




'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


A					       =logging.get_logger(__name__)

A					       ={
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}


class      _a    (      __a					):
       __a							:	Tuple  							=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       __a							:	Dict  							=   """squeezebert"""
       def __init__(      self		:      str  ,							lowercase		:      str=30_522  ,							lowercase		:      Dict=768  ,							lowercase		:      Optional[Any]=12  ,							lowercase		:      Optional[Any]=12  ,							lowercase		:      str=3_072  ,							lowercase		:      int="gelu"  ,							lowercase		:      Any=0.1  ,							lowercase		:      int=0.1  ,							lowercase		:      Dict=512  ,							lowercase		:      Tuple=2  ,							lowercase		:      int=0.02  ,							lowercase		:      Dict=1E-12  ,							lowercase		:      Dict=0  ,							lowercase		:      Dict=768  ,							lowercase		:      Union[str, Any]=4  ,							lowercase		:      Any=4  ,							lowercase		:      str=4  ,							lowercase		:      Optional[int]=1  ,							lowercase		:      str=4  ,							lowercase		:      Optional[int]=4  ,							**lowercase		:      Optional[int]  ,							):



           '''simple docstring'''

           super().__init__(pad_token_id=lowercase  ,							**lowercase							)

           UpperCAmelCase     			=						vocab_size
           UpperCAmelCase     			=						hidden_size
           UpperCAmelCase     			=						num_hidden_layers
           UpperCAmelCase     			=						num_attention_heads
           UpperCAmelCase     			=						hidden_act
           UpperCAmelCase     			=						intermediate_size
           UpperCAmelCase     			=						hidden_dropout_prob
           UpperCAmelCase     			=						attention_probs_dropout_prob
           UpperCAmelCase     			=						max_position_embeddings
           UpperCAmelCase     			=						type_vocab_size
           UpperCAmelCase     			=						initializer_range
           UpperCAmelCase     			=						layer_norm_eps
           UpperCAmelCase     			=						embedding_size
           UpperCAmelCase     			=						q_groups
           UpperCAmelCase     			=						k_groups
           UpperCAmelCase     			=						v_groups
           UpperCAmelCase     			=						post_attention_groups
           UpperCAmelCase     			=						intermediate_groups
           UpperCAmelCase     			=						output_groups






class      _a    (      __a					):
       @property
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           if self.task == "multiple-choice":
               UpperCAmelCase     			=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
               UpperCAmelCase     			=						{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
                   ('''token_type_ids''', dynamic_axis),
               ]							)










'''simple docstring'''







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__":
   A					       =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)

   A       , A					       =parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A					       =16
   A					       =16

   # Set up logging
   A					       =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
   A					       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A					       =AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A       , A					       =load_dataset('imdb', split=['train', 'test'])
   A					       =train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
   A					       =test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   A					       =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'])

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

   # Preprocess test dataset
   A					       =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'])

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

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

   A					       =time.time()
   A					       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A					       =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}""")




