



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      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      List[str]      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Any      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      int = None      ,			)  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		{}

			if train_file is not None:
						lowerCamelCase__ :      Any								=		[train_file]
			if eval_file is not None:
						lowerCamelCase__ :      str								=		[eval_file]
			if test_file is not None:
						lowerCamelCase__ :      Any								=		[test_file]

			lowerCamelCase__ :      int								=		datasets.load_dataset("""csv"""      ,			data_files=SCREAMING_SNAKE_CASE_ )
			lowerCamelCase__ :      Tuple								=		list(ds[list(files.keys() )[0]].features.keys() )
			lowerCamelCase__ :      int								=		features_name.pop(SCREAMING_SNAKE_CASE_ )
			lowerCamelCase__ :      Tuple								=		list(set(ds[list(files.keys() )[0]][label_name] ) )
			lowerCamelCase__ :      List[str]								=		{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_ )}
			lowerCamelCase__ :      Optional[int]								=		tokenizer.model_input_names
			lowerCamelCase__ :      List[str]								=		{}

			if len(SCREAMING_SNAKE_CASE_ ) == 1:
						for k in files.keys():
									lowerCamelCase__ :      Optional[Any]								=		ds[k].map(
									    lambda UpperCamelCase : tokenizer.batch_encode_plus(
									        example[features_name[0]]      ,			truncation=SCREAMING_SNAKE_CASE_      ,			max_length=SCREAMING_SNAKE_CASE_      ,			padding="""max_length""" )      ,			batched=SCREAMING_SNAKE_CASE_      ,			)
			elif len(SCREAMING_SNAKE_CASE_ ) == 2:
						for k in files.keys():
									lowerCamelCase__ :      List[str]								=		ds[k].map(
									    lambda UpperCamelCase : tokenizer.batch_encode_plus(
									        (example[features_name[0]], example[features_name[1]])      ,			truncation=SCREAMING_SNAKE_CASE_      ,			max_length=SCREAMING_SNAKE_CASE_      ,			padding="""max_length"""      ,			)      ,			batched=SCREAMING_SNAKE_CASE_      ,			)

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

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

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

			lowerCamelCase__ :      Dict								=		(
			    tf.data.Dataset.from_generator(
			        SCREAMING_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:
						lowerCamelCase__ :      str								=		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

			lowerCamelCase__ :      Optional[int]								=		(
			    tf.data.Dataset.from_generator(
			        SCREAMING_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:
						lowerCamelCase__ :      List[Any]								=		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

			lowerCamelCase__ :      Optional[int]								=		(
			    tf.data.Dataset.from_generator(
			        SCREAMING_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:
						lowerCamelCase__ :      Optional[Any]								=		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

			return train_ds, val_ds, test_ds, labelaid


_lowercase     							=						logging.getLogger(__name__)
@dataclass
class 				__A					:
			UpperCamelCase					:int     =			field(metadata={'''help''': '''Which column contains the label'''}      )
			UpperCamelCase					:str     =			field(default=lowercase__					, metadata={'''help''': '''The path of the training file'''}      )
			UpperCamelCase					:Optional[str]     =			field(default=lowercase__					, metadata={'''help''': '''The path of the development file'''}      )
			UpperCamelCase					:Optional[str]     =			field(default=lowercase__					, metadata={'''help''': '''The path of the test file'''}      )
			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=lowercase__					, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )







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







def      _A				()  ->List[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
			lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      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      ,			)
			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.

			lowerCamelCase__ :      Optional[int]								=		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__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Dict								=		get_tfds(
			    train_file=data_args.train_file      ,			eval_file=data_args.dev_file      ,			test_file=data_args.test_file      ,			tokenizer=SCREAMING_SNAKE_CASE_      ,			label_column_id=data_args.label_column_id      ,			max_seq_length=data_args.max_seq_length      ,			)

			lowerCamelCase__ :      Any								=		AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,			num_labels=len(SCREAMING_SNAKE_CASE_ )      ,			labelaid=SCREAMING_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():
						lowerCamelCase__ :      List[str]								=		TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path      ,			from_pt=bool(""".bin""" in model_args.model_name_or_path )      ,			config=SCREAMING_SNAKE_CASE_      ,			cache_dir=model_args.cache_dir      ,			)

			def compute_metrics(UpperCamelCase		:      Optional[int] ) -> Dict:
						lowerCamelCase__ :      Dict								=		np.argmax(p.predictions      ,			axis=1 )

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

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

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

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

						with open(SCREAMING_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(SCREAMING_SNAKE_CASE_ )

			return results


if __name__ == "__main__":
				main()








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def      _A				(UpperCamelCase		:      str )  ->None:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		analyze_text(UpperCamelCase )
			lowerCamelCase__ :      Any								=		list(""" """ + ascii_lowercase )
			# what is our total sum of probabilities.
			lowerCamelCase__ :      str								=		sum(single_char_strings.values() )

			# one length string
			lowerCamelCase__ :      Union[str, Any]								=		0
			# for each alpha we go in our dict and if it is in it we calculate entropy
			for ch in my_alphas:
						if ch in single_char_strings:
									lowerCamelCase__ :      List[Any]								=		single_char_strings[ch]
									lowerCamelCase__ :      List[str]								=		my_str / all_sum
									my_fir_sum += prob * math.loga(UpperCamelCase )  # entropy formula.

    # print entropy
			print(f"{round(-1 * my_fir_sum ):.1f}" )

			# two len string
			lowerCamelCase__ :      str								=		sum(two_char_strings.values() )
			lowerCamelCase__ :      int								=		0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									lowerCamelCase__ :      str								=		cha + cha
									if sequence in two_char_strings:
												lowerCamelCase__ :      str								=		two_char_strings[sequence]
												lowerCamelCase__ :      int								=		int(UpperCamelCase ) / all_sum
												my_sec_sum += prob * math.loga(UpperCamelCase )

    # print second entropy
			print(f"{round(-1 * my_sec_sum ):.1f}" )

			# print the difference between them
			print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}" )






def      _A				(UpperCamelCase		:      str )  ->tuple[dict, dict]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[int]								=		Counter()  # type: ignore
			lowerCamelCase__ :      List[Any]								=		Counter()  # type: ignore
			single_char_strings[text[-1]] += 1

			# first case when we have space at start.
			two_char_strings[" " + text[0]] += 1
			for i in range(0      ,			len(UpperCamelCase ) - 1 ):
						single_char_strings[text[i]] += 1
						two_char_strings[text[i : i + 2]] += 1
			return single_char_strings, two_char_strings






def      _A				()  ->List[str]:



			'''simple docstring'''







			import doctest

			doctest.testmod()
			# text = (
			#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
			#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
			#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
			#     "beloved evident through no service elderly is. Blind there if every no so "
			#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
			#     "of message cottage windows do besides against uncivil.  Delightful "
			#     "unreserved impossible few estimating men favourable see entreaties. She "
			#     "propriety immediate was improving. He or entrance humoured likewise "
			#     "moderate. Much nor game son say feel. Fat make met can must form into "
			#     "gate. Me we offending prevailed discovery. "
			# )

			# calculate_prob(text)


if __name__ == "__main__":
				main()



