"""simple docstring"""




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
     _a      :		int           =   'platform'
     import jax
     import jax.numpy as jnp
     import numpy as np

     from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class 						__A							:
     _UpperCamelCase							:			Dict							=		PegasusConfig
     _UpperCamelCase							:			List[Any]							=		{}
     _UpperCamelCase							:			Any							=		"gelu"






     def __init__(		self				,     a__				,     a__=13				,     a__=7				,     a__=True				,     a__=False				,     a__=99				,     a__=32				,     a__=5				,     a__=4				,     a__=37				,     a__=0.1				,     a__=0.1				,     a__=20				,     a__=2				,     a__=1				,     a__=0				,     ):
       _lowerCAmelCase						:       List[str]  			=     parent
       _lowerCAmelCase						:       Optional[int]  			=     batch_size
       _lowerCAmelCase						:       str  			=     seq_length
       _lowerCAmelCase						:       Dict  			=     is_training
       _lowerCAmelCase						:       int  			=     use_labels
       _lowerCAmelCase						:       Optional[Any]  			=     vocab_size
       _lowerCAmelCase						:       Optional[Any]  			=     hidden_size
       _lowerCAmelCase						:       int  			=     num_hidden_layers
       _lowerCAmelCase						:       Optional[int]  			=     num_attention_heads
       _lowerCAmelCase						:       str  			=     intermediate_size

       _lowerCAmelCase						:       List[str]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       int  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       Union[str, Any]  			=     max_position_embeddings
       _lowerCAmelCase						:       List[str]  			=     eos_token_id
       _lowerCAmelCase						:       List[str]  			=     pad_token_id
       _lowerCAmelCase						:       Tuple  			=     bos_token_id






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     ids_tensor([self.batch_size, self.seq_length - 1]				,     self.vocab_size  ).clip(3				,     self.vocab_size  )
       _lowerCAmelCase						:       List[Any]  			=     np.expand_dims(np.array([self.eos_token_id] * self.batch_size  )				,     1  )
       _lowerCAmelCase						:       Dict  			=     np.concatenate([input_ids, eos_tensor]				,     axis=1  )

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

       _lowerCAmelCase						:       List[Any]  			=     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						:       List[Any]  			=     prepare_pegasus_inputs_dict(a__				,     a__				,     a__  )
       return config, inputs_dict






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     20
       _lowerCAmelCase						:       Tuple  			=     model_class_name(a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     model.encode(inputs_dict["""input_ids"""]  )

       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     (
           inputs_dict["""decoder_input_ids"""],
           inputs_dict["""decoder_attention_mask"""],
       )

       _lowerCAmelCase						:       Tuple  			=     model.init_cache(decoder_input_ids.shape[0]				,     a__				,     a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,     dtype="""i4"""  )

       _lowerCAmelCase						:       Any  			=     jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]				,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,     )
       _lowerCAmelCase						:       Tuple  			=     model.decode(
           decoder_input_ids[:, :-1]				,     a__				,     decoder_attention_mask=a__				,     past_key_values=a__				,     decoder_position_ids=a__				,     )

       _lowerCAmelCase						:       str  			=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,     dtype="""i4"""  )
       _lowerCAmelCase						:       List[Any]  			=     model.decode(
           decoder_input_ids[:, -1:]				,     a__				,     decoder_attention_mask=a__				,     past_key_values=outputs_cache.past_key_values				,     decoder_position_ids=a__				,     )

       _lowerCAmelCase						:       Optional[int]  			=     model.decode(a__				,     a__  )

       _lowerCAmelCase						:       Optional[int]  			=     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  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     20
       _lowerCAmelCase						:       Optional[Any]  			=     model_class_name(a__  )

       _lowerCAmelCase						:       Tuple  			=     model.encode(inputs_dict["""input_ids"""]  )

       _lowerCAmelCase			,	_lowerCAmelCase						:       List[Any]  			=     (
           inputs_dict["""decoder_input_ids"""],
           inputs_dict["""decoder_attention_mask"""],
       )

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

       _lowerCAmelCase						:       List[Any]  			=     model.init_cache(decoder_input_ids.shape[0]				,     a__				,     a__  )
       _lowerCAmelCase						:       Tuple  			=     jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]				,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,     )

       _lowerCAmelCase						:       int  			=     model.decode(
           decoder_input_ids[:, :-1]				,     a__				,     decoder_attention_mask=a__				,     past_key_values=a__				,     decoder_position_ids=a__				,     )
       _lowerCAmelCase						:       Dict  			=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,     dtype="""i4"""  )
       _lowerCAmelCase						:       int  			=     model.decode(
           decoder_input_ids[:, -1:]				,     a__				,     past_key_values=outputs_cache.past_key_values				,     decoder_attention_mask=a__				,     decoder_position_ids=a__				,     )

       _lowerCAmelCase						:       Any  			=     model.decode(a__				,     a__				,     decoder_attention_mask=a__  )

       _lowerCAmelCase						:       Any  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]=None			,_lowerCamelCase						:       Dict=None			,)       ->      Union[str, Any]:
  if attention_mask is None:
    _lowerCAmelCase						:       int  			=     np.not_equal(_lowerCamelCase			,config.pad_token_id	).astype(np.inta	)
  if decoder_attention_mask is None:
    _lowerCAmelCase						:       Union[str, Any]  			=     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 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			int							=		(
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     _UpperCamelCase							:			Tuple							=		(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     _UpperCamelCase							:			str							=		True
     _UpperCamelCase							:			str							=		False
     _UpperCamelCase							:			List[Any]							=		False
     _UpperCamelCase							:			str							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     FlaxPegasusModelTester(self  )
       _lowerCAmelCase						:       List[str]  			=     ConfigTester(self				,     config_class=a__  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(a__				,     a__				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     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(a__				,     a__				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__  ):
           _lowerCAmelCase						:       List[str]  			=     self._prepare_for_class(a__				,     a__  )
           _lowerCAmelCase						:       List[Any]  			=     model_class(a__  )

           @jax.jit
           def encode_jitted(a__				,     a__=None				,     **a__  ):
             return model.encode(input_ids=a__				,     attention_mask=a__  )

           with self.subTest("""JIT Enabled"""  ):
             _lowerCAmelCase						:       List[str]  			=     encode_jitted(**a__  ).to_tuple()

           with self.subTest("""JIT Disabled"""  ):
             with jax.disable_jit():
               _lowerCAmelCase						:       Optional[Any]  			=     encode_jitted(**a__  ).to_tuple()

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






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__  ):
           _lowerCAmelCase						:       str  			=     model_class(a__  )
           _lowerCAmelCase						:       Optional[int]  			=     model.encode(inputs_dict["""input_ids"""]				,     inputs_dict["""attention_mask"""]  )

           _lowerCAmelCase						:       Optional[Any]  			=     {
               """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(a__				,     a__				,     a__  ):
             return model.decode(
                 decoder_input_ids=a__				,     decoder_attention_mask=a__				,     encoder_outputs=a__				,     )

           with self.subTest("""JIT Enabled"""  ):
             _lowerCAmelCase						:       Optional[int]  			=     decode_jitted(**a__  ).to_tuple()

           with self.subTest("""JIT Disabled"""  ):
             with jax.disable_jit():
               _lowerCAmelCase						:       Tuple  			=     decode_jitted(**a__  ).to_tuple()

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






     @slow
     def     __A  (		self  ):
       for model_class_name in self.all_model_classes:
         _lowerCAmelCase						:       Any  			=     model_class_name.from_pretrained("""google/pegasus-large"""				,     from_pt=a__  )
         _lowerCAmelCase						:       List[str]  			=     np.ones((1, 1)  )
         _lowerCAmelCase						:       Optional[Any]  			=     model(a__  )
         self.assertIsNotNone(a__  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""  )
       _lowerCAmelCase						:       int  			=     PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""  )

       _lowerCAmelCase						:       List[str]  			=     [
           """ 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						:       List[Any]  			=     [
           """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						:       Optional[int]  			=     tokenizer(a__				,     return_tensors="""np"""				,     truncation=a__				,     max_length=512				,     padding=a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     model.generate(**a__				,     num_beams=2  ).sequences
       _lowerCAmelCase						:       List[Any]  			=     tokenizer.batch_decode(a__				,     skip_special_tokens=a__  )
       assert tgt_text == decoded










"""simple docstring"""






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__     						=						16
lowercase__     						=						32







def      _snake_case			(				lowercase__							,    lowercase__ = 16							,    lowercase__ = "bert-base-cased"		):
				_lowerCamelCase     :			List[Any]    =    AutoTokenizer.from_pretrained(lowercase__		)
				_lowerCamelCase     :			Tuple    =    load_dataset('glue'							,    'mrpc'		)

				def tokenize_function(lowercase__		):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCamelCase     :			Union[str, Any]    =    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
				_lowerCamelCase     :			int    =    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
				_lowerCamelCase     :			Optional[int]    =    tokenized_datasets.rename_column('label'							,    'labels'		)

				def collate_fn(lowercase__		):
								# 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.
				_lowerCamelCase     :			List[str]    =    DataLoader(
				    tokenized_datasets['train']							,    shuffle=lowercase__							,    collate_fn=lowercase__							,    batch_size=lowercase__		)
				_lowerCamelCase     :			int    =    DataLoader(
				    tokenized_datasets['validation']							,    shuffle=lowercase__							,    collate_fn=lowercase__							,    batch_size=lowercase__		)

				return train_dataloader, eval_dataloader







def      _snake_case			(				lowercase__							,    lowercase__		):
				# Initialize accelerator
				_lowerCamelCase     :			Optional[int]    =    Accelerator()

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

				set_seed(lowercase__		)
				_lowerCamelCase, _lowerCamelCase     :			Optional[int]    =    get_dataloaders(lowercase__							,    lowercase__							,    lowercase__		)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_lowerCamelCase     :			int    =    AutoModelForSequenceClassification.from_pretrained(lowercase__							,    return_dict=lowercase__		)

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

				if accelerator.state.deepspeed_plugin is not None:
								_lowerCamelCase     :			str    =    accelerator.state.deepspeed_plugin.deepspeed_config[
								    'gradient_accumulation_steps'
								]
				else:
								_lowerCamelCase     :			Tuple    =    1
				_lowerCamelCase     :			List[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
				):
								_lowerCamelCase     :			Tuple    =    get_linear_schedule_with_warmup(
								    optimizer=lowercase__							,    num_warmup_steps=0							,    num_training_steps=lowercase__							,    )
				else:
								_lowerCamelCase     :			Any    =    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.
				_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase     :			Union[str, Any]    =    accelerator.prepare(
				    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)

				# We need to keep track of how many total steps we have iterated over
				_lowerCamelCase     :			Union[str, Any]    =    0
				# We also need to keep track of the stating epoch so files are named properly
				_lowerCamelCase     :			Dict    =    0

				# Now we train the model
				_lowerCamelCase     :			Dict    =    evaluate.load('glue'							,    'mrpc'		)
				_lowerCamelCase     :			Optional[int]    =    0
				_lowerCamelCase     :			str    =    {}
				for epoch in range(lowercase__							,    lowercase__		):
								model.train()
								for step, batch in enumerate(lowercase__		):
												_lowerCamelCase     :			List[Any]    =    model(**lowercase__		)
												_lowerCamelCase     :			int    =    outputs.loss
												_lowerCamelCase     :			Dict    =    loss / gradient_accumulation_steps
												accelerator.backward(lowercase__		)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

								model.eval()
								_lowerCamelCase     :			Union[str, Any]    =    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():
																_lowerCamelCase     :			Optional[int]    =    model(**lowercase__		)
												_lowerCamelCase     :			Dict    =    outputs.logits.argmax(dim=-1		)
												# It is slightly faster to call this once, than multiple times
												_lowerCamelCase, _lowerCamelCase     :			List[str]    =    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:
																				_lowerCamelCase     :			Optional[Any]    =    predictions[: len(eval_dataloader.dataset		) - samples_seen]
																				_lowerCamelCase     :			Dict    =    references[: len(eval_dataloader.dataset		) - samples_seen]
																else:
																				samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=lowercase__							,    references=lowercase__							,    )

								_lowerCamelCase     :			List[Any]    =    metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''							,    lowercase__		)
								_lowerCamelCase     :			Tuple    =    eval_metric['accuracy']

								if best_performance < eval_metric["accuracy"]:
												_lowerCamelCase     :			str    =    eval_metric['accuracy']

				if args.performance_lower_bound is not None:
								assert (
								    args.performance_lower_bound <= best_performance
								), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir							,    'all_results.json'		)							,    'w'		) as f:
												json.dump(lowercase__							,    lowercase__		)







def      _snake_case			(				):
				_lowerCamelCase     :			Any    =    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(
				    '--performance_lower_bound'							,    type=lowercase__							,    default=lowercase__							,    help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'							,    )
				parser.add_argument(
				    '--num_epochs'							,    type=lowercase__							,    default=3							,    help='Number of train epochs.'							,    )
				_lowerCamelCase     :			Optional[Any]    =    parser.parse_args()
				_lowerCamelCase     :			str    =    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
				training_function(lowercase__							,    lowercase__		)


if __name__ == "__main__":
			main()