


import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

_UpperCAmelCase			:  Any     =logging.getLogger(__name__)







@dataclass
class 				snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Optional[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."""
						        )
						    },						)
						SCREAMING_SNAKE_CASE__      :  bool					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}							)
						SCREAMING_SNAKE_CASE__      :  bool					=							field(
						    default=UpperCAmelCase__,						metadata={
						        """help""": (
						            """Whether to pad all samples to `max_seq_length`. """
						            """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
						        )
						    },						)
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							field(
						    default=UpperCAmelCase__,						metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of training examples to this """
						            """value if set."""
						        )
						    },						)
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							field(
						    default=UpperCAmelCase__,						metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
						            """value if set."""
						        )
						    },						)
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							field(
						    default=UpperCAmelCase__,						metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of prediction examples to this """
						            """value if set."""
						        )
						    },						)







@dataclass
class 				snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  str					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
						SCREAMING_SNAKE_CASE__      :  str					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}							)
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Train language if it is different from the evaluation language."""}							)
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},						)
						SCREAMING_SNAKE_CASE__      :  Optional[bool]					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""},						)
						SCREAMING_SNAKE_CASE__      :  bool					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""},						)
						SCREAMING_SNAKE_CASE__      :  str					=							field(
						    default="""main""",						metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""},						)
						SCREAMING_SNAKE_CASE__      :  bool					=							field(
						    default=UpperCAmelCase__,						metadata={
						        """help""": (
						            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
						            """with private models)."""
						        )
						    },						)
						SCREAMING_SNAKE_CASE__      :  bool					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""},						)






def 			lowerCAmelCase (		)-> int:
				# 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_ :      List[str]    				=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      str    				=				parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('''run_xnli''' ,   lowerCAmelCase_ )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,   datefmt='''%m/%d/%Y %H:%M:%S''' ,   handlers=[logging.StreamHandler(sys.stdout )] ,   )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				lowerCAmelCase_ :      Optional[Any]    				=				training_args.get_process_log_level()
				logger.setLevel(lowerCAmelCase_ )
				datasets.utils.logging.set_verbosity(lowerCAmelCase_ )
				transformers.utils.logging.set_verbosity(lowerCAmelCase_ )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
				    + f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
				logger.info(f"""Training/evaluation parameters {training_args}""" )

				# Detecting last checkpoint.
				lowerCAmelCase_ :      List[Any]    				=				None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								lowerCAmelCase_ :      Optional[Any]    				=				get_last_checkpoint(training_args.output_dir )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
												raise ValueError(
												    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
												    '''Use --overwrite_output_dir to overcome.''' )
								elif last_checkpoint is not None:
												logger.info(
												    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
												    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )

    # Set seed before initializing model.
				set_seed(training_args.seed )

				# In distributed training, the load_dataset function guarantees that only one local process can concurrently
				# download the dataset.
				# Downloading and loading xnli dataset from the hub.
				if training_args.do_train:
								if model_args.train_language is None:
												lowerCAmelCase_ :      int    				=				load_dataset(
												    '''xnli''' ,   model_args.language ,   split='''train''' ,   cache_dir=model_args.cache_dir ,   use_auth_token=True if model_args.use_auth_token else None ,   )
								else:
												lowerCAmelCase_ :      List[str]    				=				load_dataset(
												    '''xnli''' ,   model_args.train_language ,   split='''train''' ,   cache_dir=model_args.cache_dir ,   use_auth_token=True if model_args.use_auth_token else None ,   )
								lowerCAmelCase_ :      List[Any]    				=				train_dataset.features['''label'''].names

				if training_args.do_eval:
								lowerCAmelCase_ :      Union[str, Any]    				=				load_dataset(
								    '''xnli''' ,   model_args.language ,   split='''validation''' ,   cache_dir=model_args.cache_dir ,   use_auth_token=True if model_args.use_auth_token else None ,   )
								lowerCAmelCase_ :      Optional[int]    				=				eval_dataset.features['''label'''].names

				if training_args.do_predict:
								lowerCAmelCase_ :      List[Any]    				=				load_dataset(
								    '''xnli''' ,   model_args.language ,   split='''test''' ,   cache_dir=model_args.cache_dir ,   use_auth_token=True if model_args.use_auth_token else None ,   )
								lowerCAmelCase_ :      Union[str, Any]    				=				predict_dataset.features['''label'''].names

				# Labels
				lowerCAmelCase_ :      str    				=				len(lowerCAmelCase_ )

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				lowerCAmelCase_ :      str    				=				AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,   num_labels=lowerCAmelCase_ ,   idalabel={str(lowerCAmelCase_ ): label for i, label in enumerate(lowerCAmelCase_ )} ,   labelaid={label: i for i, label in enumerate(lowerCAmelCase_ )} ,   finetuning_task='''xnli''' ,   cache_dir=model_args.cache_dir ,   revision=model_args.model_revision ,   use_auth_token=True if model_args.use_auth_token else None ,   )
				lowerCAmelCase_ :      List[str]    				=				AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,   do_lower_case=model_args.do_lower_case ,   cache_dir=model_args.cache_dir ,   use_fast=model_args.use_fast_tokenizer ,   revision=model_args.model_revision ,   use_auth_token=True if model_args.use_auth_token else None ,   )
				lowerCAmelCase_ :      Optional[Any]    				=				AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) ,   config=lowerCAmelCase_ ,   cache_dir=model_args.cache_dir ,   revision=model_args.model_revision ,   use_auth_token=True if model_args.use_auth_token else None ,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,   )

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								lowerCAmelCase_ :      List[str]    				=				'''max_length'''
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								lowerCAmelCase_ :      Tuple    				=				False

				def preprocess_function(lowerCAmelCase_ ):
								# Tokenize the texts
								return tokenizer(
								    examples['''premise'''] ,   examples['''hypothesis'''] ,   padding=lowerCAmelCase_ ,   max_length=data_args.max_seq_length ,   truncation=lowerCAmelCase_ ,   )

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												lowerCAmelCase_ :      int    				=				min(len(lowerCAmelCase_ ) ,   data_args.max_train_samples )
												lowerCAmelCase_ :      int    				=				train_dataset.select(range(lowerCAmelCase_ ) )
								with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
												lowerCAmelCase_ :      List[Any]    				=				train_dataset.map(
												    lowerCAmelCase_ ,   batched=lowerCAmelCase_ ,   load_from_cache_file=not data_args.overwrite_cache ,   desc='''Running tokenizer on train dataset''' ,   )
								# Log a few random samples from the training set:
								for index in random.sample(range(len(lowerCAmelCase_ ) ) ,   3 ):
												logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												lowerCAmelCase_ :      Optional[Any]    				=				min(len(lowerCAmelCase_ ) ,   data_args.max_eval_samples )
												lowerCAmelCase_ :      Tuple    				=				eval_dataset.select(range(lowerCAmelCase_ ) )
								with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
												lowerCAmelCase_ :      Any    				=				eval_dataset.map(
												    lowerCAmelCase_ ,   batched=lowerCAmelCase_ ,   load_from_cache_file=not data_args.overwrite_cache ,   desc='''Running tokenizer on validation dataset''' ,   )

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												lowerCAmelCase_ :      Tuple    				=				min(len(lowerCAmelCase_ ) ,   data_args.max_predict_samples )
												lowerCAmelCase_ :      int    				=				predict_dataset.select(range(lowerCAmelCase_ ) )
								with training_args.main_process_first(desc='''prediction dataset map pre-processing''' ):
												lowerCAmelCase_ :      List[str]    				=				predict_dataset.map(
												    lowerCAmelCase_ ,   batched=lowerCAmelCase_ ,   load_from_cache_file=not data_args.overwrite_cache ,   desc='''Running tokenizer on prediction dataset''' ,   )

    # Get the metric function
				lowerCAmelCase_ :      Optional[int]    				=				evaluate.load('''xnli''' )

				# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(lowerCAmelCase_ ):
								lowerCAmelCase_ :      int    				=				p.predictions[0] if isinstance(p.predictions ,   lowerCAmelCase_ ) else p.predictions
								lowerCAmelCase_ :      List[str]    				=				np.argmax(lowerCAmelCase_ ,   axis=1 )
								return metric.compute(predictions=lowerCAmelCase_ ,   references=p.label_ids )

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								lowerCAmelCase_ :      Tuple    				=				default_data_collator
				elif training_args.fpaa:
								lowerCAmelCase_ :      Optional[int]    				=				DataCollatorWithPadding(lowerCAmelCase_ ,   pad_to_multiple_of=8 )
				else:
								lowerCAmelCase_ :      List[str]    				=				None

				# Initialize our Trainer
				lowerCAmelCase_ :      Union[str, Any]    				=				Trainer(
				    model=lowerCAmelCase_ ,   args=lowerCAmelCase_ ,   train_dataset=train_dataset if training_args.do_train else None ,   eval_dataset=eval_dataset if training_args.do_eval else None ,   compute_metrics=lowerCAmelCase_ ,   tokenizer=lowerCAmelCase_ ,   data_collator=lowerCAmelCase_ ,   )

				# Training
				if training_args.do_train:
								lowerCAmelCase_ :      List[Any]    				=				None
								if training_args.resume_from_checkpoint is not None:
												lowerCAmelCase_ :      str    				=				training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowerCAmelCase_ :      Tuple    				=				last_checkpoint
								lowerCAmelCase_ :      int    				=				trainer.train(resume_from_checkpoint=lowerCAmelCase_ )
								lowerCAmelCase_ :      List[Any]    				=				train_result.metrics
								lowerCAmelCase_ :      Tuple    				=				(
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase_ )
								)
								lowerCAmelCase_ :      List[Any]    				=				min(lowerCAmelCase_ ,   len(lowerCAmelCase_ ) )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics('''train''' ,   lowerCAmelCase_ )
								trainer.save_metrics('''train''' ,   lowerCAmelCase_ )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***''' )
								lowerCAmelCase_ :      str    				=				trainer.evaluate(eval_dataset=lowerCAmelCase_ )

								lowerCAmelCase_ :      Optional[Any]    				=				data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCAmelCase_ )
								lowerCAmelCase_ :      List[str]    				=				min(lowerCAmelCase_ ,   len(lowerCAmelCase_ ) )

								trainer.log_metrics('''eval''' ,   lowerCAmelCase_ )
								trainer.save_metrics('''eval''' ,   lowerCAmelCase_ )

				# Prediction
				if training_args.do_predict:
								logger.info('''*** Predict ***''' )
								lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Tuple    				=				trainer.predict(lowerCAmelCase_ ,   metric_key_prefix='''predict''' )

								lowerCAmelCase_ :      Any    				=				(
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCAmelCase_ )
								)
								lowerCAmelCase_ :      List[str]    				=				min(lowerCAmelCase_ ,   len(lowerCAmelCase_ ) )

								trainer.log_metrics('''predict''' ,   lowerCAmelCase_ )
								trainer.save_metrics('''predict''' ,   lowerCAmelCase_ )

								lowerCAmelCase_ :      Union[str, Any]    				=				np.argmax(lowerCAmelCase_ ,   axis=1 )
								lowerCAmelCase_ :      Any    				=				os.path.join(training_args.output_dir ,   '''predictions.txt''' )
								if trainer.is_world_process_zero():
												with open(lowerCAmelCase_ ,   '''w''' ) as writer:
																writer.write('''index\tprediction\n''' )
																for index, item in enumerate(lowerCAmelCase_ ):
																				lowerCAmelCase_ :      List[Any]    				=				label_list[item]
																				writer.write(f"""{index}\t{item}\n""" )


if __name__ == "__main__":
						main()



from __future__ import annotations

from math import pi



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> dict[str, float]:

				if (inductance, frequency, reactance).count(0 ) != 1:
								raise ValueError('''One and only one argument must be 0''' )
				if inductance < 0:
								raise ValueError('''Inductance cannot be negative''' )
				if frequency < 0:
								raise ValueError('''Frequency cannot be negative''' )
				if reactance < 0:
								raise ValueError('''Inductive reactance cannot be negative''' )
				if inductance == 0:
								return {"inductance": reactance / (2 * pi * frequency)}
				elif frequency == 0:
								return {"frequency": reactance / (2 * pi * inductance)}
				elif reactance == 0:
								return {"reactance": 2 * pi * frequency * inductance}
				else:
								raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
						import doctest

						doctest.testmod()