






"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , ):
	A_		:    List[Any]						     = {}

	if train_file is not None:
		A_		:    Dict						     = [train_file]
	if eval_file is not None:
		A_		:    Dict						     = [eval_file]
	if test_file is not None:
		A_		:    int						     = [test_file]

	A_		:    Tuple						     = datasets.load_dataset("""csv""" , data_files=snake_case__       )
	A_		:    List[str]						     = list(ds[list(files.keys()       )[0]].features.keys()       )
	A_		:    str						     = features_name.pop(snake_case__       )
	A_		:    Dict						     = list(set(ds[list(files.keys()       )[0]][label_name]       )       )
	A_		:    Any						     = {label: i for i, label in enumerate(snake_case__       )}
	A_		:    Union[str, Any]						     = tokenizer.model_input_names
	A_		:    Dict						     = {}

	if len(snake_case__       ) == 1:
		for k in files.keys():
			A_		:    Optional[int]						     = ds[k].map(
			    lambda snake_case__       : tokenizer.batch_encode_plus(
			        example[features_name[0]] , truncation=snake_case__ , max_length=snake_case__ , padding="""max_length"""       ) , batched=snake_case__ , )
	elif len(snake_case__       ) == 2:
		for k in files.keys():
			A_		:    Optional[int]						     = ds[k].map(
			    lambda snake_case__       : tokenizer.batch_encode_plus(
			        (example[features_name[0]], example[features_name[1]]) , truncation=snake_case__ , max_length=snake_case__ , padding="""max_length""" , ) , batched=snake_case__ , )

	def gen_train():
		for ex in transformed_ds[datasets.Split.TRAIN]:
			A_		:    int						     = {k: v for k, v in ex.items() if k in input_names}
			A_		:    Dict						     = labelaid[ex[label_name]]
			yield (d, label)

	def gen_val():
		for ex in transformed_ds[datasets.Split.VALIDATION]:
			A_		:    Optional[Any]						     = {k: v for k, v in ex.items() if k in input_names}
			A_		:    Tuple						     = labelaid[ex[label_name]]
			yield (d, label)

	def gen_test():
		for ex in transformed_ds[datasets.Split.TEST]:
			A_		:    Optional[Any]						     = {k: v for k, v in ex.items() if k in input_names}
			A_		:    Optional[int]						     = labelaid[ex[label_name]]
			yield (d, label)

	A_		:    List[Any]						     = (
	    tf.data.Dataset.from_generator(
	        snake_case__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       )) , )
	    if datasets.Split.TRAIN in transformed_ds
	    else None
	)

	if train_ds is not None:
		A_		:    Union[str, Any]						     = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

	A_		:    Tuple						     = (
	    tf.data.Dataset.from_generator(
	        snake_case__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       )) , )
	    if datasets.Split.VALIDATION in transformed_ds
	    else None
	)

	if val_ds is not None:
		A_		:    Optional[int]						     = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

	A_		:    str						     = (
	    tf.data.Dataset.from_generator(
	        snake_case__ , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       )) , )
	    if datasets.Split.TEST in transformed_ds
	    else None
	)

	if test_ds is not None:
		A_		:    Tuple						     = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

	return train_ds, val_ds, test_ds, labelaid


_lowerCAmelCase       =				logging.getLogger(__name__)



@dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	_A							:					int 			=					field(metadata={"""help""": """Which column contains the label"""}    )
	_A							:					str 			=					field(default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """The path of the training file"""}    )
	_A							:					Optional[str] 			=					field(default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """The path of the development file"""}    )
	_A							:					Optional[str] 			=					field(default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """The path of the test file"""}    )
	_A							:					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."""
	        )
	    }							,							)
	_A							:					bool 			=					field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )






@dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	_A							:					str 			=					field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
	_A							:					Optional[str] 			=					field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
	_A							:					Optional[str] 			=					field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
	_A							:					bool 			=					field(default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Set this flag to use fast tokenization."""}    )
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	_A							:					Optional[str] 			=					field(
	    default=_SCREAMING_SNAKE_CASE							,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,							)






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.
	A_		:    str						     = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
	A_     ,  A_     ,  A_		:    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 , )
	logger.info(
	    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1       )}, """
	    F"""16-bits training: {training_args.fpaa}"""       )
	logger.info(F"""Training/evaluation parameters {training_args}"""       )

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

	A_		:    str						     = 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 , )

	A_     ,  A_     ,  A_     ,  A_		:    Dict						     = get_tfds(
	    train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=snake_case__ , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )

	A_		:    Union[str, Any]						     = AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(snake_case__       ) , labelaid=snake_case__ , idalabel={id: label for label, id in labelaid.items()} , finetuning_task="""text-classification""" , cache_dir=model_args.cache_dir , )

	with training_args.strategy.scope():
		A_		:    Any						     = TFAutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path , from_pt=bool(""".bin""" in model_args.model_name_or_path       ) , config=snake_case__ , cache_dir=model_args.cache_dir , )

	def compute_metrics(snake_case__       ) -> Dict:
		A_		:    Any						     = np.argmax(p.predictions , axis=1       )

		return {"acc": (preds == p.label_ids).mean()}

	# Initialize our Trainer
	A_		:    Any						     = TFTrainer(
	    model=snake_case__ , args=snake_case__ , train_dataset=snake_case__ , eval_dataset=snake_case__ , compute_metrics=snake_case__ , )

	# Training
	if training_args.do_train:
		trainer.train()
		trainer.save_model()
		tokenizer.save_pretrained(training_args.output_dir       )

	# Evaluation
	A_		:    str						     = {}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""       )
		A_		:    Any						     = trainer.evaluate()
		A_		:    Optional[Any]						     = os.path.join(training_args.output_dir , """eval_results.txt"""       )

		with open(snake_case__ , """w"""       ) as writer:
			logger.info("""***** Eval results *****"""       )

			for key, value in result.items():
				logger.info(F"""  {key} = {value}"""       )
				writer.write(F"""{key} = {value}\n"""       )

			results.update(snake_case__       )

	return results


if __name__ == "__main__":
							main()









"""simple docstring"""
def       __UpperCamelCase						(		snake_case__ = 200       ):
	A_		:    Union[str, Any]						     = [1, 2, 5, 10, 20, 50, 100, 200]
	A_		:    int						     = [0] * (pence + 1)
	A_		:    Tuple						     = 1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(snake_case__ , pence + 1 , 1       ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
							assert solution(200) == 73_682

