






import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('SDP_ENABLED') or is_sagemaker_dp_enabled():
						__lowerCamelCase						= True
						__lowerCamelCase						= 'p3dn.24xlarge'
						import smdistributed.dataparallel.tensorflow as sdp
else:
						__lowerCamelCase						= False






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase=None     )     ->		str:







							"""simple docstring"""





							_a  :    int			=						tqdm(UpperCAmelCase     )
							for i, batch in enumerate(UpperCAmelCase     ):
														with tf.GradientTape() as tape:
																					_a		,					_a  :    Optional[Any]			=						batch
																					_a  :    str			=						model(UpperCAmelCase     )
																					_a  :    Optional[Any]			=						loss(UpperCAmelCase    ,	outputs.logits     )

														if SDP_ENABLED:
																					_a  :    List[Any]			=						sdp.DistributedGradientTape(UpperCAmelCase    ,	sparse_as_dense=UpperCAmelCase     )

														_a  :    Optional[int]			=						tape.gradient(UpperCAmelCase    ,	model.trainable_variables     )
														opt.apply_gradients(zip(UpperCAmelCase    ,	model.trainable_variables     )     )

														pbar.set_description(F'Loss: {loss_value:.4f}'     )

														if SDP_ENABLED and i == 0:
																					sdp.broadcast_variables(model.variables    ,	root_rank=0     )
																					sdp.broadcast_variables(opt.variables()    ,	root_rank=0     )

														if max_steps and i >= max_steps:
																					break

							_a  :    List[Any]			=						{'''loss''': loss_value.numpy()}
							return train_results






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		Optional[int]:







							"""simple docstring"""





							_a		,					_a  :    Tuple			=						load_dataset('''imdb'''    ,	split=['''train''', '''test''']     )

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

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

							# Preprocess test dataset
							_a  :    Tuple			=						test_dataset.map(
							    lambda UpperCAmelCase     : tokenizer(e['''text''']    ,	truncation=UpperCAmelCase    ,	padding='''max_length'''     )    ,	batched=UpperCAmelCase     )
							test_dataset.set_format(type='''tensorflow'''    ,	columns=['''input_ids''', '''attention_mask''', '''label''']     )

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

							if SDP_ENABLED:
														_a  :    Dict			=						tf_train_dataset.shard(sdp.size()    ,	sdp.rank()     )
														_a  :    Union[str, Any]			=						tf_test_dataset.shard(sdp.size()    ,	sdp.rank()     )
							_a  :    int			=						tf_train_dataset.batch(UpperCAmelCase    ,	drop_remainder=UpperCAmelCase     )
							_a  :    Tuple			=						tf_test_dataset.batch(UpperCAmelCase    ,	drop_remainder=UpperCAmelCase     )

							return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
						__lowerCamelCase						= argparse.ArgumentParser()

						# Hyperparameters sent by the client are passed as command-line arguments to the script.
						parser.add_argument('--epochs', type=int, default=3)
						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)
						parser.add_argument('--max_steps', type=int, default=None)

						# Data, model, and output directories
						parser.add_argument('--output_data_dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
						parser.add_argument('--model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
						parser.add_argument('--n_gpus', type=str, default=os.environ['SM_NUM_GPUS'])

						__lowerCamelCase						,  __lowerCamelCase						= parser.parse_known_args()

						# Set up logging
						__lowerCamelCase						= logging.getLogger(__name__)

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

						if SDP_ENABLED:
												sdp.init()

												__lowerCamelCase						= tf.config.experimental.list_physical_devices('GPU')
												for gpu in gpus:
																		tf.config.experimental.set_memory_growth(gpu, True)
												if gpus:
																		tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], 'GPU')

    # Load model and tokenizer
						__lowerCamelCase						= TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						__lowerCamelCase						= AutoTokenizer.from_pretrained(args.model_name_or_path)

						# get datasets
						__lowerCamelCase						,  __lowerCamelCase						= get_datasets(
						    tokenizer=tokenizer,
						    train_batch_size=args.per_device_train_batch_size,
						    eval_batch_size=args.per_device_eval_batch_size,
						)

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

						# Training
						if args.do_train:
												# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
												__lowerCamelCase						= time.time()
												__lowerCamelCase						= fit(
												    model,
												    loss,
												    optimizer,
												    tf_train_dataset,
												    args.epochs,
												    args.per_device_train_batch_size,
												    max_steps=args.max_steps,
												)
												__lowerCamelCase						= time.time() - start_train_time
												logger.info('*** Train ***')
												logger.info(F'''train_runtime = {end_train_time}''')

												__lowerCamelCase						= os.path.join(args.output_dir, 'train_results.txt')

												if not SDP_ENABLED or sdp.rank() == 0:
																		with open(output_eval_file, 'w') as writer:
																								logger.info('***** Train results *****')
																								logger.info(train_results)
																								for key, value in train_results.items():
																														logger.info(F'''  {key} = {value}''')
																														writer.write(F'''{key} = {value}\n''')

    # Evaluation
						if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
												__lowerCamelCase						= model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
												logger.info('*** Evaluate ***')

												__lowerCamelCase						= os.path.join(args.output_dir, 'eval_results.txt')

												with open(output_eval_file, 'w') as writer:
																		logger.info('***** Eval results *****')
																		logger.info(result)
																		for key, value in result.items():
																								logger.info(F'''  {key} = {value}''')
																								writer.write(F'''{key} = {value}\n''')

    # Save result
						if SDP_ENABLED:
												if sdp.rank() == 0:
																		model.save_pretrained(args.output_dir)
																		tokenizer.save_pretrained(args.output_dir)
						else:
												model.save_pretrained(args.output_dir)
												tokenizer.save_pretrained(args.output_dir)







import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__lowerCamelCase						= logging.get_logger(__name__)

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''semantic'''




						def __init__(    self		,	lowercase=10_000		,	lowercase=True		,	lowercase=768		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=10_048		,	lowercase=129_595		,	lowercase=129_599		,	lowercase=10_000		,	lowercase=256		,	lowercase=49.9		,	**lowercase		,	)			->						int:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	eos_token_id=lowercase		,	renormalize_logits=lowercase		,	max_new_tokens=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

													_a  :    Optional[int]			=						text_encoding_offset
													_a  :    str			=						text_pad_token
													_a  :    List[str]			=						eos_token_id
													_a  :    Tuple			=						semantic_infer_token
													_a  :    int			=						semantic_vocab_size
													_a  :    List[str]			=						max_input_semantic_length
													_a  :    Union[str, Any]			=						semantic_rate_hz

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''coarse_acoustics'''




						def __init__(    self		,	lowercase=True		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=12_048		,	lowercase=75		,	lowercase=2		,	lowercase=12_050		,	lowercase=256		,	lowercase = 630		,	lowercase = 60		,	**lowercase		,	)			->						List[Any]:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	renormalize_logits=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

													_a  :    str			=						coarse_semantic_pad_token
													_a  :    Any			=						coarse_rate_hz
													_a  :    Dict			=						n_coarse_codebooks
													_a  :    str			=						coarse_infer_token
													_a  :    List[Any]			=						max_coarse_input_length
													_a  :    Union[str, Any]			=						max_coarse_history
													_a  :    Any			=						sliding_window_len

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''fine_acoustics'''




						def __init__(    self		,	lowercase=0.5		,	lowercase=512		,	lowercase=1_024		,	lowercase=8		,	**lowercase		,	)			->						str:
													super().__init__(temperature=lowercase       )

													_a  :    Tuple			=						max_fine_history_length
													_a  :    Tuple			=						max_fine_input_length
													_a  :    Any			=						n_fine_codebooks

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''bark'''
						lowercase									=  True




						def __init__(    self		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase=24_000		,	lowercase=1_024		,	**lowercase		,	)			->						List[str]:
													if semantic_config is None:
																				_a  :    str			=						{}
																				logger.info('''semantic_config is None. initializing the semantic model with default values.'''       )

													if coarse_acoustics_config is None:
																				_a  :    Any			=						{}
																				logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''       )

													if fine_acoustics_config is None:
																				_a  :    int			=						{}
																				logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''       )

													_a  :    List[Any]			=						BarkSemanticGenerationConfig(**lowercase       )
													_a  :    Union[str, Any]			=						BarkCoarseGenerationConfig(**lowercase       )
													_a  :    str			=						BarkFineGenerationConfig(**lowercase       )

													_a  :    List[str]			=						sample_rate
													_a  :    str			=						codebook_size




						@classmethod
						def      snake_case__(    cls		,	lowercase		,	lowercase		,	lowercase		,	**lowercase		,	)			->						List[Any]:
													return cls(
													    semantic_config=semantic_config.to_dict()		,	coarse_acoustics_config=coarse_acoustics_config.to_dict()		,	fine_acoustics_config=fine_acoustics_config.to_dict()		,	**lowercase		,	)







						def      snake_case__(    self       )			->						List[str]:
													_a  :    Any			=						copy.deepcopy(self.__dict__       )

													_a  :    str			=						self.semantic_config.to_dict()
													_a  :    Any			=						self.coarse_acoustics_config.to_dict()
													_a  :    Union[str, Any]			=						self.fine_acoustics_config.to_dict()

													_a  :    List[str]			=						self.__class__.model_type
													return output