






"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


A_			: Optional[int]				=			[nn.LayerNorm]

A_			: int				=			logging.get_logger(__name__)

A_			: Optional[int]				=			version.parse(version.parse(torch.__version__).base_version)

A_			: int				=			parsed_torch_version_base >= version.parse("2.0")
A_			: Dict				=			parsed_torch_version_base >= version.parse("1.12")
A_			: List[Any]				=			parsed_torch_version_base >= version.parse("1.11")
A_			: int				=			parsed_torch_version_base < version.parse("1.11")






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   self				):
 from torch import _softmax_backward_data

 if is_torch_less_than_1_11:
  return _softmax_backward_data(_lowerCamelCase			,   _lowerCamelCase			,   parent.dim			,   self				)
 else:
  return _softmax_backward_data(_lowerCamelCase			,   _lowerCamelCase			,   parent.dim			,   self.dtype				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase = 0				):
 lowerCamelCase__     :			Tuple     				=						index.to(layer.weight.device				)
 lowerCamelCase__     :			int     				=						layer.weight.index_select(_lowerCamelCase			,   _lowerCamelCase				).clone().detach()
 if layer.bias is not None:
  if dim == 1:
   lowerCamelCase__     :			Union[str, Any]     				=						layer.bias.clone().detach()
  else:
   lowerCamelCase__     :			List[Any]     				=						layer.bias[index].clone().detach()
 lowerCamelCase__     :			Tuple     				=						list(layer.weight.size()				)
 lowerCamelCase__     :			Union[str, Any]     				=						len(_lowerCamelCase				)
 lowerCamelCase__     :			Tuple     				=						nn.Linear(new_size[1]			,   new_size[0]			,   bias=layer.bias is not None				).to(layer.weight.device				)
 lowerCamelCase__     :			str     				=						False
 new_layer.weight.copy_(W.contiguous()				)
 lowerCamelCase__     :			Union[str, Any]     				=						True
 if layer.bias is not None:
  lowerCamelCase__     :			Any     				=						False
  new_layer.bias.copy_(b.contiguous()				)
  lowerCamelCase__     :			Any     				=						True
 return new_layer



class 		a_      (	nn.Module ):







      '''simple docstring'''

      def __init__(self,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       super().__init__()
       lowerCamelCase__     :			Tuple     				=						nf
       lowerCamelCase__     :			Any     				=						nn.Parameter(torch.empty(lowerCamelCase_,     lowerCamelCase_					)					)
       lowerCamelCase__     :			Dict     				=						nn.Parameter(torch.zeros(lowerCamelCase_					)					)
       nn.init.normal_(self.weight,     std=0.02					)





      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						x.size()[:-1] + (self.nf,)
       lowerCamelCase__     :			Optional[Any]     				=						torch.addmm(self.bias,     x.view(-1,     x.size(-1					)					),     self.weight					)
       lowerCamelCase__     :			Dict     				=						x.view(lowerCamelCase_					)
       return x






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase = 1				):
 lowerCamelCase__     :			Optional[int]     				=						index.to(layer.weight.device				)
 lowerCamelCase__     :			List[str]     				=						layer.weight.index_select(_lowerCamelCase			,   _lowerCamelCase				).clone().detach()
 if dim == 0:
  lowerCamelCase__     :			Optional[int]     				=						layer.bias.clone().detach()
 else:
  lowerCamelCase__     :			int     				=						layer.bias[index].clone().detach()
 lowerCamelCase__     :			Tuple     				=						list(layer.weight.size()				)
 lowerCamelCase__     :			Optional[Any]     				=						len(_lowerCamelCase				)
 lowerCamelCase__     :			Dict     				=						ConvaD(new_size[1]			,   new_size[0]				).to(layer.weight.device				)
 lowerCamelCase__     :			List[Any]     				=						False
 new_layer.weight.copy_(W.contiguous()				)
 lowerCamelCase__     :			Dict     				=						True
 lowerCamelCase__     :			Union[str, Any]     				=						False
 new_layer.bias.copy_(b.contiguous()				)
 lowerCamelCase__     :			Optional[int]     				=						True
 return new_layer






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase = None				):
 if isinstance(_lowerCamelCase			,   nn.Linear				):
  return prune_linear_layer(_lowerCamelCase			,   _lowerCamelCase			,   dim=0 if dim is None else dim				)
 elif isinstance(_lowerCamelCase			,   _lowerCamelCase				):
  return prune_convad_layer(_lowerCamelCase			,   _lowerCamelCase			,   dim=1 if dim is None else dim				)
 else:
  raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   *_lowerCamelCase				):
 assert len(_lowerCamelCase				) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

 # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
 lowerCamelCase__     :			Any     				=						len(inspect.signature(_lowerCamelCase				).parameters				)
 if num_args_in_forward_chunk_fn != len(_lowerCamelCase				):
  raise ValueError(
      f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_lowerCamelCase				)} input '''
      'tensors are given'				)

 if chunk_size > 0:
  lowerCamelCase__     :			Any     				=						input_tensors[0].shape[chunk_dim]
  for input_tensor in input_tensors:
   if input_tensor.shape[chunk_dim] != tensor_shape:
    raise ValueError(
        f'''All input tenors have to be of the same shape: {tensor_shape}, '''
        f'''found shape {input_tensor.shape[chunk_dim]}'''				)

  if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
   raise ValueError(
       f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
       f'''size {chunk_size}'''				)

  lowerCamelCase__     :			Union[str, Any]     				=						input_tensors[0].shape[chunk_dim] // chunk_size

  # chunk input tensor into tuples
  lowerCamelCase__     :			Optional[Any]     				=						tuple(input_tensor.chunk(_lowerCamelCase			,   dim=_lowerCamelCase				) for input_tensor in input_tensors				)
  # apply forward fn to every tuple
  lowerCamelCase__     :			Optional[int]     				=						tuple(forward_fn(*_lowerCamelCase				) for input_tensors_chunk in zip(*_lowerCamelCase				)				)
  # concatenate output at same dimension
  return torch.cat(_lowerCamelCase			,   dim=_lowerCamelCase				)

 return forward_fn(*_lowerCamelCase				)






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Optional[Any]     				=						torch.ones(_lowerCamelCase			,   _lowerCamelCase				)
 lowerCamelCase__     :			Optional[Any]     				=						set(_lowerCamelCase				) - already_pruned_heads  # Convert to set and remove already pruned heads
 for head in heads:
  # Compute how many pruned heads are before the head and move the index accordingly
  lowerCamelCase__     :			Dict     				=						head - sum(1 if h < head else 0 for h in already_pruned_heads				)
  lowerCamelCase__     :			Optional[int]     				=						0
 lowerCamelCase__     :			Dict     				=						mask.view(-1				).contiguous().eq(1				)
 lowerCamelCase__     :			torch.LongTensor     				=						torch.arange(len(_lowerCamelCase				)				)[mask].long()
 return heads, index






def        lowerCamelCase_  (						*_lowerCamelCase			,   _lowerCamelCase = None				):
 return torch.meshgrid(*_lowerCamelCase			,   indexing=_lowerCamelCase				)






def        lowerCamelCase_  (						_lowerCamelCase				):
 return tensor.device, storage_ptr(_lowerCamelCase				), storage_size(_lowerCamelCase				)






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



def        __a     (    lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					Union[str, Any]			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					int=None				)      -> Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=   tqdm(lowerCAmelCase_				)
						for i, batch in enumerate(lowerCAmelCase_				):
												with tf.GradientTape() as tape:
																		UpperCAmelCase_,   UpperCAmelCase_=   batch
																		UpperCAmelCase_=   model(lowerCAmelCase_				)
																		UpperCAmelCase_=   loss(lowerCAmelCase_			,outputs.logits				)

												if SDP_ENABLED:
																		UpperCAmelCase_=   sdp.DistributedGradientTape(lowerCAmelCase_			,sparse_as_dense=lowerCAmelCase_				)

												UpperCAmelCase_=   tape.gradient(lowerCAmelCase_			,model.trainable_variables				)
												opt.apply_gradients(zip(lowerCAmelCase_			,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

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



def        __a     (    lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					str				)      -> str:






						'''simple docstring'''



						UpperCAmelCase_,   UpperCAmelCase_=   load_dataset("""imdb"""			,split=["""train""", """test"""]				)

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

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

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

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

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

						return tf_train_dataset, tf_test_dataset


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=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'''])

	__A    ,			__A				    =						parser.parse_known_args()

	# 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''',
	)

	if SDP_ENABLED:
		sdp.init()

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

	# get datasets
	__A    ,			__A				    =						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
	__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)

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

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

		__A				    =						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)




