


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__":
							SCREAMING_SNAKE_CASE__       			=				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=1_6)
							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)

							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							SCREAMING_SNAKE_CASE__       			=				1_6
							SCREAMING_SNAKE_CASE__       			=				1_6

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

							# Load dataset
							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				load_dataset("""imdb""", split=["""train""", """test"""])
							SCREAMING_SNAKE_CASE__       			=				train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
							SCREAMING_SNAKE_CASE__       			=				test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							SCREAMING_SNAKE_CASE__       			=				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"""])

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

							# Preprocess test dataset
							SCREAMING_SNAKE_CASE__       			=				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"""])

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

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

							SCREAMING_SNAKE_CASE__       			=				time.time()
							SCREAMING_SNAKE_CASE__       			=				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							SCREAMING_SNAKE_CASE__       			=				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 typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				"prophetnet"
							lowerCAmelCase__          =				["past_key_values"]
							lowerCAmelCase__          =				{
							    "num_attention_heads": "num_encoder_attention_heads",
							}




							def __init__(  self						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = "gelu"						,       UpperCAmelCase = 30522						,       UpperCAmelCase = 1024						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 512						,       UpperCAmelCase = 0.02						,       UpperCAmelCase = True						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 2						,       UpperCAmelCase = 32						,       UpperCAmelCase = 128						,       UpperCAmelCase = False						,       UpperCAmelCase = 0.0						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 1						,       UpperCAmelCase = 2						,       **UpperCAmelCase						,       )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = vocab_size
								lowercase_			 = hidden_size
								lowercase_			 = encoder_ffn_dim
								lowercase_			 = num_encoder_layers
								lowercase_			 = num_encoder_attention_heads
								lowercase_			 = decoder_ffn_dim
								lowercase_			 = num_decoder_layers
								lowercase_			 = num_decoder_attention_heads
								lowercase_			 = max_position_embeddings
								lowercase_			 = init_std  # Normal(0, this parameter)
								lowercase_			 = activation_function

								# parameters for prophetnet
								lowercase_			 = ngram
								lowercase_			 = num_buckets
								lowercase_			 = relative_max_distance
								lowercase_			 = disable_ngram_loss
								lowercase_			 = eps

								# 3 Types of Dropout
								lowercase_			 = attention_dropout
								lowercase_			 = activation_dropout
								lowercase_			 = dropout

								lowercase_			 = use_cache

								super().__init__(
								    pad_token_id=UpperCAmelCase						,       bos_token_id=UpperCAmelCase						,       eos_token_id=UpperCAmelCase						,       is_encoder_decoder=UpperCAmelCase						,       add_cross_attention=UpperCAmelCase						,       decoder_start_token_id=UpperCAmelCase						,       **UpperCAmelCase						,       )




							@property
							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								raise NotImplementedError(
								    "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
								    " `num_decoder_layers`."  )

