import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.framework.errors_impl import ResourceExhaustedError

    from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
    import pyanvml.pyanvml as nvml

_lowercase							:      Union[str, Any]      							=logging.get_logger(__name__)



def A__  (			lowercase:     List[str],		lowercase:     int					)				->		Optional[int]:
       def run_func(lowercase:     Optional[int]					):
              @wraps(__A					)
              def run_in_eager_mode(*lowercase:     Union[str, Any],		**lowercase:     Optional[int]					):
                     return func(*__A,		**__A					)

              @wraps(__A					)
              @tf.function(experimental_compile=__A					)
              def run_in_graph_mode(*lowercase:     Union[str, Any],		**lowercase:     List[Any]					):
                     return func(*__A,		**__A					)

              if do_eager_mode is True:
                     if use_xla is not False:
                            raise ValueError(
                                'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'					)
                     return run_in_eager_mode
              else:
                     return run_in_graph_mode

       return run_func



def A__  (			lowercase:     Any,		lowercase:     Any,		lowercase:     Any					)				->		Optional[Any]:
       A						:     Union[str, Any]         =random.Random()
       A						:     Any         =[rng.randint(0,		vocab_size - 1					) for i in range(batch_size * sequence_length					)]
       return tf.constant(__A,		shape=(batch_size, sequence_length),		dtype=tf.intaa					)



class  SCREAMING_SNAKE_CASE_ (					__lowerCAmelCase			):
  '''simple docstring'''


  lowercase		:     int            =				42
  lowercase		:     int            =				42
  lowercase		:     List[str]            =				"TensorFlow"







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				List[str]:
         return tf.__version__







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				float:
         A						:     List[str]         =self.args.strategy
         if strategy is None:
                raise ValueError('A device strategy has to be initialized before using TensorFlow.'     )
         A						:     Optional[Any]         =self._prepare_inference_func(_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase     )
         return self._measure_speed(_inference     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				float:
         A						:     List[Any]         =self.args.strategy
         if strategy is None:
                raise ValueError('A device strategy has to be initialized before using TensorFlow.'     )
         A						:     str         =self._prepare_train_func(_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase     )
         return self._measure_speed(_train     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				[Memory, Optional[MemorySummary]]:


         if self.args.is_gpu:
                tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]      ,	_UpperCamelCase     )
         A						:     Optional[Any]         =self.args.strategy
         if strategy is None:
                raise ValueError('A device strategy has to be initialized before using TensorFlow.'     )
         A						:     List[Any]         =self._prepare_inference_func(_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase     )
         return self._measure_memory(_inference     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				[Memory, Optional[MemorySummary]]:


         if self.args.is_gpu:
                tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]      ,	_UpperCamelCase     )
         A						:     Optional[int]         =self.args.strategy
         if strategy is None:
                raise ValueError('A device strategy has to be initialized before using TensorFlow.'     )

         A						:     Optional[int]         =self._prepare_train_func(_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase     )
         return self._measure_memory(_train     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Callable[[], None]:
         A						:     Tuple         =self.config_dict[model_name]

         if self.args.fpaa:
                raise NotImplementedError('Mixed precision is currently not supported.'     )

         A						:     Dict         =(
             hasattr(_UpperCamelCase      ,	'architectures'     )
             and isinstance(config.architectures      ,	_UpperCamelCase     )
             and len(config.architectures     ) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
                try:
                       A						:     Dict         ="""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
                       A						:     List[Any]         =__import__('transformers'      ,	fromlist=[model_class]     )
                       A						:     int         =getattr(_UpperCamelCase      ,	_UpperCamelCase     )
                       A						:     int         =model_cls(_UpperCamelCase     )
                except ImportError:
                       raise ImportError(
                           f'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                           ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'     )
         else:
                A						:     List[Any]         =TF_MODEL_MAPPING[config.__class__](_UpperCamelCase     )

         # encoder-decoder has vocab size saved differently
         A						:     int         =config.vocab_size if hasattr(_UpperCamelCase      ,	'vocab_size'     ) else config.encoder.vocab_size
         A						:     List[Any]         =random_input_ids(_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase     )

         @run_with_tf_optimizations(self.args.eager_mode      ,	self.args.use_xla     )
         def encoder_decoder_forward():
                return model(_UpperCamelCase      ,	decoder_input_ids=_UpperCamelCase      ,	training=_UpperCamelCase     )

         @run_with_tf_optimizations(self.args.eager_mode      ,	self.args.use_xla     )
         def encoder_forward():
                return model(_UpperCamelCase      ,	training=_UpperCamelCase     )

         A						:     Union[str, Any]         =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

         return _inference







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Callable[[], None]:
         A						:     Any         =self.config_dict[model_name]

         if self.args.eager_mode is not False:
                raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'     )

         if self.args.fpaa:
                raise NotImplementedError('Mixed precision is currently not supported.'     )

         A						:     List[str]         =(
             hasattr(_UpperCamelCase      ,	'architectures'     )
             and isinstance(config.architectures      ,	_UpperCamelCase     )
             and len(config.architectures     ) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
                try:
                       A						:     Union[str, Any]         ="""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
                       A						:     str         =__import__('transformers'      ,	fromlist=[model_class]     )
                       A						:     int         =getattr(_UpperCamelCase      ,	_UpperCamelCase     )
                       A						:     Optional[int]         =model_cls(_UpperCamelCase     )
                except ImportError:
                       raise ImportError(
                           f'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                           ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'     )
         else:
                A						:     List[str]         =TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_UpperCamelCase     )

         # encoder-decoder has vocab size saved differently
         A						:     int         =config.vocab_size if hasattr(_UpperCamelCase      ,	'vocab_size'     ) else config.encoder.vocab_size
         A						:     Union[str, Any]         =random_input_ids(_UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase     )

         @run_with_tf_optimizations(self.args.eager_mode      ,	self.args.use_xla     )
         def encoder_decoder_train():
                A						:     Dict         =model(_UpperCamelCase      ,	decoder_input_ids=_UpperCamelCase      ,	labels=_UpperCamelCase      ,	training=_UpperCamelCase     )[0]
                A						:     int         =tf.gradients(_UpperCamelCase      ,	model.trainable_variables     )
                return gradients

         @run_with_tf_optimizations(self.args.eager_mode      ,	self.args.use_xla     )
         def encoder_train():
                A						:     Dict         =model(_UpperCamelCase      ,	labels=_UpperCamelCase      ,	training=_UpperCamelCase     )[0]
                A						:     Any         =tf.gradients(_UpperCamelCase      ,	model.trainable_variables     )
                return gradients

         A						:     List[str]         =encoder_decoder_train if config.is_encoder_decoder else encoder_train

         return _train







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				float:


         with self.args.strategy.scope():
                try:
                       if self.args.is_tpu or self.args.use_xla:
                              # run additional 10 times to stabilize compilation for tpu
                              logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'     )
                              timeit.repeat(_UpperCamelCase      ,	repeat=1      ,	number=5     )

                       # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                       A						:     Tuple         =timeit.repeat(
                           _UpperCamelCase      ,	repeat=self.args.repeat      ,	number=10      ,	)

                       return min(_UpperCamelCase     ) / 10.0
                except ResourceExhaustedError as e:
                       self.print_fn(f'Doesn\'t fit on GPU. {e}'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Callable[[], None]     )    ->				[Memory, MemorySummary]:
         logger.info(
             'Note that TensorFlow allocates more memory than '
             'it might need to speed up computation. '
             'The memory reported here corresponds to the memory '
             'reported by `nvidia-smi`, which can vary depending '
             'on total available memory on the GPU that is used.'     )
         with self.args.strategy.scope():
                try:
                       if self.args.trace_memory_line_by_line:
                              if not self.args.eager_mode:
                                     raise ValueError(
                                         '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
                                         ' consumption line by line.'     )
                              A						:     List[str]         =start_memory_tracing('transformers'     )

                       if self.args.is_tpu:
                              # tpu
                              raise NotImplementedError(
                                  'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
                                  ' with `args.memory=False`'     )
                       elif self.args.is_gpu:
                              # gpu
                              if not is_pyanvml_available():
                                     logger.warning(
                                         'py3nvml not installed, we won\'t log GPU memory usage. '
                                         'Install py3nvml (pip install py3nvml) to log information about GPU.'     )
                                     A						:     Optional[Any]         ="""N/A"""
                              else:
                                     logger.info(
                                         'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
                                         ' running on the same GPU.'     )
                                     # init nvml
                                     nvml.nvmlInit()
                                     func()
                                     A						:     Optional[int]         =nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx     )
                                     A						:     Optional[Any]         =nvml.nvmlDeviceGetMemoryInfo(_UpperCamelCase     )
                                     A						:     int         =meminfo.used
                                     A						:     List[Any]         =Memory(_UpperCamelCase     )
                                     # shutdown nvml
                                     nvml.nvmlShutdown()
                       else:
                              # cpu
                              if self.args.trace_memory_line_by_line:
                                     logger.info(
                                         'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
                                         ' TensorFlow.'     )
                                     A						:     str         =None
                              else:
                                     A						:     Union[str, Any]         =measure_peak_memory_cpu(_UpperCamelCase     )
                                     A						:     List[Any]         =Memory(_UpperCamelCase     ) if isinstance(_UpperCamelCase      ,	_UpperCamelCase     ) else memory_bytes
                       if self.args.trace_memory_line_by_line:
                              A						:     Any         =stop_memory_tracing(_UpperCamelCase     )
                              if memory is None:
                                     A						:     Union[str, Any]         =summary.total
                       else:
                              A						:     List[str]         =None

                       return memory, summary
                except ResourceExhaustedError as e:
                       self.print_fn(f'Doesn\'t fit on GPU. {e}'     )
                       return "N/A", None







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase							:      List[Any]      							=1_6
_lowercase							:      Union[str, Any]      							=3_2



def A__  (			lowercase:     Accelerator,		lowercase:     int = 16,		lowercase:     str = "bert-base-cased"					)				->		Optional[int]:
       A						:     List[Any]         =AutoTokenizer.from_pretrained(lowercase					)
       A						:     Any         =load_dataset('glue',		'mrpc'					)

       def tokenize_function(lowercase:     Any					):
              # max_length=None => use the model max length (it's actually the default)
              A						:     List[str]         =tokenizer(examples['sentence1'],		examples['sentence2'],		truncation=lowercase,		max_length=lowercase					)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       A						:     Any         =datasets.map(
           lowercase,		batched=lowercase,		remove_columns=['idx', 'sentence1', 'sentence2'],		load_from_cache_file=lowercase					)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       A						:     Dict         =tokenized_datasets.rename_column('label',		'labels'					)

       def collate_fn(lowercase:     Optional[int]					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(lowercase,		padding='max_length',		max_length=128,		return_tensors='pt'					)
              return tokenizer.pad(lowercase,		padding='longest',		return_tensors='pt'					)

       # Instantiate dataloaders.
       A						:     Union[str, Any]         =DataLoader(
           tokenized_datasets['train'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)
       A						:     str         =DataLoader(
           tokenized_datasets['validation'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)

       return train_dataloader, eval_dataloader



def A__  (			lowercase:     Dict,		lowercase:     Optional[int],		lowercase:     Any,		lowercase:     str					)				->		Tuple:
       model.eval()
       A						:     Tuple         =0
       for step, batch in enumerate(lowercase					):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device					)
              with torch.no_grad():
                     A						:     Tuple         =model(**lowercase					)
              A						:     Tuple         =outputs.logits.argmax(dim=-1					)
              # It is slightly faster to call this once, than multiple times
              A  ,  A						:     Union[str, Any]         =accelerator.gather(
                  (predictions, batch['labels'])					)  # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.use_distributed:
                     if step == len(lowercase					) - 1:
                            A						:     List[Any]         =predictions[: len(eval_dataloader.dataset					) - samples_seen]
                            A						:     Optional[int]         =references[: len(eval_dataloader.dataset					) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=lowercase,		references=lowercase,		)

       A						:     Union[str, Any]         =metric.compute()
       return eval_metric["accuracy"]



def A__  (			lowercase:     Union[str, Any],		lowercase:     Dict					)				->		List[str]:
       # Initialize accelerator
       A						:     Optional[int]         =Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A						:     int         =config['lr']
       A						:     Optional[Any]         =int(config['num_epochs']					)
       A						:     Union[str, Any]         =int(config['seed']					)
       A						:     List[str]         =int(config['batch_size']					)
       A						:     Optional[Any]         =args.model_name_or_path

       set_seed(lowercase					)
       A  ,  A						:     str         =get_dataloaders(lowercase,		lowercase,		lowercase					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A						:     List[str]         =AutoModelForSequenceClassification.from_pretrained(lowercase,		return_dict=lowercase					)

       # Instantiate optimizer
       A						:     Any         =(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       A						:     List[str]         =optimizer_cls(params=model.parameters(),		lr=lowercase					)

       if accelerator.state.deepspeed_plugin is not None:
              A						:     Optional[int]         =accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              A						:     Dict         =1
       A						:     Union[str, Any]         =(len(lowercase					) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A						:     List[Any]         =get_linear_schedule_with_warmup(
                  optimizer=lowercase,		num_warmup_steps=0,		num_training_steps=lowercase,		)
       else:
              A						:     List[str]         =DummyScheduler(lowercase,		total_num_steps=lowercase,		warmup_num_steps=0					)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       A  ,  A  ,  A  ,  A  ,  A						:     Optional[int]         =accelerator.prepare(
           lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)

       # We need to keep track of how many total steps we have iterated over
       A						:     Tuple         =0
       # We also need to keep track of the stating epoch so files are named properly
       A						:     List[str]         =0
       A						:     Tuple         =evaluate.load('glue',		'mrpc'					)
       A						:     Optional[int]         =num_epochs

       if args.partial_train_epoch is not None:
              A						:     Dict         =args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint					)
              A						:     List[Any]         =args.resume_from_checkpoint.split('epoch_'					)[1]
              A						:     List[Any]         =''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              A						:     Union[str, Any]         =int(lowercase					) + 1
              A						:     List[str]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              accelerator.print('resumed checkpoint performance:',		lowercase					)
              accelerator.print('resumed checkpoint\'s scheduler\'s lr:',		lr_scheduler.get_lr()[0]					)
              accelerator.print('resumed optimizers\'s lr:',		optimizer.param_groups[0]['lr']					)
              with open(os.path.join(args.output_dir,		F'state_{starting_epoch-1}.json'					),		'r'					) as f:
                     A						:     Union[str, Any]         =json.load(lowercase					)
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       A						:     str         ={}
       for epoch in range(lowercase,		lowercase					):
              model.train()
              for step, batch in enumerate(lowercase					):
                     A						:     Tuple         =model(**lowercase					)
                     A						:     List[Any]         =outputs.loss
                     A						:     Any         =loss / gradient_accumulation_steps
                     accelerator.backward(lowercase					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              A						:     Union[str, Any]         =F'epoch_{epoch}'
              A						:     Optional[Any]         =os.path.join(args.output_dir,		lowercase					)
              accelerator.save_state(lowercase					)
              A						:     Optional[Any]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              A						:     Dict         =accuracy
              A						:     Optional[Any]         =lr_scheduler.get_lr()[0]
              A						:     Any         =optimizer.param_groups[0]['lr']
              A						:     str         =epoch
              A						:     Dict         =overall_step
              accelerator.print(F'epoch {epoch}:',		lowercase					)

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir,		F'state_{epoch}.json'					),		'w'					) as f:
                            json.dump(lowercase,		lowercase					)



def A__  (			)				->		Optional[int]:
       A						:     Optional[int]         =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'					)
       parser.add_argument(
           '--model_name_or_path',		type=lowercase,		default='bert-base-cased',		help='Path to pretrained model or model identifier from huggingface.co/models.',		required=lowercase,		)
       parser.add_argument(
           '--output_dir',		type=lowercase,		default='.',		help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',		)
       parser.add_argument(
           '--resume_from_checkpoint',		type=lowercase,		default=lowercase,		help='If the training should continue from a checkpoint folder.',		)
       parser.add_argument(
           '--partial_train_epoch',		type=lowercase,		default=lowercase,		help='If passed, the training will stop after this number of epochs.',		)
       parser.add_argument(
           '--num_epochs',		type=lowercase,		default=2,		help='Number of train epochs.',		)
       A						:     str         =parser.parse_args()
       A						:     Optional[int]         ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

       training_function(lowercase,		lowercase					)


if __name__ == "__main__":
    main()






