




"""simple docstring"""





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


A   =       logging.getLogger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Union[str, Any]:


	"""simple docstring"""
	return (preds == labels).mean()



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






@dataclass
class      a__				:
			lowercase_    =	field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()   )}   )
			lowercase_    =	field(metadata={"help": "Should contain the data files for the task."}   )
			lowercase_    =	field(
			    default=1_2_8							,    metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }							,    )
			lowercase_    =	field(
			    default=__magic_name__							,    metadata={"help": "Overwrite the cached training and evaluation sets"}   )






def 					_UpperCamelCase    (			)			->			int:


	"""simple docstring"""
	__UpperCAmelCase	: Any		=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
	__UpperCAmelCase	: List[Any]		=					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"				,       lowerCamelCase__							)

	# Set seed
	set_seed(training_args.seed							)

	try:
		__UpperCAmelCase	: Tuple		=					processors[data_args.task_name]()
		__UpperCAmelCase	: str		=					processor.get_labels()
		__UpperCAmelCase	: str		=					len(lowerCamelCase__							)
	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	: Tuple		=					AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,       num_labels=lowerCamelCase__				,       finetuning_task=data_args.task_name				,       cache_dir=model_args.cache_dir				,       )
	__UpperCAmelCase	: Tuple		=					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	: List[Any]		=					AutoModelForMultipleChoice.from_pretrained(
	    model_args.model_name_or_path				,       from_tf=bool(".ckpt" in model_args.model_name_or_path							)				,       config=lowerCamelCase__				,       cache_dir=model_args.cache_dir				,       )

	# Get datasets
	__UpperCAmelCase	: int		=					(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir				,       tokenizer=lowerCamelCase__				,       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	: List[str]		=					(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir				,       tokenizer=lowerCamelCase__				,       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(UpperCamelCase							) -> Dict:
		__UpperCAmelCase	: int		=					np.argmax(p.predictions				,       axis=1							)
		return {"acc": simple_accuracy(lowerCamelCase__				,       p.label_ids							)}

	# Data collator
	__UpperCAmelCase	: Optional[Any]		=					DataCollatorWithPadding(lowerCamelCase__				,       pad_to_multiple_of=8							) if training_args.fpaa else None

	# Initialize our Trainer
	__UpperCAmelCase	: Dict		=					Trainer(
	    model=lowerCamelCase__				,       args=lowerCamelCase__				,       train_dataset=lowerCamelCase__				,       eval_dataset=lowerCamelCase__				,       compute_metrics=lowerCamelCase__				,       data_collator=lowerCamelCase__				,       )

	# 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	: Optional[int]		=					{}
	if training_args.do_eval:
		logger.info("*** Evaluate ***"							)

		__UpperCAmelCase	: int		=					trainer.evaluate()

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

				results.update(lowerCamelCase__							)

	return results

def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


	"""simple docstring"""
	main()


if __name__ == "__main__":
					main()








"""simple docstring"""





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A   =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A   =       """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""



class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir      ,  "models/bert/"))
						__UpperCAmelCase	: Tuple		=					self.transformer_dir
						shutil.copy(
						    os.path.join(UpperCamelCase_      ,  "src/transformers/models/bert/modeling_bert.py")      ,  os.path.join(self.transformer_dir      ,  "models/bert/modeling_bert.py")      ,  )


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					"src/transformers"
						shutil.rmtree(self.transformer_dir)


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    List[str]=None):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					comment + F"\nclass {class_name}(nn.Module):\n" + class_code
						if overwrite_result is not None:
									__UpperCAmelCase	: str		=					comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
						__UpperCAmelCase	: Optional[int]		=					black.Mode(target_versions={black.TargetVersion.PYaa}      ,  line_length=119)
						__UpperCAmelCase	: Dict		=					black.format_str(UpperCamelCase_      ,  mode=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					os.path.join(self.transformer_dir      ,  "new_code.py")
						with open(UpperCamelCase_      ,  "w"      ,  newline="\n") as f:
									f.write(UpperCamelCase_)
						if overwrite_result is None:
									self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase_)) == 0)
						else:
									check_copies.is_copy_consistent(f.name      ,  overwrite=UpperCamelCase_)
									with open(UpperCamelCase_      ,  "r") as f:
												self.assertTrue(f.read()      ,  UpperCamelCase_)


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead")
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						self.check_copy_consistency(
						    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"      ,  "BertLMPredictionHead"      ,  REFERENCE_CODE + "\n"      ,  )

						# With no empty line at the end
						self.check_copy_consistency(
						    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"      ,  "BertLMPredictionHead"      ,  UpperCamelCase_      ,  )

						# Copy consistency with rename
						self.check_copy_consistency(
						    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"      ,  "TestModelLMPredictionHead"      ,  re.sub("Bert"      ,  "TestModel"      ,  UpperCamelCase_)      ,  )

						# Copy consistency with a really long name
						__UpperCAmelCase	: Optional[Any]		=					"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
						self.check_copy_consistency(
						    F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"      ,  F"{long_class_name}LMPredictionHead"      ,  re.sub("Bert"      ,  UpperCamelCase_      ,  UpperCamelCase_)      ,  )

						# Copy consistency with overwrite
						self.check_copy_consistency(
						    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"      ,  "TestModelLMPredictionHead"      ,  UpperCamelCase_      ,  overwrite_result=re.sub("Bert"      ,  "TestModel"      ,  UpperCamelCase_)      ,  )


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					check_copies.LOCALIZED_READMES["README_zh-hans.md"]

						__UpperCAmelCase	: str		=					(
						    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
						    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
						    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
						    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
						    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
						    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
						    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
						    " method has been applied to compress GPT2 into"
						    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
						    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
						    " Multilingual BERT into"
						    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
						    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
						    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
						    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
						    " Luong, Quoc V. Le, Christopher D. Manning."
						)
						__UpperCAmelCase	: List[str]		=					(
						    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
						    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
						    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
						    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
						)
						__UpperCAmelCase	: str		=					(
						    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
						    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
						    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
						    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
						    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
						    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
						    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
						    " method has been applied to compress GPT2 into"
						    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
						    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
						    " Multilingual BERT into"
						    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
						    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
						    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
						    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
						    " Christopher D. Manning 发布。\n"
						)

						__UpperCAmelCase	,     __UpperCAmelCase	: Any		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

						self.assertFalse(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)

						__UpperCAmelCase	,     __UpperCAmelCase	: int		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

						# Check whether the number of models is equal to README.md after conversion.
						self.assertTrue(UpperCamelCase_)

						__UpperCAmelCase	: str		=					(
						    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
						    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
						    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
						    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
						)
						__UpperCAmelCase	: Optional[int]		=					(
						    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
						    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
						    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
						    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
						)
						__UpperCAmelCase	: int		=					(
						    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
						    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
						    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
						    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
						)

						__UpperCAmelCase	,     __UpperCAmelCase	: List[str]		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

						# Check if the model link is synchronized.
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)


