

'''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


lowercase__         =      logging.getLogger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
	return (preds == labels).mean()




@dataclass
class 	A_			:






					'''simple docstring'''



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




@dataclass
class 	A_			:






					'''simple docstring'''



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




def 				UpperCamelCase(					):
	# 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.

	UpperCAmelCase	:     Dict						 =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
	UpperCAmelCase	:     int						 =			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:
		UpperCAmelCase	:     List[str]						 =			processors[data_args.task_name]()
		UpperCAmelCase	:     int						 =			processor.get_labels()
		UpperCAmelCase	:     Union[str, Any]						 =			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.

	UpperCAmelCase	:     Union[str, Any]						 =			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  ,	)
	UpperCAmelCase	:     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  ,	)
	UpperCAmelCase	:     List[Any]						 =			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
	UpperCAmelCase	:     Optional[Any]						 =			(
	    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
	)
	UpperCAmelCase	:     List[str]						 =			(
	    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_     ) -> Dict:
		UpperCAmelCase	:     List[str]						 =			np.argmax(p.predictions  ,	axis=1     )
		return {"acc": simple_accuracy(UpperCAmelCase_  ,	p.label_ids     )}

	# Data collator
	UpperCAmelCase	:     Optional[int]						 =			DataCollatorWithPadding(UpperCAmelCase_  ,	pad_to_multiple_of=8     ) if training_args.fpaa else None

	# Initialize our Trainer
	UpperCAmelCase	:     List[str]						 =			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
	UpperCAmelCase	:     Union[str, Any]						 =			{}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'     )

		UpperCAmelCase	:     Any						 =			trainer.evaluate()

		UpperCAmelCase	:     Optional[Any]						 =			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 				UpperCamelCase(					UpperCAmelCase_     ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()







'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
      return resize(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

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

      if do_color_quantize and clusters is None:
       raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     Dict						 =			[to_numpy_array(lowercase_	) for image in images]

      if do_resize:
       UpperCAmelCase	:     List[Any]						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

      if do_color_quantize:
       UpperCAmelCase	:     List[str]						 =			[to_channel_dimension_format(lowercase_					,      ChannelDimension.LAST	) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       UpperCAmelCase	:     int						 =			np.array(lowercase_	)
       UpperCAmelCase	:     str						 =			color_quantize(lowercase_					,      lowercase_	).reshape(images.shape[:-1]	)

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       UpperCAmelCase	:     int						 =			list(lowercase_	)
      else:
       UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




