






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__":
					snake_case_   :	List[Any]							      =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)

					snake_case_      ,	snake_case_   :	Tuple							      =parser.parse_known_args()

					# overwrite batch size until we have tf_glue.py
					snake_case_   :	Optional[int]							      =16
					snake_case_   :	Any							      =16

					# Set up logging
					snake_case_   :	int							      =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
					snake_case_   :	Dict							      =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
					snake_case_   :	Dict							      =AutoTokenizer.from_pretrained(args.model_name_or_path)

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

					# Preprocess train dataset
					snake_case_   :	Optional[int]							      =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'''])

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

					# Preprocess test dataset
					snake_case_   :	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'''])

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

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

					snake_case_   :	List[Any]							      =time.time()
					snake_case_   :	Union[str, Any]							      =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
					snake_case_   :	List[str]							      =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}""")


from collections import OrderedDict
from typing import Mapping

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


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



