






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
							    FlaxRobertaPreLayerNormForCausalLM,
							    FlaxRobertaPreLayerNormForMaskedLM,
							    FlaxRobertaPreLayerNormForMultipleChoice,
							    FlaxRobertaPreLayerNormForQuestionAnswering,
							    FlaxRobertaPreLayerNormForSequenceClassification,
							    FlaxRobertaPreLayerNormForTokenClassification,
							    FlaxRobertaPreLayerNormModel,
							)




class 				__A     (			unittest.TestCase ):





				"""simple docstring"""







				def __init__( self							,  lowerCamelCase__							,  lowerCamelCase__=13							,  lowerCamelCase__=7							,  lowerCamelCase__=True							,  lowerCamelCase__=True							,  lowerCamelCase__=True							,  lowerCamelCase__=True							,  lowerCamelCase__=99							,  lowerCamelCase__=32							,  lowerCamelCase__=5							,  lowerCamelCase__=4							,  lowerCamelCase__=37							,  lowerCamelCase__="gelu"							,  lowerCamelCase__=0.1							,  lowerCamelCase__=0.1							,  lowerCamelCase__=512							,  lowerCamelCase__=16							,  lowerCamelCase__=2							,  lowerCamelCase__=0.02							,  lowerCamelCase__=4							,  ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=parent
									__UpperCamelCase      :					int       		=batch_size
									__UpperCamelCase      :					List[str]       		=seq_length
									__UpperCamelCase      :					List[str]       		=is_training
									__UpperCamelCase      :					str       		=use_attention_mask
									__UpperCamelCase      :					Optional[int]       		=use_token_type_ids
									__UpperCamelCase      :					Any       		=use_labels
									__UpperCamelCase      :					Optional[int]       		=vocab_size
									__UpperCamelCase      :					Optional[Any]       		=hidden_size
									__UpperCamelCase      :					int       		=num_hidden_layers
									__UpperCamelCase      :					Any       		=num_attention_heads
									__UpperCamelCase      :					str       		=intermediate_size
									__UpperCamelCase      :					Dict       		=hidden_act
									__UpperCamelCase      :					int       		=hidden_dropout_prob
									__UpperCamelCase      :					Any       		=attention_probs_dropout_prob
									__UpperCamelCase      :					Any       		=max_position_embeddings
									__UpperCamelCase      :					Tuple       		=type_vocab_size
									__UpperCamelCase      :					Union[str, Any]       		=type_sequence_label_size
									__UpperCamelCase      :					Any       		=initializer_range
									__UpperCamelCase      :					Union[str, Any]       		=num_choices


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size    )

									__UpperCamelCase      :					Dict       		=None
									if self.use_attention_mask:
														__UpperCamelCase      :					Optional[int]       		=random_attention_mask([self.batch_size, self.seq_length]    )

									__UpperCamelCase      :					List[str]       		=None
									if self.use_token_type_ids:
														__UpperCamelCase      :					List[str]       		=ids_tensor([self.batch_size, self.seq_length]							,  self.type_vocab_size    )

									__UpperCamelCase      :					Optional[Any]       		=RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size							,  hidden_size=self.hidden_size							,  num_hidden_layers=self.num_hidden_layers							,  num_attention_heads=self.num_attention_heads							,  intermediate_size=self.intermediate_size							,  hidden_act=self.hidden_act							,  hidden_dropout_prob=self.hidden_dropout_prob							,  attention_probs_dropout_prob=self.attention_probs_dropout_prob							,  max_position_embeddings=self.max_position_embeddings							,  type_vocab_size=self.type_vocab_size							,  is_decoder=lowerCamelCase__							,  initializer_range=self.initializer_range							,  )

									return config, input_ids, token_type_ids, attention_mask


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		=self.prepare_config_and_inputs()
									__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Tuple       		=config_and_inputs
									__UpperCamelCase      :					List[Any]       		={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
									return config, inputs_dict







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=self.prepare_config_and_inputs()
									__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Optional[Any]       		=config_and_inputs

									__UpperCamelCase      :					int       		=True
									__UpperCamelCase      :					str       		=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									__UpperCamelCase      :					Union[str, Any]       		=ids_tensor([self.batch_size, self.seq_length]							,  vocab_size=2    )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 				__A     (			a      ,     unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      List[Any]			 =True

				UpperCamelCase__ :      Optional[int]			 =(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=FlaxRobertaPreLayerNormModelTester(self    )







				@slow
				def       __lowercase							( self    ):




									"""simple docstring"""

									for model_class_name in self.all_model_classes:
														__UpperCamelCase      :					Optional[int]       		=model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'							,  from_pt=lowerCamelCase__    )
														__UpperCamelCase      :					str       		=model(np.ones((1, 1)    )    )
														self.assertIsNotNone(lowerCamelCase__    )




@require_flax
class 				__A     (			unittest.TestCase ):





				"""simple docstring"""







				@slow
				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'							,  from_pt=lowerCamelCase__    )

									__UpperCamelCase      :					Tuple       		=np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							,  dtype=jnp.intaa    )
									__UpperCamelCase      :					Dict       		=model(lowerCamelCase__    )[0]
									__UpperCamelCase      :					Optional[Any]       		=[1, 11, 50265]
									self.assertEqual(list(output.shape    )							,  lowerCamelCase__    )
									# compare the actual values for a slice.
									__UpperCamelCase      :					Union[str, Any]       		=np.array(
									    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]							,  dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]							,  lowerCamelCase__							,  atol=1E-4    )    )







				@slow
				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		=FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'							,  from_pt=lowerCamelCase__    )

									__UpperCamelCase      :					int       		=np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							,  dtype=jnp.intaa    )
									__UpperCamelCase      :					List[str]       		=model(lowerCamelCase__    )[0]
									# compare the actual values for a slice.
									__UpperCamelCase      :					Dict       		=np.array(
									    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]							,  dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]							,  lowerCamelCase__							,  atol=1E-4    )    )


'''simple docstring'''

import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__a: Optional[Any]          =   16
__a: Any          =   32
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = 16     ):
 lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 lowercase__				:    Optional[int]  =					load_dataset('''glue'''		,     '''mrpc'''     )

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

 # Apply the method we just defined to all the examples in all the splits of the dataset
 # starting with the main process first:
 with accelerator.main_process_first():
  lowercase__				:    List[Any]  =					datasets.map(
      UpperCAmelCase		,     batched=UpperCAmelCase		,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,     )

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

 def collate_fn(UpperCAmelCase     ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  lowercase__				:    List[str]  =					128 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   lowercase__				:    List[str]  =					16
  elif accelerator.mixed_precision != "no":
   lowercase__				:    Dict  =					8
  else:
   lowercase__				:    Optional[int]  =					None

  return tokenizer.pad(
      UpperCAmelCase		,     padding='''longest'''		,     max_length=UpperCAmelCase		,     pad_to_multiple_of=UpperCAmelCase		,     return_tensors='''pt'''		,     )

 # Instantiate dataloaders.
 lowercase__				:    str  =					DataLoader(
     tokenized_datasets['''train''']		,     shuffle=UpperCAmelCase		,     collate_fn=UpperCAmelCase		,     batch_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					DataLoader(
     tokenized_datasets['''validation''']		,     shuffle=UpperCAmelCase		,     collate_fn=UpperCAmelCase		,     batch_size=UpperCAmelCase     )

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  __a: Tuple          =   mocked_dataloaders  # noqa: F811
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,     UpperCAmelCase     ) == "1":
  lowercase__				:    Optional[int]  =					2
 # Initialize Accelerator

 # New Code #
 # We pass in "all" to `log_with` to grab all available trackers in the environment
 # Note: If using a custom `Tracker` class, should be passed in here such as:
 # >>> log_with = ["all", MyCustomTrackerClassInstance()]
 if args.with_tracking:
  lowercase__				:    Union[str, Any]  =					Accelerator(
      cpu=args.cpu		,     mixed_precision=args.mixed_precision		,     log_with='''all'''		,     project_dir=args.project_dir     )
 else:
  lowercase__				:    Any  =					Accelerator(cpu=args.cpu		,     mixed_precision=args.mixed_precision     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowercase__				:    int  =					config['''lr''']
 lowercase__				:    Optional[int]  =					int(config['''num_epochs''']     )
 lowercase__				:    Optional[Any]  =					int(config['''seed''']     )
 lowercase__				:    int  =					int(config['''batch_size''']     )
 set_seed(UpperCAmelCase     )

 lowercase__ ,							lowercase__				:    str  =					get_dataloaders(UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    Union[str, Any]  =					evaluate.load('''glue'''		,     '''mrpc'''     )

 # If the batch size is too big we use gradient accumulation
 lowercase__				:    Any  =					1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  lowercase__				:    Union[str, Any]  =					batch_size // MAX_GPU_BATCH_SIZE
  lowercase__				:    Any  =					MAX_GPU_BATCH_SIZE

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowercase__				:    Optional[int]  =					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,     return_dict=UpperCAmelCase     )

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 lowercase__				:    List[str]  =					model.to(accelerator.device     )

 # Instantiate optimizer
 lowercase__				:    List[Any]  =					AdamW(params=model.parameters()		,     lr=UpperCAmelCase     )

 # Instantiate scheduler
 lowercase__				:    List[str]  =					get_linear_schedule_with_warmup(
     optimizer=UpperCAmelCase		,     num_warmup_steps=100		,     num_training_steps=(len(UpperCAmelCase     ) * num_epochs) // gradient_accumulation_steps		,     )

 # 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.
 lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    List[str]  =					accelerator.prepare(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )

 # New Code #
 # We need to initialize the trackers we use. Overall configurations can also be stored
 if args.with_tracking:
  lowercase__				:    Optional[Any]  =					os.path.split(UpperCAmelCase     )[-1].split('''.'''     )[0]
  accelerator.init_trackers(UpperCAmelCase		,     UpperCAmelCase     )

 # Now we train the model
 for epoch in range(UpperCAmelCase     ):
  model.train()
  # New Code #
  # For our tracking example, we will log the total loss of each epoch
  if args.with_tracking:
   lowercase__				:    str  =					0
  for step, batch in enumerate(UpperCAmelCase     ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device     )
   lowercase__				:    List[str]  =					model(**UpperCAmelCase     )
   lowercase__				:    List[str]  =					outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   lowercase__				:    List[str]  =					loss / gradient_accumulation_steps
   accelerator.backward(UpperCAmelCase     )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  for step, batch in enumerate(UpperCAmelCase     ):
   # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
   batch.to(accelerator.device     )
   with torch.no_grad():
    lowercase__				:    List[str]  =					model(**UpperCAmelCase     )
   lowercase__				:    Optional[int]  =					outputs.logits.argmax(dim=-1     )
   lowercase__ ,							lowercase__				:    Optional[int]  =					accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
   metric.add_batch(
       predictions=UpperCAmelCase		,     references=UpperCAmelCase		,     )

  lowercase__				:    int  =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""		,     UpperCAmelCase     )

  # New Code #
  # To actually log, we call `Accelerator.log`
  # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': eval_metric['''accuracy'''],
           '''f1''': eval_metric['''f1'''],
           '''train_loss''': total_loss.item() / len(UpperCAmelCase     ),
           '''epoch''': epoch,
       }		,     step=UpperCAmelCase		,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
 if args.with_tracking:
  accelerator.end_training()
def 			__UpperCamelCase       (   ):
 lowercase__				:    Any  =					argparse.ArgumentParser(description='''Simple example of training script.'''     )
 parser.add_argument(
     '''--mixed_precision'''		,     type=UpperCAmelCase		,     default=UpperCAmelCase		,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']		,     help='''Whether to use mixed precision. Choose'''
     '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
     '''and an Nvidia Ampere GPU.'''		,     )
 parser.add_argument('''--cpu'''		,     action='''store_true'''		,     help='''If passed, will train on the CPU.'''     )
 parser.add_argument(
     '''--with_tracking'''		,     action='''store_true'''		,     help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,     )
 parser.add_argument(
     '''--project_dir'''		,     type=UpperCAmelCase		,     default='''logs'''		,     help='''Location on where to store experiment tracking logs` and relevent project information'''		,     )
 lowercase__				:    str  =					parser.parse_args()
 lowercase__				:    Tuple  =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(UpperCAmelCase		,     UpperCAmelCase     )


if __name__ == "__main__":
  main()

