






"""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        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__ = None			,):
			UpperCAmelCase_  :				List[str]      =      {}

			if train_file is not None:
						UpperCAmelCase_  :				List[str]      =      [train_file]
			if eval_file is not None:
						UpperCAmelCase_  :				int      =      [eval_file]
			if test_file is not None:
						UpperCAmelCase_  :				Dict      =      [test_file]

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

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

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

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

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

			UpperCAmelCase_  :				Dict      =      (
			    tf.data.Dataset.from_generator(
			        A__			,({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:
						UpperCAmelCase_  :				Any      =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

			UpperCAmelCase_  :				Tuple      =      (
			    tf.data.Dataset.from_generator(
			        A__			,({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:
						UpperCAmelCase_  :				str      =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

			UpperCAmelCase_  :				Dict      =      (
			    tf.data.Dataset.from_generator(
			        A__			,({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:
						UpperCAmelCase_  :				Union[str, Any]      =      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     UpperCamelCase_ :
	__magic_name__			   =       field(metadata={'''help''': '''Which column contains the label'''}  )
	__magic_name__			   =       field(default=__A       ,		metadata={'''help''': '''The path of the training file'''}  )
	__magic_name__			   =       field(default=__A       ,		metadata={'''help''': '''The path of the development file'''}  )
	__magic_name__			   =       field(default=__A       ,		metadata={'''help''': '''The path of the test file'''}  )
	__magic_name__			   =       field(
	    default=1_28       ,		metadata={
	        '''help''': (
	            '''The maximum total input sequence length after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }       ,		)
	__magic_name__			   =       field(
	    default=__A       ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )







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





def        snake_case   (     ):
			# 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_  :				Tuple      =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				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.

			UpperCAmelCase_  :				Union[str, 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_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Optional[Any]      =      get_tfds(
			    train_file=data_args.train_file			,eval_file=data_args.dev_file			,test_file=data_args.test_file			,tokenizer=A__			,label_column_id=data_args.label_column_id			,max_seq_length=data_args.max_seq_length			,)

			UpperCAmelCase_  :				Dict      =      AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,num_labels=len(A__		)			,labelaid=A__			,idalabel={id: label for label, id in labelaid.items()}			,finetuning_task="text-classification"			,cache_dir=model_args.cache_dir			,)

			with training_args.strategy.scope():
						UpperCAmelCase_  :				int      =      TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path			,from_pt=bool(".bin" in model_args.model_name_or_path		)			,config=A__			,cache_dir=model_args.cache_dir			,)

			def compute_metrics(A__		) -> Dict:
						UpperCAmelCase_  :				int      =      np.argmax(p.predictions			,axis=1		)

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

			# Initialize our Trainer
			UpperCAmelCase_  :				Tuple      =      TFTrainer(
			    model=A__			,args=A__			,train_dataset=A__			,eval_dataset=A__			,compute_metrics=A__			,)

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

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

						with open(A__			,"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(A__		)

			return results


if __name__ == "__main__":
			main()











"""simple docstring"""
import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs







class     UpperCamelCase_ (__A  ):





	def __init__(   self      :				List[Any]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				List[Any]=None			,			lowerCAmelCase_      :				List[str]=True			,			lowerCAmelCase_      :				List[Any]=None			,			**lowerCAmelCase_      :				Tuple		)		->    Dict:
				UpperCAmelCase_  :				Tuple      =      parent
				UpperCAmelCase_  :				Optional[int]      =      config_class
				UpperCAmelCase_  :				List[str]      =      has_text_modality
				UpperCAmelCase_  :				Tuple      =      kwargs
				UpperCAmelCase_  :				int      =      common_properties





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Optional[int]      =      self.config_class(**self.inputs_dict		)
				UpperCAmelCase_  :				int      =      (
				    ["hidden_size", "num_attention_heads", "num_hidden_layers"]
				    if self.common_properties is None
				    else self.common_properties
				)

				# Add common fields for text models
				if self.has_text_modality:
							common_properties.extend(["vocab_size"]		)

				# Test that config has the common properties as getters
				for prop in common_properties:
							self.parent.assertTrue(hasattr(lowerCAmelCase_			,			lowerCAmelCase_		)			,			msg=f"""`{prop}` does not exist"""		)

				# Test that config has the common properties as setter
				for idx, name in enumerate(lowerCAmelCase_		):
							try:
										setattr(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
										self.parent.assertEqual(
										    getattr(lowerCAmelCase_			,			lowerCAmelCase_		)			,			lowerCAmelCase_			,			msg=f"""`{name} value {idx} expected, but was {getattr(lowerCAmelCase_			,			lowerCAmelCase_		)}"""		)
							except NotImplementedError:
										# Some models might not be able to implement setters for common_properties
										# In that case, a NotImplementedError is raised
										pass

        # Test if config class can be called with Config(prop_name=..)
				for idx, name in enumerate(lowerCAmelCase_		):
							try:
										UpperCAmelCase_  :				Optional[Any]      =      self.config_class(**{name: idx}		)
										self.parent.assertEqual(
										    getattr(lowerCAmelCase_			,			lowerCAmelCase_		)			,			lowerCAmelCase_			,			msg=f"""`{name} value {idx} expected, but was {getattr(lowerCAmelCase_			,			lowerCAmelCase_		)}"""		)
							except NotImplementedError:
										# Some models might not be able to implement setters for common_properties
										# In that case, a NotImplementedError is raised
										pass





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Tuple:
				UpperCAmelCase_  :				str      =      self.config_class(**self.inputs_dict		)
				UpperCAmelCase_  :				List[str]      =      json.loads(config.to_json_string()		)
				for key, value in self.inputs_dict.items():
							self.parent.assertEqual(obj[key]			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Any:
				UpperCAmelCase_  :				Optional[Any]      =      self.config_class(**self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				List[str]      =      os.path.join(lowerCAmelCase_			,			"config.json"		)
							config_first.to_json_file(lowerCAmelCase_		)
							UpperCAmelCase_  :				Dict      =      self.config_class.from_json_file(lowerCAmelCase_		)

				self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Dict:
				UpperCAmelCase_  :				Any      =      self.config_class(**self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							config_first.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_  :				Tuple      =      self.config_class.from_pretrained(lowerCAmelCase_		)

				self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Optional[Any]:
				UpperCAmelCase_  :				Any      =      self.config_class(**self.inputs_dict		)

				UpperCAmelCase_  :				int      =      "test"
				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				Optional[int]      =      os.path.join(lowerCAmelCase_			,			lowerCAmelCase_		)
							config_first.save_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_  :				Union[str, Any]      =      self.config_class.from_pretrained(lowerCAmelCase_			,			subfolder=lowerCAmelCase_		)

				self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Tuple:
				UpperCAmelCase_  :				List[str]      =      self.config_class(**self.inputs_dict			,			num_labels=5		)
				self.parent.assertEqual(len(config.idalabel		)			,			5		)
				self.parent.assertEqual(len(config.labelaid		)			,			5		)

				UpperCAmelCase_  :				List[Any]      =      3
				self.parent.assertEqual(len(config.idalabel		)			,			3		)
				self.parent.assertEqual(len(config.labelaid		)			,			3		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Optional[Any]:
				if self.config_class.is_composition:
							return
				UpperCAmelCase_  :				str      =      self.config_class()
				self.parent.assertIsNotNone(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[str]:
				UpperCAmelCase_  :				Optional[int]      =      copy.deepcopy(lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      self.config_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      []
				for key, value in config_common_kwargs.items():
							if key == "torch_dtype":
										if not is_torch_available():
													continue
										else:
													import torch

													if config.torch_dtype != torch.floataa:
																wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)		)
							elif getattr(lowerCAmelCase_			,			lowerCAmelCase_		) != value:
										wrong_values.append((key, getattr(lowerCAmelCase_			,			lowerCAmelCase_		), value)		)

				if len(lowerCAmelCase_		) > 0:
							UpperCAmelCase_  :				Any      =      "\n".join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]		)
							raise ValueError(f"""The following keys were not properly set in the config:\n{errors}"""		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    str:
				self.create_and_test_config_common_properties()
				self.create_and_test_config_to_json_string()
				self.create_and_test_config_to_json_file()
				self.create_and_test_config_from_and_save_pretrained()
				self.create_and_test_config_from_and_save_pretrained_subfolder()
				self.create_and_test_config_with_num_labels()
				self.check_config_can_be_init_without_params()
				self.check_config_arguments_init()



