from_pretrained(__lowerCAmelCase					).to(__lowerCAmelCase					)

														UpperCamelCase__			       =     encoder(inputs_dict["""input_ids"""]					,	attention_mask=inputs_dict["""attention_mask"""]					)[
														    0
														]

														self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3					)

														with tempfile.TemporaryDirectory() as tmpdirname:
																					UpperCamelCase__			       =     model.get_decoder()
																					decoder.save_pretrained(__lowerCAmelCase					)
																					UpperCamelCase__			       =     MaMaaaDecoder.from_pretrained(__lowerCAmelCase					).to(__lowerCAmelCase					)

														UpperCamelCase__			       =     decoder(
														    input_ids=inputs_dict["""decoder_input_ids"""]					,	attention_mask=inputs_dict["""decoder_attention_mask"""]					,	encoder_hidden_states=__lowerCAmelCase					,	encoder_attention_mask=inputs_dict["""attention_mask"""]					,	)[0]

														self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3					)






@require_torch
class     __SCREAMING_SNAKE_CASE   (       _a				,						_a				,						_a				,						unittest.TestCase    ):
							snake_case       : int         =    (
							    (
							        MaMaaaModel,
							        MaMaaaForConditionalGeneration,
							    )
							    if is_torch_available()
							    else ()
							)
							snake_case       : Any         =    (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
							snake_case       : str         =    (
							    {
							        """conversational""": MaMaaaForConditionalGeneration,
							        """feature-extraction""": MaMaaaModel,
							        """summarization""": MaMaaaForConditionalGeneration,
							        """text2text-generation""": MaMaaaForConditionalGeneration,
							        """translation""": MaMaaaForConditionalGeneration,
							    }
							    if is_torch_available()
							    else {}
							)
							snake_case       : Dict         =    True
							snake_case       : Any         =    True
							snake_case       : Dict         =    False
							snake_case       : List[Any]         =    False






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														if pipeline_test_casse_name == "TranslationPipelineTests":
																					# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
																					# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
																					return True

														return False






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     MaMaaaModelTester(self					)
														UpperCamelCase__			       =     ConfigTester(self					,	config_class=__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														self.config_tester.run_common_tests()






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														for model_class in self.all_model_classes:
																					UpperCamelCase__			       =     model_class(__lowerCAmelCase					)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(__lowerCAmelCase					)
																												UpperCamelCase__       ,       UpperCamelCase__			       =     model_class.from_pretrained(__lowerCAmelCase					,	output_loading_info=__lowerCAmelCase					)
																					self.assertEqual(info["""missing_keys"""]					,	[]					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_encoder_decoder_model_standalone(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
																					UpperCamelCase__			       =     model_class(__lowerCAmelCase					)
																					model.to(__lowerCAmelCase					)
																					model.eval()

																					UpperCamelCase__			       =     copy.deepcopy(self._prepare_for_class(__lowerCAmelCase					,	__lowerCAmelCase					)					)

																					if not self.is_encoder_decoder:
																												UpperCamelCase__			       =     inputs["""input_ids"""]
																												del inputs["input_ids"]
																					else:
																												UpperCamelCase__			       =     inputs["""input_ids"""]
																												UpperCamelCase__			       =     inputs.get("""decoder_input_ids"""					,	__lowerCAmelCase					)
																												del inputs["input_ids"]
																												inputs.pop("""decoder_input_ids"""					,	__lowerCAmelCase					)

																					UpperCamelCase__			       =     model.get_input_embeddings()
																					if not self.is_encoder_decoder:
																												UpperCamelCase__			       =     wte(__lowerCAmelCase					)
																					else:
																												UpperCamelCase__			       =     wte(__lowerCAmelCase					)
																												UpperCamelCase__			       =     wte(__lowerCAmelCase					)

																					with torch.no_grad():
																												model(**__lowerCAmelCase					)[0]







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														UpperCamelCase__			       =     input_dict["""input_ids"""]
														UpperCamelCase__			       =     input_ids.ne(1					).to(__lowerCAmelCase					)
														UpperCamelCase__			       =     MaMaaaForConditionalGeneration(__lowerCAmelCase					).eval().to(__lowerCAmelCase					)
														if torch_device == "cuda":
																					model.half()
														model.generate(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					)
														model.generate(num_beams=4					,	do_sample=__lowerCAmelCase					,	early_stopping=__lowerCAmelCase					,	num_return_sequences=3					)






def       _UpperCamelCase       (a__				:Optional[int]    ):






							"""simple docstring"""





							return torch.tensor(a__				,     dtype=torch.long				,     device=a__    )


UpperCamelCase__     =	1e-4

@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							@cached_property
							def 			_lowerCamelCase						(  self					):
														return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""					).to(__lowerCAmelCase					)
														UpperCamelCase__			       =     _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]					)
														UpperCamelCase__			       =     _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]					)
														UpperCamelCase__			       =     prepare_mam_aaa_inputs_dict(model.config					,	__lowerCAmelCase					,	__lowerCAmelCase					)
														with torch.no_grad():
																					UpperCamelCase__			       =     model(**__lowerCAmelCase					)[0]
														UpperCamelCase__			       =     torch.Size((1, 11, 1024)					)
														self.assertEqual(output.shape					,	__lowerCAmelCase					)
														# change to expected output here
														UpperCamelCase__			       =     torch.tensor(
														    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]					,	device=__lowerCAmelCase					)
														self.assertTrue(torch.allclose(output[:, :3, :3]					,	__lowerCAmelCase					,	atol=__lowerCAmelCase					)					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""					).to(__lowerCAmelCase					)

														# change to intended input
														UpperCamelCase__			       =     _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]					)
														UpperCamelCase__			       =     _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]					)
														UpperCamelCase__			       =     prepare_mam_aaa_inputs_dict(model.config					,	__lowerCAmelCase					,	__lowerCAmelCase					)
														with torch.no_grad():
																					UpperCamelCase__			       =     model(**__lowerCAmelCase					)[0]
														UpperCamelCase__			       =     torch.Size((1, 11, model.config.vocab_size)					)
														self.assertEqual(output.shape					,	__lowerCAmelCase					)
														# change to expected output here
														UpperCamelCase__			       =     torch.tensor(
														    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]					,	device=__lowerCAmelCase					)
														self.assertTrue(torch.allclose(output[:, :3, :3]					,	__lowerCAmelCase					,	atol=__lowerCAmelCase					)					)







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""					).to(__lowerCAmelCase					)
														UpperCamelCase__			       =     MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""					,	src_lang="""fr"""					,	tgt_lang="""en"""					)

														UpperCamelCase__			       =     [
														    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
														    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
														    """Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
														    """ Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
														    """ l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
														]

														# The below article tests that we don't add any hypotheses outside of the top n_beams
														UpperCamelCase__			       =     tokenizer(__lowerCAmelCase					,	padding=__lowerCAmelCase					,	return_tensors="""pt"""					)

														UpperCamelCase__			       =     model.generate(
														    input_ids=dct["""input_ids"""].to(__lowerCAmelCase					)					,	attention_mask=dct["""attention_mask"""].to(__lowerCAmelCase					)					,	num_beams=5					,	forced_bos_token_id=tokenizer.get_lang_id("""en"""					)					,	)

														UpperCamelCase__			       =     [
														    """The NSA case highlights the total absence of intelligence debate""",
														    """I think there are two levels of response from the French government.""",
														    """When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
														    """ Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
														    """ communications in France.""",
														]

														UpperCamelCase__			       =     tokenizer.batch_decode(
														    hypotheses_batch.tolist()					,	clean_up_tokenization_spaces=__lowerCAmelCase					,	skip_special_tokens=__lowerCAmelCase					)
														assert generated == expected_en











import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase__     =	logging.getLogger(__name__)





def       _UpperCamelCase       (a__				:Union[str, Any]				,     a__				:Optional[Any]    ):






							"""simple docstring"""





							return (preds == labels).mean()

@dataclass
class     __SCREAMING_SNAKE_CASE   :
							snake_case       : str         =    field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
							snake_case       : Optional[str]         =    field(
							    default=_a				,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
							snake_case       : Optional[str]         =    field(
							    default=_a				,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
							snake_case       : Optional[str]         =    field(
							    default=_a				,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				,						)






@dataclass
class     __SCREAMING_SNAKE_CASE   :
							snake_case       : str         =    field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()    )}    )
							snake_case       : str         =    field(metadata={"""help""": """Should contain the data files for the task."""}    )
							snake_case       : 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."""
							        )
							    }				,						)
							snake_case       : bool         =    field(
							    default=_a				,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )






def       _UpperCamelCase       ():






							"""simple docstring"""





							UpperCamelCase__			       =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
							UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir    )
							    and os.listdir(training_args.output_dir    )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    """ --overwrite_output_dir to overcome."""    )

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,     datefmt="""%m/%d/%Y %H:%M:%S"""				,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,     )
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""				,     training_args.local_rank				,     training_args.device				,     training_args.n_gpu				,     bool(training_args.local_rank != -1    )				,     training_args.fpaa				,     )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank    ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""				,     a__    )

							# Set seed
							set_seed(training_args.seed    )

							try:
														UpperCamelCase__			       =     processors[data_args.task_name]()
														UpperCamelCase__			       =     processor.get_labels()
														UpperCamelCase__			       =     len(a__    )
							except KeyError:
														raise ValueError("""Task not found: %s""" % (data_args.task_name)    )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							UpperCamelCase__			       =     AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,     num_labels=a__				,     finetuning_task=data_args.task_name				,     cache_dir=model_args.cache_dir				,     )
							UpperCamelCase__			       =     AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,     cache_dir=model_args.cache_dir				,     )
							UpperCamelCase__			       =     AutoModelForMultipleChoice.from_pretrained(
							    model_args.model_name_or_path				,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )				,     config=a__				,     cache_dir=model_args.cache_dir				,     )

							# Get datasets
							UpperCamelCase__			       =     (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir				,     tokenizer=a__				,     task=data_args.task_name				,     max_seq_length=data_args.max_seq_length				,     overwrite_cache=data_args.overwrite_cache				,     mode=Split.train				,     )
							    if training_args.do_train
							    else None
							)
							UpperCamelCase__			       =     (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir				,     tokenizer=a__				,     task=data_args.task_name				,     max_seq_length=data_args.max_seq_length				,     overwrite_cache=data_args.overwrite_cache				,     mode=Split.dev				,     )
							    if training_args.do_eval
							    else None
							)

							def compute_metrics(a__				:EvalPrediction    ) -> Dict:
														UpperCamelCase__			       =     np.argmax(p.predictions				,     axis=1    )
														return {"acc": simple_accuracy(a__				,     p.label_ids    )}

							# Data collator
							UpperCamelCase__			       =     DataCollatorWithPadding(a__				,     pad_to_multiple_of=8    ) if training_args.fpaa else None

							# Initialize our Trainer
							UpperCamelCase__			       =     Trainer(
							    model=a__				,     args=a__				,     train_dataset=a__				,     eval_dataset=a__				,     compute_metrics=a__				,     data_collator=a__				,     )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
							UpperCamelCase__			       =     {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""    )

														UpperCamelCase__			       =     trainer.evaluate()

														UpperCamelCase__			       =     os.path.join(training_args.output_dir				,     """eval_results.txt"""    )
														if trainer.is_world_master():
																					with open(a__				,     """w"""    ) as writer:
																												logger.info("""***** Eval results *****"""    )
																												for key, value in result.items():
																																			logger.info("""  %s = %s"""				,     a__				,     a__    )
																																			writer.write("""%s = %s\n""" % (key, value)    )

																												results.update(a__    )

							return results





def       _UpperCamelCase       (a__				:Union[str, Any]    ):






							"""simple docstring"""





							main()


if __name__ == "__main__":
					main()




