




from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

UpperCAmelCase_		:				Optional[Any]									=     {
    # 1536-bit
    5: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 2048-bit
    14: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 3072-bit
    15: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 4096-bit
    16: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
            + "FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 6144-bit
    17: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
            + "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
            + "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
            + "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
            + "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
            + "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
            + "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
            + "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
            + "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
            + "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
            + "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
            + "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
            + "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
            + "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
            + "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
            + "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
            + "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
            + "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
            + "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
            + "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
            + "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
            + "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
            + "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
            + "6DCC4024FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 8192-bit
    18: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
            + "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
            + "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
            + "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
            + "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
            + "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
            + "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
            + "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
            + "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
            + "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
            + "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
            + "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
            + "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
            + "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
            + "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
            + "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
            + "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
            + "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
            + "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
            + "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
            + "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
}




class 		__A      :

						def __init__(		self   :Union[str, Any]    , __snake_case   :int = 14 ):
										'''simple docstring'''

										if group not in primes:
														raise ValueError("""Unsupported Group""" )
										__magic_name__	:	Any								=primes[group]["""prime"""]
										__magic_name__	:	Optional[Any]								=primes[group]["""generator"""]

										__magic_name__	:	int								=int(hexlify(urandom(32 ) )    , base=16 )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										return hex(self.__private_key )[2:]

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=pow(self.generator    , self.__private_key    , self.prime )
										return hex(__snake_case )[2:]

						def     A__						(		self   :Optional[int]    , __snake_case   :int ):
										'''simple docstring'''

										return (
										    2 <= key <= self.prime - 2
										    and pow(__snake_case    , (self.prime - 1) // 2    , self.prime ) == 1
										)

						def     A__						(		self   :str    , __snake_case   :str ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=int(__snake_case    , base=16 )
										if not self.is_valid_public_key(__snake_case ):
														raise ValueError("""Invalid public key""" )
										__magic_name__	:	List[str]								=pow(__snake_case    , self.__private_key    , self.prime )
										return shaaaa(str(__snake_case ).encode() ).hexdigest()

						@staticmethod
						def     A__						(		__snake_case   :int    , __snake_case   :int ):
										'''simple docstring'''

										return (
										    2 <= remote_public_key_str <= prime - 2
										    and pow(__snake_case    , (prime - 1) // 2    , __snake_case ) == 1
										)


						@staticmethod
						def     A__						(		__snake_case   :str    , __snake_case   :str    , __snake_case   :int = 14 ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=int(__snake_case    , base=16 )
										__magic_name__	:	List[Any]								=int(__snake_case    , base=16 )
										__magic_name__	:	Tuple								=primes[group]["""prime"""]
										if not DiffieHellman.is_valid_public_key_static(__snake_case    , __snake_case ):
														raise ValueError("""Invalid public key""" )
										__magic_name__	:	Dict								=pow(__snake_case    , __snake_case    , __snake_case )
										return shaaaa(str(__snake_case ).encode() ).hexdigest()


if __name__ == "__main__":
							import doctest

							doctest.testmod()










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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = None	,     ):
				__magic_name__	:	Optional[int]								={}

				if train_file is not None:
								__magic_name__	:	Optional[int]								=[train_file]
				if eval_file is not None:
								__magic_name__	:	Any								=[eval_file]
				if test_file is not None:
								__magic_name__	:	int								=[test_file]

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

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

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

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

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

				__magic_name__	:	Union[str, Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({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:
								__magic_name__	:	Optional[Any]								=train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

				__magic_name__	:	Optional[Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({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:
								__magic_name__	:	Any								=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

				__magic_name__	:	Any								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({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:
								__magic_name__	:	Optional[int]								=test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

				return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_		:				int									=     logging.getLogger(__name__)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(metadata={"""help""": """Which column contains the label"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the training file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the development file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the test file"""}			)
						UpperCamelCase     						=					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     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)




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




def 		lowerCAmelCase_   (	):
				# 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.
				__magic_name__	:	List[Any]								=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
				__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, 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.

				__magic_name__	:	Dict								=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	,     )

				__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Optional[Any]								=get_tfds(
				    train_file=data_args.train_file	,     eval_file=data_args.dev_file	,     test_file=data_args.test_file	,     tokenizer=lowerCamelCase	,     label_column_id=data_args.label_column_id	,     max_seq_length=data_args.max_seq_length	,     )

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

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

				def compute_metrics(lowerCamelCase   ) -> Dict:
								__magic_name__	:	Tuple								=np.argmax(p.predictions	,     axis=1   )

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

				# Initialize our Trainer
				__magic_name__	:	int								=TFTrainer(
				    model=lowerCamelCase	,     args=lowerCamelCase	,     train_dataset=lowerCamelCase	,     eval_dataset=lowerCamelCase	,     compute_metrics=lowerCamelCase	,     )

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

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

								with open(lowerCamelCase	,     """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(lowerCamelCase   )

				return results


if __name__ == "__main__":
							main()




