





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     _snake_case  :    Any						    =  'platform'
     import jax
     import jax.numpy as jnp
     import numpy as np

     from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class 				_UpperCAmelCase    :

  """simple docstring"""


  a_        = PegasusConfig
  a_        = {}
  a_        = 'gelu'






  def __init__(				self :		Dict					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		Optional[Any]=1_3					,  lowerCAmelCase_ :		List[Any]=7					,  lowerCAmelCase_ :		Union[str, Any]=True					,  lowerCAmelCase_ :		Optional[int]=False					,  lowerCAmelCase_ :		List[Any]=9_9					,  lowerCAmelCase_ :		Union[str, Any]=3_2					,  lowerCAmelCase_ :		str=5					,  lowerCAmelCase_ :		Optional[Any]=4					,  lowerCAmelCase_ :		List[str]=3_7					,  lowerCAmelCase_ :		Optional[Any]=0.1					,  lowerCAmelCase_ :		str=0.1					,  lowerCAmelCase_ :		Tuple=2_0					,  lowerCAmelCase_ :		Optional[int]=2					,  lowerCAmelCase_ :		str=1					,  lowerCAmelCase_ :		Optional[int]=0					,  )							->						Any:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	seq_length
     __lowerCAmelCase								=	is_training
     __lowerCAmelCase								=	use_labels
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	intermediate_size

     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	attention_probs_dropout_prob
     __lowerCAmelCase								=	max_position_embeddings
     __lowerCAmelCase								=	eos_token_id
     __lowerCAmelCase								=	pad_token_id
     __lowerCAmelCase								=	bos_token_id






  def 				lowercase		(				self :		Any     )							->						str:
     __lowerCAmelCase								=	ids_tensor([self.batch_size, self.seq_length - 1]					,  self.vocab_size     ).clip(3					,  self.vocab_size     )
     __lowerCAmelCase								=	np.expand_dims(np.array([self.eos_token_id] * self.batch_size     )					,  1     )
     __lowerCAmelCase								=	np.concatenate([input_ids, eos_tensor]					,  axis=1     )

     __lowerCAmelCase								=	ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size     )

     __lowerCAmelCase								=	self.config_cls(
         vocab_size=self.vocab_size					,  d_model=self.hidden_size					,  encoder_layers=self.num_hidden_layers					,  decoder_layers=self.num_hidden_layers					,  encoder_attention_heads=self.num_attention_heads					,  decoder_attention_heads=self.num_attention_heads					,  encoder_ffn_dim=self.intermediate_size					,  decoder_ffn_dim=self.intermediate_size					,  dropout=self.hidden_dropout_prob					,  attention_dropout=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  eos_token_ids=[2]					,  bos_token_id=self.bos_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.pad_token_id					,  **self.config_updates					,  )
     __lowerCAmelCase								=	prepare_pegasus_inputs_dict(UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_     )
     return config, inputs_dict






  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		Dict     )							->						int:
     __lowerCAmelCase								=	2_0
     __lowerCAmelCase								=	model_class_name(UpperCamelCase_     )

     __lowerCAmelCase								=	model.encode(inputs_dict['input_ids']     )

     __lowerCAmelCase	,	__lowerCAmelCase								=	(
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     __lowerCAmelCase								=	model.init_cache(decoder_input_ids.shape[0]					,  UpperCamelCase_					,  UpperCamelCase_     )
     __lowerCAmelCase								=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,  dtype='i4'     )

     __lowerCAmelCase								=	jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,  )
     __lowerCAmelCase								=	model.decode(
         decoder_input_ids[:, :-1]					,  UpperCamelCase_					,  decoder_attention_mask=UpperCamelCase_					,  past_key_values=UpperCamelCase_					,  decoder_position_ids=UpperCamelCase_					,  )

     __lowerCAmelCase								=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,  dtype='i4'     )
     __lowerCAmelCase								=	model.decode(
         decoder_input_ids[:, -1:]					,  UpperCamelCase_					,  decoder_attention_mask=UpperCamelCase_					,  past_key_values=outputs_cache.past_key_values					,  decoder_position_ids=UpperCamelCase_					,  )

     __lowerCAmelCase								=	model.decode(UpperCamelCase_					,  UpperCamelCase_     )

     __lowerCAmelCase								=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
     self.parent.assertTrue(diff < 1e-3					,  msg=f"""Max diff is {diff}"""     )






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		Dict					,  lowerCAmelCase_ :		Dict					,  lowerCAmelCase_ :		Any     )							->						Optional[int]:
     __lowerCAmelCase								=	2_0
     __lowerCAmelCase								=	model_class_name(UpperCamelCase_     )

     __lowerCAmelCase								=	model.encode(inputs_dict['input_ids']     )

     __lowerCAmelCase	,	__lowerCAmelCase								=	(
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     __lowerCAmelCase								=	jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
         ]					,  axis=-1					,  )

     __lowerCAmelCase								=	model.init_cache(decoder_input_ids.shape[0]					,  UpperCamelCase_					,  UpperCamelCase_     )
     __lowerCAmelCase								=	jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,  )

     __lowerCAmelCase								=	model.decode(
         decoder_input_ids[:, :-1]					,  UpperCamelCase_					,  decoder_attention_mask=UpperCamelCase_					,  past_key_values=UpperCamelCase_					,  decoder_position_ids=UpperCamelCase_					,  )
     __lowerCAmelCase								=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,  dtype='i4'     )
     __lowerCAmelCase								=	model.decode(
         decoder_input_ids[:, -1:]					,  UpperCamelCase_					,  past_key_values=outputs_cache.past_key_values					,  decoder_attention_mask=UpperCamelCase_					,  decoder_position_ids=UpperCamelCase_					,  )

     __lowerCAmelCase								=	model.decode(UpperCamelCase_					,  UpperCamelCase_					,  decoder_attention_mask=UpperCamelCase_     )

     __lowerCAmelCase								=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
     self.parent.assertTrue(diff < 1e-3					,  msg=f"""Max diff is {diff}"""     )





def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Tuple=None,	lowerCAmelCase_      :			Tuple=None,	):



   if attention_mask is None:
      __lowerCAmelCase								=	np.not_equal(A__,	config.pad_token_id	).astype(np.inta	)
   if decoder_attention_mask is None:
      __lowerCAmelCase								=	np.concatenate(
          [
              np.ones(decoder_input_ids[:, :1].shape,	dtype=np.inta	),
              np.not_equal(decoder_input_ids[:, 1:],	config.pad_token_id	).astype(np.inta	),
          ],	axis=-1,	)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
   }




@require_flax
class 				_UpperCAmelCase    (    __lowerCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = (
      (
          FlaxPegasusForConditionalGeneration,
          FlaxPegasusModel,
      )
      if is_flax_available()
      else ()
  )
  a_        = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
  a_        = True
  a_        = False
  a_        = False
  a_        = False






  def 				lowercase		(				self :		Union[str, Any]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	FlaxPegasusModelTester(self     )
     __lowerCAmelCase								=	ConfigTester(self					,  config_class=UpperCamelCase_     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						Optional[Any]:
     self.config_tester.run_common_tests()






  def 				lowercase		(				self :		Optional[int]     )							->						List[str]:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_     )






  def 				lowercase		(				self :		List[Any]     )							->						Optional[int]:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward_with_attn_mask(UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_     )






  def 				lowercase		(				self :		Dict     )							->						Tuple:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__     ):
           __lowerCAmelCase								=	self._prepare_for_class(UpperCamelCase_					,  UpperCamelCase_     )
           __lowerCAmelCase								=	model_class(UpperCamelCase_     )

           @jax.jit
           def encode_jitted(lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		Optional[Any]=None					,  **lowerCAmelCase_ :		Dict     ):
              return model.encode(input_ids=UpperCamelCase_					,  attention_mask=UpperCamelCase_     )

           with self.subTest('JIT Enabled'     ):
              __lowerCAmelCase								=	encode_jitted(**UpperCamelCase_     ).to_tuple()

           with self.subTest('JIT Disabled'     ):
              with jax.disable_jit():
                 __lowerCAmelCase								=	encode_jitted(**UpperCamelCase_     ).to_tuple()

           self.assertEqual(len(UpperCamelCase_     )					,  len(UpperCamelCase_     )     )
           for jitted_output, output in zip(UpperCamelCase_					,  UpperCamelCase_     ):
              self.assertEqual(jitted_output.shape					,  output.shape     )






  def 				lowercase		(				self :		str     )							->						Union[str, Any]:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__     ):
           __lowerCAmelCase								=	model_class(UpperCamelCase_     )
           __lowerCAmelCase								=	model.encode(inputs_dict['input_ids']					,  inputs_dict['attention_mask']     )

           __lowerCAmelCase								=	{
               'decoder_input_ids': inputs_dict['decoder_input_ids'],
               'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
               'encoder_outputs': encoder_outputs,
           }

           @jax.jit
           def decode_jitted(lowerCAmelCase_ :		List[Any]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		str     ):
              return model.decode(
                  decoder_input_ids=UpperCamelCase_					,  decoder_attention_mask=UpperCamelCase_					,  encoder_outputs=UpperCamelCase_					,  )

           with self.subTest('JIT Enabled'     ):
              __lowerCAmelCase								=	decode_jitted(**UpperCamelCase_     ).to_tuple()

           with self.subTest('JIT Disabled'     ):
              with jax.disable_jit():
                 __lowerCAmelCase								=	decode_jitted(**UpperCamelCase_     ).to_tuple()

           self.assertEqual(len(UpperCamelCase_     )					,  len(UpperCamelCase_     )     )
           for jitted_output, output in zip(UpperCamelCase_					,  UpperCamelCase_     ):
              self.assertEqual(jitted_output.shape					,  output.shape     )






  @slow
  def 				lowercase		(				self :		Tuple     )							->						str:
     for model_class_name in self.all_model_classes:
        __lowerCAmelCase								=	model_class_name.from_pretrained('google/pegasus-large'					,  from_pt=UpperCamelCase_     )
        __lowerCAmelCase								=	np.ones((1, 1)     )
        __lowerCAmelCase								=	model(UpperCamelCase_     )
        self.assertIsNotNone(UpperCamelCase_     )






  @slow
  def 				lowercase		(				self :		Union[str, Any]     )							->						Dict:
     __lowerCAmelCase								=	FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'     )
     __lowerCAmelCase								=	PegasusTokenizer.from_pretrained('google/pegasus-xsum'     )

     __lowerCAmelCase								=	[
         ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
         ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ',
     ]

     __lowerCAmelCase								=	[
         'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
         'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
     ]

     __lowerCAmelCase								=	tokenizer(UpperCamelCase_					,  return_tensors='np'					,  truncation=UpperCamelCase_					,  max_length=5_1_2					,  padding=UpperCamelCase_     )
     __lowerCAmelCase								=	model.generate(**UpperCamelCase_					,  num_beams=2     ).sequences
     __lowerCAmelCase								=	tokenizer.batch_decode(UpperCamelCase_					,  skip_special_tokens=UpperCamelCase_     )
     assert tgt_text == decoded







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


UpperCAmelCase_									=	16
UpperCAmelCase_									=	32




def 					lowerCamelCase__       (						A__       :					Accelerator  ,			A__       :					int = 16  ,			A__       :					str = "bert-base-cased"	):





       '''simple docstring'''



       __lowerCamelCase   =						AutoTokenizer.from_pretrained(A__	)
       __lowerCamelCase   =						load_dataset("""glue"""  ,			"""mrpc"""	)

       def tokenize_function(A__       :					int	):
              # max_length=None => use the model max length (it's actually the default)
              __lowerCamelCase   =						tokenizer(examples["""sentence1"""]  ,			examples["""sentence2"""]  ,			truncation=A__  ,			max_length=A__	)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       __lowerCamelCase   =						datasets.map(
           A__  ,			batched=A__  ,			remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,			load_from_cache_file=A__	)

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

       def collate_fn(A__       :					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(A__  ,			padding="""max_length"""  ,			max_length=128  ,			return_tensors="""pt"""	)
              return tokenizer.pad(A__  ,			padding="""longest"""  ,			return_tensors="""pt"""	)

       # Instantiate dataloaders.
       __lowerCamelCase   =						DataLoader(
           tokenized_datasets["""train"""]  ,			shuffle=A__  ,			collate_fn=A__  ,			batch_size=A__	)
       __lowerCamelCase   =						DataLoader(
           tokenized_datasets["""validation"""]  ,			shuffle=A__  ,			collate_fn=A__  ,			batch_size=A__	)

       return train_dataloader, eval_dataloader




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					Union[str, Any]  ,			A__       :					Tuple  ,			A__       :					Optional[Any]	):





       '''simple docstring'''



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

       __lowerCamelCase   =						metric.compute()
       return eval_metric["accuracy"]




def 					lowerCamelCase__       (						A__       :					Optional[Any]  ,			A__       :					Optional[int]	):





       '''simple docstring'''



       __lowerCamelCase   =						Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __lowerCamelCase   =						config["""lr"""]
       __lowerCamelCase   =						int(config["""num_epochs"""]	)
       __lowerCamelCase   =						int(config["""seed"""]	)
       __lowerCamelCase   =						int(config["""batch_size"""]	)
       __lowerCamelCase   =						args.model_name_or_path

       set_seed(A__	)
       __lowerCamelCase,   __lowerCamelCase   =						get_dataloaders(A__  ,			A__  ,			A__	)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       __lowerCamelCase   =						AutoModelForSequenceClassification.from_pretrained(A__  ,			return_dict=A__	)

       # Instantiate optimizer
       __lowerCamelCase   =						(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       __lowerCamelCase   =						optimizer_cls(params=model.parameters()  ,			lr=A__	)

       if accelerator.state.deepspeed_plugin is not None:
              __lowerCamelCase   =						accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              __lowerCamelCase   =						1
       __lowerCamelCase   =						(len(A__	) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              __lowerCamelCase   =						get_linear_schedule_with_warmup(
                  optimizer=A__  ,			num_warmup_steps=0  ,			num_training_steps=A__  ,			)
       else:
              __lowerCamelCase   =						DummyScheduler(A__  ,			total_num_steps=A__  ,			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.
       __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						accelerator.prepare(
           A__  ,			A__  ,			A__  ,			A__  ,			A__	)

       # We need to keep track of how many total steps we have iterated over
       __lowerCamelCase   =						0
       # We also need to keep track of the stating epoch so files are named properly
       __lowerCamelCase   =						0
       __lowerCamelCase   =						evaluate.load("""glue"""  ,			"""mrpc"""	)
       __lowerCamelCase   =						num_epochs

       if args.partial_train_epoch is not None:
              __lowerCamelCase   =						args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint	)
              __lowerCamelCase   =						args.resume_from_checkpoint.split("""epoch_"""	)[1]
              __lowerCamelCase   =						""""""
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              __lowerCamelCase   =						int(A__	) + 1
              __lowerCamelCase   =						evaluation_loop(A__  ,			A__  ,			A__  ,			A__	)
              accelerator.print("""resumed checkpoint performance:"""  ,			A__	)
              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:
                     __lowerCamelCase   =						json.load(A__	)
                     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
       __lowerCamelCase   =						{}
       for epoch in range(A__  ,			A__	):
              model.train()
              for step, batch in enumerate(A__	):
                     __lowerCamelCase   =						model(**A__	)
                     __lowerCamelCase   =						outputs.loss
                     __lowerCamelCase   =						loss / gradient_accumulation_steps
                     accelerator.backward(A__	)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              __lowerCamelCase   =						f'epoch_{epoch}'
              __lowerCamelCase   =						os.path.join(args.output_dir  ,			A__	)
              accelerator.save_state(A__	)
              __lowerCamelCase   =						evaluation_loop(A__  ,			A__  ,			A__  ,			A__	)
              __lowerCamelCase   =						accuracy
              __lowerCamelCase   =						lr_scheduler.get_lr()[0]
              __lowerCamelCase   =						optimizer.param_groups[0]["""lr"""]
              __lowerCamelCase   =						epoch
              __lowerCamelCase   =						overall_step
              accelerator.print(f'epoch {epoch}:'  ,			A__	)

              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(A__  ,			A__	)




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       __lowerCamelCase   =						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
       parser.add_argument(
           """--model_name_or_path"""  ,			type=A__  ,			default="""bert-base-cased"""  ,			help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,			required=A__  ,			)
       parser.add_argument(
           """--output_dir"""  ,			type=A__  ,			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=A__  ,			default=A__  ,			help="""If the training should continue from a checkpoint folder."""  ,			)
       parser.add_argument(
           """--partial_train_epoch"""  ,			type=A__  ,			default=A__  ,			help="""If passed, the training will stop after this number of epochs."""  ,			)
       parser.add_argument(
           """--num_epochs"""  ,			type=A__  ,			default=2  ,			help="""Number of train epochs."""  ,			)
       __lowerCamelCase   =						parser.parse_args()
       __lowerCamelCase   =						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

       training_function(A__  ,			A__	)


if __name__ == "__main__":
       main()

