



import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


UpperCamelCase__			=    logging.get_logger(__name__)

if is_vision_available():
			import PIL.Image
			from PIL import Image
			from PIL.Image import Image as ImageType
else:
			UpperCamelCase__			=    object

if is_torch_available():
			import torch

if is_soundfile_availble():
			import soundfile as sf
class       _lowerCAmelCase :






			"""simple docstring"""





			def __init__(					self						:							Tuple      ,  lowercase						:							Optional[Any] )    ->       Union[str, Any]:
									"""simple docstring"""







									__lowercase											=					value





			def __str__(					self						:							Tuple )    ->       List[str]:
									"""simple docstring"""







									return self.to_string()





			def 		snake_case__      (					self						:							List[str] )    ->       Union[str, Any]:
									"""simple docstring"""







									logger.error(
									    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable""" )
									return self._value







			def 		snake_case__      (					self						:							Tuple )    ->       str:
									"""simple docstring"""







									logger.error(
									    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable""" )
									return str(self._value )





class       _lowerCAmelCase (			_UpperCAmelCase     ,       _UpperCAmelCase      ):






			"""simple docstring"""





			def 		snake_case__      (					self						:							List[str] )    ->       Dict:
									"""simple docstring"""







									return self._value







			def 		snake_case__      (					self						:							int )    ->       List[Any]:
									"""simple docstring"""







									return self._value





class       _lowerCAmelCase (			_UpperCAmelCase     ,       _UpperCAmelCase      ):






			"""simple docstring"""





			def __init__(					self						:							Dict      ,  lowercase						:							List[str] )    ->       Union[str, Any]:
									"""simple docstring"""







									super().__init__(lowercase )

									if not is_vision_available():
															raise ImportError("""PIL must be installed in order to handle images.""" )

									__lowercase											=					None
									__lowercase											=					None
									__lowercase											=					None

									if isinstance(lowercase      ,  lowercase ):
															__lowercase											=					value
									elif isinstance(lowercase      ,  (str, pathlib.Path) ):
															__lowercase											=					value
									elif isinstance(lowercase      ,  torch.Tensor ):
															__lowercase											=					value
									else:
															raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(lowercase )}" )





			def 		snake_case__      (					self						:							Union[str, Any]      ,  lowercase						:							List[Any]=None      ,  lowercase						:							Union[str, Any]=None )    ->       Union[str, Any]:
									"""simple docstring"""







									from IPython.display import Image, display

									display(Image(self.to_string() ) )





			def 		snake_case__      (					self						:							str )    ->       Optional[int]:
									"""simple docstring"""







									if self._raw is not None:
															return self._raw

									if self._path is not None:
															__lowercase											=					Image.open(self._path )
															return self._raw







			def 		snake_case__      (					self						:							List[Any] )    ->       List[str]:
									"""simple docstring"""







									if self._path is not None:
															return self._path

									if self._raw is not None:
															__lowercase											=					tempfile.mkdtemp()
															__lowercase											=					os.path.join(lowercase      ,  str(uuid.uuida() ) + """.png""" )
															self._raw.save(self._path )

															return self._path

									if self._tensor is not None:
															__lowercase											=					self._tensor.cpu().detach().numpy()

															# There is likely simpler than load into image into save
															__lowercase											=					Image.fromarray((array * 255).astype(np.uinta ) )

															__lowercase											=					tempfile.mkdtemp()
															__lowercase											=					os.path.join(lowercase      ,  str(uuid.uuida() ) + """.png""" )

															img.save(self._path )

															return self._path





class       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			def __init__(					self						:							Any      ,  lowercase						:							int      ,  lowercase						:							int=16_000 )    ->       int:
									"""simple docstring"""







									super().__init__(lowercase )

									if not is_soundfile_availble():
															raise ImportError("""soundfile must be installed in order to handle audio.""" )

									__lowercase											=					None
									__lowercase											=					None

									__lowercase											=					samplerate

									if isinstance(lowercase      ,  (str, pathlib.Path) ):
															__lowercase											=					value
									elif isinstance(lowercase      ,  torch.Tensor ):
															__lowercase											=					value
									else:
															raise ValueError(F"Unsupported audio type: {type(lowercase )}" )





			def 		snake_case__      (					self						:							Dict      ,  lowercase						:							str=None      ,  lowercase						:							Any=None )    ->       Optional[Any]:
									"""simple docstring"""







									from IPython.display import Audio, display

									display(Audio(self.to_string()      ,  rate=self.samplerate ) )





			def 		snake_case__      (					self						:							Optional[Any] )    ->       List[Any]:
									"""simple docstring"""







									if self._tensor is not None:
															return self._tensor

									if self._path is not None:
															__lowercase						,      __lowercase											=					sf.read(self._path )
															__lowercase											=					torch.tensor(lowercase )
															return self._tensor







			def 		snake_case__      (					self						:							Dict )    ->       int:
									"""simple docstring"""







									if self._path is not None:
															return self._path

									if self._tensor is not None:
															__lowercase											=					tempfile.mkdtemp()
															__lowercase											=					os.path.join(lowercase      ,  str(uuid.uuida() ) + """.wav""" )
															sf.write(self._path      ,  self._tensor      ,  samplerate=self.samplerate )
															return self._path





UpperCamelCase__			=    {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
UpperCamelCase__			=    {str: AgentText}

if is_vision_available():
			UpperCamelCase__			=    AgentImage






def 			UpperCAmelCase__     (     *lowercase__    ,							**lowercase__ )			->   Dict:
						__lowercase											=					[(arg.to_raw() if isinstance(lowercase__    ,							lowercase__ ) else arg) for arg in args]
						__lowercase											=					{k: (v.to_raw() if isinstance(lowercase__    ,							lowercase__ ) else v) for k, v in kwargs.items()}
						return args, kwargs







def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__=None )			->   Optional[int]:
						if isinstance(lowercase__    ,							lowercase__ ):
												__lowercase											=					{}
												for i, (k, v) in enumerate(outputs.items() ):
																		if output_types is not None:
																								# If the class has defined outputs, we can map directly according to the class definition
																								if output_types[i] in AGENT_TYPE_MAPPING:
																														__lowercase											=					AGENT_TYPE_MAPPING[output_types[i]](lowercase__ )
																								else:
																														__lowercase											=					AgentType(lowercase__ )

																		else:
																								# If the class does not have defined output, then we map according to the type
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(lowercase__    ,							_k ):
																																				__lowercase											=					_v(lowercase__ )
																								if k not in decoded_outputs:
																														__lowercase											=					AgentType[v]

						elif isinstance(lowercase__    ,							(list, tuple) ):
												__lowercase											=					type(lowercase__ )()
												for i, v in enumerate(lowercase__ ):
																		if output_types is not None:
																								# If the class has defined outputs, we can map directly according to the class definition
																								if output_types[i] in AGENT_TYPE_MAPPING:
																														decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](lowercase__ ) )
																								else:
																														decoded_outputs.append(AgentType(lowercase__ ) )
																		else:
																								# If the class does not have defined output, then we map according to the type
																								__lowercase											=					False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(lowercase__    ,							_k ):
																																				decoded_outputs.append(_v(lowercase__ ) )
																																				__lowercase											=					True

																								if not found:
																														decoded_outputs.append(AgentType(lowercase__ ) )

						else:
												if output_types[0] in AGENT_TYPE_MAPPING:
																		# If the class has defined outputs, we can map directly according to the class definition
																		__lowercase											=					AGENT_TYPE_MAPPING[output_types[0]](lowercase__ )

												else:
																		# If the class does not have defined output, then we map according to the type
																		for _k, _v in INSTANCE_TYPE_MAPPING.items():
																								if isinstance(lowercase__    ,							_k ):
																														return _v(lowercase__ )
																		return AgentType(lowercase__ )

						return decoded_outputs










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():
			UpperCamelCase__			=    True
			UpperCamelCase__			=    "p3dn.24xlarge"
			import smdistributed.dataparallel.tensorflow as sdp
else:
			UpperCamelCase__			=    False






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__=None )			->   str:
						__lowercase											=					tqdm(lowercase__ )
						for i, batch in enumerate(lowercase__ ):
												with tf.GradientTape() as tape:
																		__lowercase						,      __lowercase											=					batch
																		__lowercase											=					model(lowercase__ )
																		__lowercase											=					loss(lowercase__    ,							outputs.logits )

												if SDP_ENABLED:
																		__lowercase											=					sdp.DistributedGradientTape(lowercase__    ,							sparse_as_dense=lowercase__ )

												__lowercase											=					tape.gradient(lowercase__    ,							model.trainable_variables )
												opt.apply_gradients(zip(lowercase__    ,							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

						__lowercase											=					{"""loss""": loss_value.numpy()}
						return train_results






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__    ,							lowercase__ )			->   int:
						# Load dataset
						__lowercase						,      __lowercase											=					load_dataset("""imdb"""    ,							split=["""train""", """test"""] )

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

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

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

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

						if SDP_ENABLED:
												__lowercase											=					tf_train_dataset.shard(sdp.size()    ,							sdp.rank() )
												__lowercase											=					tf_test_dataset.shard(sdp.size()    ,							sdp.rank() )
						__lowercase											=					tf_train_dataset.batch(lowercase__    ,							drop_remainder=lowercase__ )
						__lowercase											=					tf_test_dataset.batch(lowercase__    ,							drop_remainder=lowercase__ )

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
			UpperCamelCase__			=    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"])

			UpperCamelCase__			, UpperCamelCase__			=    parser.parse_known_args()

			# Set up logging
			UpperCamelCase__			=    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()

						UpperCamelCase__			=    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
			UpperCamelCase__			=    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
			UpperCamelCase__			=    AutoTokenizer.from_pretrained(args.model_name_or_path)

			# get datasets
			UpperCamelCase__			, UpperCamelCase__			=    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
			UpperCamelCase__			=    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
			UpperCamelCase__			=    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
			UpperCamelCase__			=    [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)
						UpperCamelCase__			=    time.time()
						UpperCamelCase__			=    fit(
						    model,
						    loss,
						    optimizer,
						    tf_train_dataset,
						    args.epochs,
						    args.per_device_train_batch_size,
						    max_steps=args.max_steps,
						)
						UpperCamelCase__			=    time.time() - start_train_time
						logger.info("*** Train ***")
						logger.info(F"""train_runtime = {end_train_time}""")

						UpperCamelCase__			=    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):
						UpperCamelCase__			=    model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
						logger.info("*** Evaluate ***")

						UpperCamelCase__			=    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)





