

"""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     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Tuple:
		return (preds == labels).mean()





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





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






def     a__    (    )	->	List[Any]:
		# 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.

		__lowerCAmelCase:       Any          =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
		__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       List[str]          =			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"					, __SCREAMING_SNAKE_CASE )

		# Set seed
		set_seed(training_args.seed )

		try:
				__lowerCAmelCase:       Optional[int]          =			processors[data_args.task_name]()
				__lowerCAmelCase:       Optional[int]          =			processor.get_labels()
				__lowerCAmelCase:       Union[str, Any]          =			len(__SCREAMING_SNAKE_CASE )
		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.

		__lowerCAmelCase:       Dict          =			AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path					, num_labels=__SCREAMING_SNAKE_CASE					, finetuning_task=data_args.task_name					, cache_dir=model_args.cache_dir					, )
		__lowerCAmelCase:       Optional[Any]          =			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					, )
		__lowerCAmelCase:       str          =			AutoModelForMultipleChoice.from_pretrained(
		    model_args.model_name_or_path					, from_tf=bool(".ckpt" in model_args.model_name_or_path )					, config=__SCREAMING_SNAKE_CASE					, cache_dir=model_args.cache_dir					, )

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

		# Data collator
		__lowerCAmelCase:       int          =			DataCollatorWithPadding(__SCREAMING_SNAKE_CASE					, pad_to_multiple_of=8 ) if training_args.fpaa else None

		# Initialize our Trainer
		__lowerCAmelCase:       Tuple          =			Trainer(
		    model=__SCREAMING_SNAKE_CASE					, args=__SCREAMING_SNAKE_CASE					, train_dataset=__SCREAMING_SNAKE_CASE					, eval_dataset=__SCREAMING_SNAKE_CASE					, compute_metrics=__SCREAMING_SNAKE_CASE					, data_collator=__SCREAMING_SNAKE_CASE					, )

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

				__lowerCAmelCase:       Union[str, Any]          =			trainer.evaluate()

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

								results.update(__SCREAMING_SNAKE_CASE )

		return results






def     a__    (    __SCREAMING_SNAKE_CASE )	->	str:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
			main()





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  """beit"""

				def __init__(	self							:				List[Any]     ,  UpperCamelCase__							:				List[str]=8_1_9_2     ,  UpperCamelCase__							:				Dict=7_6_8     ,  UpperCamelCase__							:				List[str]=1_2     ,  UpperCamelCase__							:				Union[str, Any]=1_2     ,  UpperCamelCase__							:				Dict=3_0_7_2     ,  UpperCamelCase__							:				Optional[int]="gelu"     ,  UpperCamelCase__							:				Tuple=0.0     ,  UpperCamelCase__							:				Tuple=0.0     ,  UpperCamelCase__							:				Union[str, Any]=0.02     ,  UpperCamelCase__							:				Optional[Any]=1e-12     ,  UpperCamelCase__							:				str=2_2_4     ,  UpperCamelCase__							:				str=1_6     ,  UpperCamelCase__							:				Union[str, Any]=3     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				int=False     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Dict=0.1     ,  UpperCamelCase__							:				List[Any]=0.1     ,  UpperCamelCase__							:				Any=True     ,  UpperCamelCase__							:				Optional[Any]=[3, 5, 7, 1_1]     ,  UpperCamelCase__							:				Optional[Any]=[1, 2, 3, 6]     ,  UpperCamelCase__							:				Optional[Any]=True     ,  UpperCamelCase__							:				Tuple=0.4     ,  UpperCamelCase__							:				Optional[Any]=2_5_6     ,  UpperCamelCase__							:				Optional[int]=1     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				Optional[Any]=2_5_5     ,  **UpperCamelCase__							:				Optional[int]     ,  )->   int:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:       str          =			vocab_size
						__lowerCAmelCase:       List[Any]          =			hidden_size
						__lowerCAmelCase:       str          =			num_hidden_layers
						__lowerCAmelCase:       Tuple          =			num_attention_heads
						__lowerCAmelCase:       Union[str, Any]          =			intermediate_size
						__lowerCAmelCase:       List[Any]          =			hidden_act
						__lowerCAmelCase:       Optional[Any]          =			hidden_dropout_prob
						__lowerCAmelCase:       List[Any]          =			attention_probs_dropout_prob
						__lowerCAmelCase:       str          =			initializer_range
						__lowerCAmelCase:       Optional[Any]          =			layer_norm_eps

						__lowerCAmelCase:       Any          =			image_size
						__lowerCAmelCase:       Any          =			patch_size
						__lowerCAmelCase:       Union[str, Any]          =			num_channels
						__lowerCAmelCase:       Tuple          =			use_mask_token
						__lowerCAmelCase:       Optional[Any]          =			use_absolute_position_embeddings
						__lowerCAmelCase:       List[Any]          =			use_relative_position_bias
						__lowerCAmelCase:       Optional[Any]          =			use_shared_relative_position_bias
						__lowerCAmelCase:       List[str]          =			layer_scale_init_value
						__lowerCAmelCase:       str          =			drop_path_rate
						__lowerCAmelCase:       str          =			use_mean_pooling
						# decode head attributes (semantic segmentation)
						__lowerCAmelCase:       Optional[Any]          =			out_indices
						__lowerCAmelCase:       Union[str, Any]          =			pool_scales
						# auxiliary head attributes (semantic segmentation)
						__lowerCAmelCase:       List[str]          =			use_auxiliary_head
						__lowerCAmelCase:       Union[str, Any]          =			auxiliary_loss_weight
						__lowerCAmelCase:       Optional[int]          =			auxiliary_channels
						__lowerCAmelCase:       Dict          =			auxiliary_num_convs
						__lowerCAmelCase:       List[Any]          =			auxiliary_concat_input
						__lowerCAmelCase:       str          =			semantic_loss_ignore_index





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  version.parse("""1.11"""  )

				@property
				def 							lowercase_     (	self							:				str)->   Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						    ])




				@property
				def 							lowercase_     (	self							:				Any)->   float:



						'''simple docstring'''


						return 1e-4


