
'''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


UpperCAmelCase__ :			Optional[Any]											=		logging.getLogger(__name__)







def 	A  (   UpperCamelCase_       :  Optional[Any]    ,						UpperCamelCase_       :  int   )       ->							int:







			'''simple docstring'''



			return (preds == labels).mean()






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






@dataclass
class  A  :
	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=128	,   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=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Overwrite the cached training and evaluation sets'}     )







def 	A  (   )       ->							Any:







			'''simple docstring'''



			lowerCAmelCase__										=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
			lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				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"    ,						UpperCamelCase_   )

			# Set seed
			set_seed(training_args.seed   )

			try:
						lowerCAmelCase__										=				processors[data_args.task_name]()
						lowerCAmelCase__										=				processor.get_labels()
						lowerCAmelCase__										=				len(UpperCamelCase_   )
			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__										=				AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,						num_labels=UpperCamelCase_    ,						finetuning_task=data_args.task_name    ,						cache_dir=model_args.cache_dir    ,						)
			lowerCAmelCase__										=				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__										=				AutoModelForMultipleChoice.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    ,						)

			# Get datasets
			lowerCAmelCase__										=				(
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir    ,						tokenizer=UpperCamelCase_    ,						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__										=				(
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir    ,						tokenizer=UpperCamelCase_    ,						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_       :  EvalPrediction   ) -> Dict:
						lowerCAmelCase__										=				np.argmax(p.predictions    ,						axis=1   )
						return {"acc": simple_accuracy(UpperCamelCase_    ,						p.label_ids   )}

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

			# Initialize our Trainer
			lowerCAmelCase__										=				Trainer(
			    model=UpperCamelCase_    ,						args=UpperCamelCase_    ,						train_dataset=UpperCamelCase_    ,						eval_dataset=UpperCamelCase_    ,						compute_metrics=UpperCamelCase_    ,						data_collator=UpperCamelCase_    ,						)

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

						lowerCAmelCase__										=				trainer.evaluate()

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

												results.update(UpperCamelCase_   )

			return results







def 	A  (   UpperCamelCase_       :  int   )       ->							str:







			'''simple docstring'''



			main()


if __name__ == "__main__":
		main()












"""simple docstring"""





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Optional[int]									=		["""pixel_values"""]




				def __init__(  self	:Optional[Any]    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Dict[str, int]] = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:bool = True    ,     __lowercase	:Union[int, float] = 1 / 255    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     **__lowercase	:Tuple    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   int       =size if size is not None else {'''shortest_edge''': 256}
											__lowerCamelCase   :   int       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											__lowerCamelCase   :   Optional[Any]       =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     param_name='''crop_size'''      )
											__lowerCamelCase   :   List[str]       =do_resize
											__lowerCamelCase   :   Optional[Any]       =size
											__lowerCamelCase   :   Tuple       =resample
											__lowerCamelCase   :   int       =do_center_crop
											__lowerCamelCase   :   Optional[Any]       =crop_size
											__lowerCamelCase   :   int       =do_rescale
											__lowerCamelCase   :   str       =rescale_factor
											__lowerCamelCase   :   int       =do_normalize
											__lowerCamelCase   :   Union[str, Any]       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase   :   Tuple       =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:PILImageResampling = PILImageResampling.BICUBIC    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:str    ,     ):
											__lowerCamelCase   :   Tuple       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
											__lowerCamelCase   :   List[Any]       =get_resize_output_image_size(__lowercase    ,     size=size['''shortest_edge''']    ,     default_to_square=__lowercase      )
											return resize(__lowercase    ,     size=__lowercase    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:int    ,     ):
											__lowerCamelCase   :   List[Any]       =get_size_dict(__lowercase      )
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'      )
											return center_crop(__lowercase    ,     size=(size['''height'''], size['''width'''])    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:str    ,     __lowercase	:np.ndarray    ,     __lowercase	:float    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:str      ):
											return rescale(__lowercase    ,     scale=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:Union[str, Any]    ,     ):
											return normalize(__lowercase    ,     mean=__lowercase    ,     std=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:ImageInput    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Optional[float] = None    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:Union[str, ChannelDimension] = ChannelDimension.FIRST    ,     **__lowercase	:Optional[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   Optional[Any]       =size if size is not None else self.size
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											__lowerCamelCase   :   List[str]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   Optional[int]       =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase   :   Any       =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase   :   Union[str, Any]       =get_size_dict(__lowercase    ,     param_name='''crop_size'''      )
											__lowerCamelCase   :   Any       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   List[str]       =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase   :   List[Any]       =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase   :   List[Any]       =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase   :   int       =image_std if image_std is not None else self.image_std

											__lowerCamelCase   :   int       =make_list_of_images(__lowercase      )

											if not valid_images(__lowercase      ):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

											if do_resize and size is None:
																		raise ValueError('''Size must be specified if do_resize is True.'''      )

											if do_center_crop and crop_size is None:
																		raise ValueError('''Crop size must be specified if do_center_crop is True.'''      )

											if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

											# All transformations expect numpy arrays.
											__lowerCamelCase   :   int       =[to_numpy_array(__lowercase      ) for image in images]

											if do_resize:
																		__lowerCamelCase   :   Tuple       =[self.resize(image=__lowercase    ,     size=__lowercase    ,     resample=__lowercase      ) for image in images]

											if do_center_crop:
																		__lowerCamelCase   :   Any       =[self.center_crop(image=__lowercase    ,     size=__lowercase      ) for image in images]

											if do_rescale:
																		__lowerCamelCase   :   Optional[Any]       =[self.rescale(image=__lowercase    ,     scale=__lowercase      ) for image in images]

											if do_normalize:
																		__lowerCamelCase   :   Tuple       =[self.normalize(image=__lowercase    ,     mean=__lowercase    ,     std=__lowercase      ) for image in images]

											__lowerCamelCase   :   List[str]       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

											__lowerCamelCase   :   Union[str, Any]       ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase    ,     tensor_type=__lowercase      )




				def        __lowercase	(  self	:Dict    ,     __lowercase	:Dict    ,     __lowercase	:List[Tuple] = None      ):
											__lowerCamelCase   :   Dict       =outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
																		if len(__lowercase      ) != len(__lowercase      ):
																									raise ValueError(
																									    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''      )

																		if is_torch_tensor(__lowercase      ):
																									__lowerCamelCase   :   Optional[int]       =target_sizes.numpy()

																		__lowerCamelCase   :   List[Any]       =[]

																		for idx in range(len(__lowercase      )      ):
																									__lowerCamelCase   :   Optional[int]       =torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0      )    ,     size=target_sizes[idx]    ,     mode='''bilinear'''    ,     align_corners=__lowercase      )
																									__lowerCamelCase   :   Union[str, Any]       =resized_logits[0].argmax(dim=0      )
																									semantic_segmentation.append(__lowercase      )
											else:
																		__lowerCamelCase   :   Union[str, Any]       =logits.argmax(dim=1      )
																		__lowerCamelCase   :   Optional[int]       =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

											return semantic_segmentation




