
'''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__":
						lowerCAmelCase :  Optional[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)

						lowerCAmelCase  ,		lowerCAmelCase :  Dict      =      parser.parse_known_args()

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

						# Set up logging
						lowerCAmelCase :  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
						lowerCAmelCase :  Union[str, Any]      =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						lowerCAmelCase :  List[str]      =      AutoTokenizer.from_pretrained(args.model_name_or_path)

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

						# Preprocess train dataset
						lowerCAmelCase :  Dict      =      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'])

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

						# Preprocess test dataset
						lowerCAmelCase :  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'])

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

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

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






'''simple docstring'''
import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowerCAmelCase :  Optional[Any]      =      logging.getLogger(__name__)







def 						A_(			):
							UpperCamelCase					       =			argparse.ArgumentParser(description='PyTorch Transformer Language Model')
							parser.add_argument('--model_name'						,			type=A						,			default='transfo-xl-wt103'						,			help='pretrained model name')
							parser.add_argument(
							    '--split'						,			type=A						,			default='test'						,			choices=['all', 'valid', 'test']						,			help='which split to evaluate')
							parser.add_argument('--batch_size'						,			type=A						,			default=10						,			help='batch size')
							parser.add_argument('--tgt_len'						,			type=A						,			default=128						,			help='number of tokens to predict')
							parser.add_argument('--ext_len'						,			type=A						,			default=0						,			help='length of the extended context')
							parser.add_argument('--mem_len'						,			type=A						,			default=1600						,			help='length of the retained previous heads')
							parser.add_argument('--clamp_len'						,			type=A						,			default=1000						,			help='max positional embedding index')
							parser.add_argument('--no_cuda'						,			action='store_true'						,			help='Do not use CUDA even though CUA is available')
							parser.add_argument('--work_dir'						,			type=A						,			required=A						,			help='path to the work_dir')
							parser.add_argument('--no_log'						,			action='store_true'						,			help='do not log the eval result')
							parser.add_argument('--same_length'						,			action='store_true'						,			help='set same length attention with masking')
							parser.add_argument('--server_ip'						,			type=A						,			default=''						,			help='Can be used for distant debugging.')
							parser.add_argument('--server_port'						,			type=A						,			default=''						,			help='Can be used for distant debugging.')
							UpperCamelCase					       =			parser.parse_args()
							assert args.ext_len >= 0, "extended context length must be non-negative"

							if args.server_ip and args.server_port:
														# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
														import ptvsd

														print('Waiting for debugger attach')
														ptvsd.enable_attach(address=(args.server_ip, args.server_port)						,			redirect_output=A)
														ptvsd.wait_for_attach()

							UpperCamelCase					       =			torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
							logger.info('device: {}'.format(A))

							# Load a pre-processed dataset
							# You can also build the corpus yourself using TransfoXLCorpus methods
							# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
							# and tokenizing the dataset
							# The pre-processed corpus is a convertion (using the conversion script )
							UpperCamelCase					       =			TransfoXLCorpus.from_pretrained(args.model_name)

							UpperCamelCase					       =			corpus.get_iterator('valid'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)
							UpperCamelCase					       =			corpus.get_iterator('test'						,			args.batch_size						,			args.tgt_len						,			device=A						,			ext_len=args.ext_len)

							# Load a pre-trained model
							UpperCamelCase					       =			TransfoXLLMHeadModel.from_pretrained(args.model_name)
							model.to(A)

							logger.info(
							    'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
							        args.batch_size						,			args.tgt_len						,			args.ext_len						,			args.mem_len						,			args.clamp_len))

							model.reset_memory_length(args.mem_len)
							if args.clamp_len > 0:
														UpperCamelCase					       =			args.clamp_len
							if args.same_length:
														UpperCamelCase					       =			True

							###############################################################################
							# Evaluation code
							###############################################################################
							def evaluate(A		:						Dict):
														# Turn on evaluation mode which disables dropout.
														model.eval()
														UpperCamelCase       ,     UpperCamelCase					       =			0, 0.0
														UpperCamelCase					       =			time.time()
														with torch.no_grad():
																					UpperCamelCase					       =			None
																					for idx, (data, target, seq_len) in enumerate(A):
																												UpperCamelCase					       =			model(A						,			lm_labels=A						,			mems=A)
																												UpperCamelCase       ,     UpperCamelCase       ,     UpperCamelCase					       =			ret
																												UpperCamelCase					       =			loss.mean()
																												total_loss += seq_len * loss.item()
																												total_len += seq_len
																					UpperCamelCase					       =			time.time() - start_time
														logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(A						,			1000 * total_time / (idx + 1)))
														return total_loss / total_len

							# Run on test data.
							if args.split == "all":
														UpperCamelCase					       =			evaluate(A)
														UpperCamelCase					       =			evaluate(A)
							elif args.split == "valid":
														UpperCamelCase					       =			evaluate(A)
														UpperCamelCase					       =			None
							elif args.split == "test":
														UpperCamelCase					       =			evaluate(A)
														UpperCamelCase					       =			None

							def format_log(A		:						Any						,			A		:						Optional[Any]):
														UpperCamelCase					       =			'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(A						,			A						,			math.exp(A))
														return log_str

							UpperCamelCase					       =			''
							if valid_loss is not None:
														log_str += format_log(A						,			'valid')
							if test_loss is not None:
														log_str += format_log(A						,			'test')

							logger.info('=' * 100)
							logger.info(A)
							logger.info('=' * 100)


if __name__ == "__main__":
						main()




