


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase     :					Union[str, Any]       =logging.get_logger(__name__)

_UpperCAmelCase     :					Union[str, Any]       ={
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Optional[Any]   =							"""electra"""




						def __init__(			self				,  __lowercase=3_0_5_2_2				,  __lowercase=1_2_8				,  __lowercase=2_5_6				,  __lowercase=1_2				,  __lowercase=4				,  __lowercase=1_0_2_4				,  __lowercase="gelu"				,  __lowercase=0.1				,  __lowercase=0.1				,  __lowercase=5_1_2				,  __lowercase=2				,  __lowercase=0.02				,  __lowercase=1e-12				,  __lowercase="first"				,  __lowercase=True				,  __lowercase="gelu"				,  __lowercase=0.1				,  __lowercase=0				,  __lowercase="absolute"				,  __lowercase=True				,  __lowercase=None				,  **__lowercase				,  )   ->      Union[str, Any]:
										super().__init__(pad_token_id=__lowercase				,  **__lowercase							)

										lowerCAmelCase_	: str      			=			vocab_size
										lowerCAmelCase_	: Optional[Any]      			=			embedding_size
										lowerCAmelCase_	: str      			=			hidden_size
										lowerCAmelCase_	: List[Any]      			=			num_hidden_layers
										lowerCAmelCase_	: int      			=			num_attention_heads
										lowerCAmelCase_	: int      			=			intermediate_size
										lowerCAmelCase_	: List[str]      			=			hidden_act
										lowerCAmelCase_	: Optional[Any]      			=			hidden_dropout_prob
										lowerCAmelCase_	: int      			=			attention_probs_dropout_prob
										lowerCAmelCase_	: List[Any]      			=			max_position_embeddings
										lowerCAmelCase_	: Dict      			=			type_vocab_size
										lowerCAmelCase_	: Union[str, Any]      			=			initializer_range
										lowerCAmelCase_	: str      			=			layer_norm_eps

										lowerCAmelCase_	: Dict      			=			summary_type
										lowerCAmelCase_	: Any      			=			summary_use_proj
										lowerCAmelCase_	: Tuple      			=			summary_activation
										lowerCAmelCase_	: Dict      			=			summary_last_dropout
										lowerCAmelCase_	: Optional[int]      			=			position_embedding_type
										lowerCAmelCase_	: Tuple      			=			use_cache
										lowerCAmelCase_	: Tuple      			=			classifier_dropout







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						@property
						def   lowercase_	(			self							)   ->      Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
														lowerCAmelCase_	: int      			=			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
														lowerCAmelCase_	: Optional[Any]      			=			{0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										        ('''token_type_ids''', dynamic_axis),
										    ]							)



import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
						_UpperCAmelCase     :					Optional[int]       =argparse.ArgumentParser()

						# Hyperparameters sent by the client are passed as command-line arguments to the script.
						parser.add_argument("""--epochs""", type=int, default=1)
						parser.add_argument("""--per_device_train_batch_size""", type=int, default=16)
						parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
						parser.add_argument("""--model_name_or_path""", type=str)
						parser.add_argument("""--learning_rate""", type=str, default=5E-5)
						parser.add_argument("""--do_train""", type=bool, default=True)
						parser.add_argument("""--do_eval""", type=bool, default=True)
						parser.add_argument("""--output_dir""", type=str)

						_UpperCAmelCase       ,   _UpperCAmelCase     :					List[str]       =parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						_UpperCAmelCase     :					Optional[Any]       =16
						_UpperCAmelCase     :					List[str]       =16

						# Set up logging
						_UpperCAmelCase     :					Optional[Any]       =logging.getLogger(__name__)

						logging.basicConfig(
						    level=logging.getLevelName("""INFO"""),
						    handlers=[logging.StreamHandler(sys.stdout)],
						    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
						)

						# Load model and tokenizer
						_UpperCAmelCase     :					List[str]       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_UpperCAmelCase     :					Any       =AutoTokenizer.from_pretrained(args.model_name_or_path)

						# Load dataset
						_UpperCAmelCase       ,   _UpperCAmelCase     :					Union[str, Any]       =load_dataset("""imdb""", split=["""train""", """test"""])
						_UpperCAmelCase     :					int       =train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
						_UpperCAmelCase     :					str       =test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

						# Preprocess train dataset
						_UpperCAmelCase     :					str       =train_dataset.map(
						    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
						)
						train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

						_UpperCAmelCase     :					List[Any]       ={
						    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["""input_ids""", """attention_mask"""]
						}
						_UpperCAmelCase     :					Optional[Any]       =tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
						    args.per_device_train_batch_size
						)

						# Preprocess test dataset
						_UpperCAmelCase     :					List[Any]       =test_dataset.map(
						    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
						)
						test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

						_UpperCAmelCase     :					Dict       ={
						    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["""input_ids""", """attention_mask"""]
						}
						_UpperCAmelCase     :					Any       =tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
						    args.per_device_eval_batch_size
						)

						# fine optimizer and loss
						_UpperCAmelCase     :					List[Any]       =tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
						_UpperCAmelCase     :					str       =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
						_UpperCAmelCase     :					Optional[Any]       =[tf.keras.metrics.SparseCategoricalAccuracy()]
						model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

						_UpperCAmelCase     :					Tuple       =time.time()
						_UpperCAmelCase     :					List[str]       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						_UpperCAmelCase     :					Optional[int]       =time.time() - start_train_time

						logger.info("""*** Train ***""")
						logger.info(f"""train_runtime = {end_train_time}""")
						for key, value in train_results.history.items():
												logger.info(f"""  {key} = {value}""")