






"""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_	:					int     	=   {}

			if train_file is not None:
						UpperCAmelCase_	:					Tuple     	=   [train_file]
			if eval_file is not None:
						UpperCAmelCase_	:					Union[str, Any]     	=   [eval_file]
			if test_file is not None:
						UpperCAmelCase_	:					Tuple     	=   [test_file]

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

			if len(A__		) == 1:
						for k in files.keys():
									UpperCAmelCase_	:					Any     	=   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_	:					Union[str, Any]     	=   {k: v for k, v in ex.items() if k in input_names}
									UpperCAmelCase_	:					Any     	=   labelaid[ex[label_name]]
									yield (d, label)

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

			UpperCAmelCase_	:					List[Any]     	=   (
			    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_	:					Optional[Any]     	=   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

			UpperCAmelCase_	:					Optional[Any]     	=   (
			    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_	:					Optional[Any]     	=   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_	:					List[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_	:					Dict     	=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
			UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[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.

			UpperCAmelCase_	:					Optional[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_	:					Tuple     	=   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_	:					Optional[int]     	=   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_	:					Any     	=   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_	:					Any     	=   np.argmax(p.predictions      ,axis=1		)

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

			# Initialize our Trainer
			UpperCAmelCase_	:					Optional[int]     	=   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_	:					List[str]     	=   {}
			if training_args.do_eval:
						logger.info("*** Evaluate ***"		)
						UpperCAmelCase_	:					str     	=   trainer.evaluate()
						UpperCAmelCase_	:					Tuple     	=   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 json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


SCREAMING_SNAKE_CASE							:				int     	=			get_tests_dir("fixtures")
SCREAMING_SNAKE_CASE							:				str     	=			get_tests_dir("fixtures/dummy_feature_extractor_config.json")
SCREAMING_SNAKE_CASE							:				str     	=			get_tests_dir("fixtures/dummy-config.json")






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				0



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							str:
											with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE_				=				WavaVecaConfig()

																		# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       ).to_dict()

																		config_dict.pop('feature_extractor_type'       )
																		SCREAMING_SNAKE_CASE_				=				WavaVecaFeatureExtractor(**_lowercase       )

																		# save in new folder
																		model_config.save_pretrained(_lowercase       )
																		config.save_pretrained(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )

																		# make sure private variable is not incorrectly saved
																		SCREAMING_SNAKE_CASE_				=				json.loads(config.to_json_string()       )
																		self.assertTrue('_processor_class' not in dict_as_saved       )

											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							Any:
											with self.assertRaisesRegex(
											    _lowercase,     'bert-base is not a local folder and is not a valid model identifier'       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('bert-base'       )



				def   a__					(    self       )     ->							List[Any]:
											with self.assertRaisesRegex(
											    _lowercase,     R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase,     revision='aaaaaa'       )



				def   a__					(    self       )     ->							List[Any]:
											with self.assertRaisesRegex(
											    _lowercase,     'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.',     ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'       )



				def   a__					(    self       )     ->							List[str]:
											# If remote code is not set, we will time out when asking whether to load the model.
											with self.assertRaises(_lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       )
											# If remote code is disabled, we can't load this config.
											with self.assertRaises(_lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
											    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
											self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )

											# Test feature extractor can be reloaded.
											with tempfile.TemporaryDirectory() as tmp_dir:
																		feature_extractor.save_pretrained(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase,     trust_remote_code=_lowercase       )
											self.assertEqual(reloaded_feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )



				def   a__					(    self       )     ->							int:
											try:
																		AutoConfig.register('custom',     _lowercase       )
																		AutoFeatureExtractor.register(_lowercase,     _lowercase       )
																		# Trying to register something existing in the Transformers library will raise an error
																		with self.assertRaises(_lowercase       ):
																									AutoFeatureExtractor.register(_lowercase,     _lowercase       )

																		# Now that the config is registered, it can be used as any other config with the auto-API
																		SCREAMING_SNAKE_CASE_				=				CustomFeatureExtractor.from_pretrained(_lowercase       )
																		with tempfile.TemporaryDirectory() as tmp_dir:
																									feature_extractor.save_pretrained(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
																									self.assertIsInstance(_lowercase,     _lowercase       )

											finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																									del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





				def   a__					(    self       )     ->							str:






											class    snake_case     (      lowercase_		):






															"""simple docstring"""
															_a    	=     True

											try:
																		AutoConfig.register('custom',     _lowercase       )
																		AutoFeatureExtractor.register(_lowercase,     _lowercase       )
																		# If remote code is not set, the default is to use local
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(feature_extractor.is_local       )

																		# If remote code is disabled, we load the local one.
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(feature_extractor.is_local       )

																		# If remote is enabled, we load from the Hub
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(not hasattr(_lowercase,     'is_local'       )       )

											finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																									del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





