


'''simple docstring'''



from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFTransfoXLForSequenceClassification,
	    TFTransfoXLLMHeadModel,
	    TFTransfoXLModel,
	)






class  UpperCamelCase__:




				def __init__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				int   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			13
										UpperCAmelCase         =			7
										UpperCAmelCase         =			30
										UpperCAmelCase         =			self.seq_length + self.mem_len
										UpperCAmelCase         =			15
										UpperCAmelCase         =			True
										UpperCAmelCase         =			True
										UpperCAmelCase         =			99
										UpperCAmelCase         =			[10, 50, 80]
										UpperCAmelCase         =			32
										UpperCAmelCase         =			32
										UpperCAmelCase         =			4
										UpperCAmelCase         =			8
										UpperCAmelCase         =			128
										UpperCAmelCase         =			2
										UpperCAmelCase         =			2
										UpperCAmelCase         =			None
										UpperCAmelCase         =			1
										UpperCAmelCase         =			0
										UpperCAmelCase         =			3
										UpperCAmelCase         =			self.vocab_size - 1
										UpperCAmelCase         =			0.01




				def   a__(	self    :				Optional[Any]   )->  List[Any]:

										"""simple docstring"""




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

										UpperCAmelCase         =			None
										if self.use_labels:
																UpperCAmelCase         =			ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size   )

										UpperCAmelCase         =			TransfoXLConfig(
										    vocab_size=self.vocab_size   ,					mem_len=self.mem_len   ,					clamp_len=self.clamp_len   ,					cutoffs=self.cutoffs   ,					d_model=self.hidden_size   ,					d_embed=self.d_embed   ,					n_head=self.num_attention_heads   ,					d_head=self.d_head   ,					d_inner=self.d_inner   ,					div_val=self.div_val   ,					n_layer=self.num_hidden_layers   ,					eos_token_id=self.eos_token_id   ,					pad_token_id=self.vocab_size - 1   ,					init_range=self.init_range   ,					num_labels=self.num_labels   ,					)

										return (config, input_ids_a, input_ids_a, lm_labels)




				def   a__(	self    :				Tuple   )->  int:

										"""simple docstring"""




										random.seed(self.seed   )
										tf.random.set_seed(self.seed   )




				def   a__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Dict   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			TFTransfoXLModel(UpperCamelCase__   )

										UpperCAmelCase         =			model(UpperCamelCase__   ).to_tuple()

										UpperCAmelCase         =			{'''input_ids''': input_ids_a, '''mems''': mems_a}

										UpperCAmelCase         =			model(UpperCamelCase__   ).to_tuple()

										self.parent.assertEqual(hidden_states_a.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )
										self.parent.assertEqual(hidden_states_a.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a]   ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,					)
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a]   ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,					)




				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				List[str]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			TFTransfoXLLMHeadModel(UpperCamelCase__   )

										UpperCAmelCase         =			model(UpperCamelCase__   ).to_tuple()

										UpperCAmelCase         =			{'''input_ids''': input_ids_a, '''labels''': lm_labels}
										UpperCAmelCase         =			model(UpperCamelCase__   ).to_tuple()

										UpperCAmelCase         =			model([input_ids_a, mems_a]   ).to_tuple()

										UpperCAmelCase         =			{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

										UpperCAmelCase         =			model(UpperCamelCase__   ).to_tuple()

										self.parent.assertEqual(lm_logits_a.shape   ,					(self.batch_size, self.seq_length, self.vocab_size)   )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a]   ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,					)

										self.parent.assertEqual(lm_logits_a.shape   ,					(self.batch_size, self.seq_length, self.vocab_size)   )
										self.parent.assertListEqual(
										    [mem.shape for mem in mems_a]   ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,					)




				def   a__(	self    :				str   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				int   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			TFTransfoXLForSequenceClassification(UpperCamelCase__   )
										UpperCAmelCase         =			model(UpperCamelCase__   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )





				def   a__(	self    :				Union[str, Any]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.prepare_config_and_inputs()
										(UpperCAmelCase)         =			config_and_inputs
										UpperCAmelCase         =			{'''input_ids''': input_ids_a}
										return config, inputs_dict







@require_tf
class  UpperCamelCase__(							SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       unittest.TestCase ):
				__magic_name__       :   Tuple							= (
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				__magic_name__       :   Optional[int]							= () if is_tf_available() else ()
				__magic_name__       :   str							= (
				    {
				        "feature-extraction": TFTransfoXLModel,
				        "text-classification": TFTransfoXLForSequenceClassification,
				        "text-generation": TFTransfoXLLMHeadModel,
				        "zero-shot": TFTransfoXLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
				__magic_name__       :   Optional[Any]							= False
				__magic_name__       :   Optional[Any]							= False
				__magic_name__       :   List[str]							= False
				__magic_name__       :   Optional[int]							= False




				def   a__(	self    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Dict   )->  Optional[int]:

										"""simple docstring"""




										if pipeline_test_casse_name == "TextGenerationPipelineTests":
																# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
																# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
																# tokenizer.
																return True

										return False




				def   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			TFTransfoXLModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=UpperCamelCase__   ,					d_embed=37   )




				def   a__(	self    :				Dict   )->  Any:

										"""simple docstring"""




										self.config_tester.run_common_tests()




				def   a__(	self    :				Dict   )->  Any:

										"""simple docstring"""




										self.model_tester.set_seed()
										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__   )




				def   a__(	self    :				Union[str, Any]   )->  Dict:

										"""simple docstring"""




										self.model_tester.set_seed()
										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__   )




				def   a__(	self    :				Any   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__   )




				def   a__(	self    :				List[Any]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			[TFTransfoXLForSequenceClassification]

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			model_class(UpperCamelCase__   )
																assert isinstance(model.get_input_embeddings()   ,					tf.keras.layers.Layer   )
																if model_class in list_other_models_with_output_ebd:
																						UpperCAmelCase         =			model.get_output_embeddings()
																						assert isinstance(UpperCamelCase__   ,					tf.keras.layers.Layer   )
																						UpperCAmelCase         =			model.get_bias()
																						assert name is None
																else:
																						UpperCAmelCase         =			model.get_output_embeddings()
																						assert x is None
																						UpperCAmelCase         =			model.get_bias()
																						assert name is None




				def   a__(	self    :				Optional[Any]   )->  Optional[int]:

										"""simple docstring"""




										pass




				@slow
				def   a__(	self    :				List[str]   )->  Tuple:

										"""simple docstring"""




										for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																UpperCAmelCase         =			TFTransfoXLModel.from_pretrained(UpperCamelCase__   )
																self.assertIsNotNone(UpperCamelCase__   )





				@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''   )
				def   a__(	self    :				Union[str, Any]   )->  str:

										"""simple docstring"""




										pass







@require_tf
class  UpperCamelCase__(							unittest.TestCase ):




				@unittest.skip('''Skip test until #12651 is resolved.'''   )
				@slow
				def   a__(	self    :				Optional[Any]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''   )
										# fmt: off
										UpperCAmelCase         =			tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]   ,					dtype=tf.intaa   )  # noqa: E231
										# fmt: on
										#  In 1991 , the remains of Russian Tsar Nicholas II and his family
										#  ( except for Alexei and Maria ) are discovered .
										#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
										#  remainder of the story . 1883 Western Siberia ,
										#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
										#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
										#  father initially slaps him for making such an accusation , Rasputin watches as the
										#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
										#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
										#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

										# fmt: off
										UpperCAmelCase         =			[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # noqa: E231
										# fmt: on
										#  In 1991, the remains of Russian Tsar Nicholas II and his family (
										#  except for Alexei and Maria ) are discovered. The voice of young son,
										#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
										#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
										#  and a group of men to perform magic. Rasputin has a vision and
										#  denounces one of the men as a horse thief. Although his father initially
										#  slaps him for making such an accusation, Rasputin watches as the man
										#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
										#  of the Virgin Mary, prompting him to become a priest.
										#  Rasputin quickly becomes famous, with people, even a bishop, begging for
										#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
										# Nicholas II and his family were discovered. The voice of <unk> young son,
										# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

										UpperCAmelCase         =			model.generate(UpperCamelCase__   ,					max_length=200   ,					do_sample=UpperCamelCase__   )
										self.assertListEqual(output_ids[0].numpy().tolist()   ,					UpperCamelCase__   )









'''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
#
########################################################################

_lowercase    :    Optional[Any]			     =  16
_lowercase    :    Dict			     =  32


def    lowerCamelCase__   ( A     :    Accelerator   ,     A     :    int = 16  ):




						'''simple docstring'''






						UpperCAmelCase         =			AutoTokenizer.from_pretrained('''bert-base-cased'''  )
						UpperCAmelCase         =			load_dataset('''glue'''   ,     '''mrpc'''  )

						def tokenize_function(A     :    int  ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase         =			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
						# starting with the main process first:
						with accelerator.main_process_first():
												UpperCAmelCase         =			datasets.map(
												    A   ,     batched=A   ,     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
						UpperCAmelCase         =			tokenized_datasets.rename_column('''label'''   ,     '''labels'''  )

						def collate_fn(A     :    List[Any]  ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCAmelCase         =			1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		UpperCAmelCase         =			16
												elif accelerator.mixed_precision != "no":
																		UpperCAmelCase         =			8
												else:
																		UpperCAmelCase         =			None

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

						# Instantiate dataloaders.
						UpperCAmelCase         =			DataLoader(
						    tokenized_datasets['''train''']   ,     shuffle=A   ,     collate_fn=A   ,     batch_size=A  )
						UpperCAmelCase         =			DataLoader(
						    tokenized_datasets['''validation''']   ,     shuffle=A   ,     collate_fn=A   ,     batch_size=A  )

						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

	_lowercase    :    Union[str, Any]			     =  mocked_dataloaders  # noqa: F811


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Tuple  ):




						'''simple docstring'''






						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,     A  ) == "1":
												UpperCAmelCase         =			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:
												UpperCAmelCase         =			Accelerator(
												    cpu=args.cpu   ,     mixed_precision=args.mixed_precision   ,     log_with='''all'''   ,     project_dir=args.project_dir  )
						else:
												UpperCAmelCase         =			Accelerator(cpu=args.cpu   ,     mixed_precision=args.mixed_precision  )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase         =			config['''lr''']
						UpperCAmelCase         =			int(config['''num_epochs''']  )
						UpperCAmelCase         =			int(config['''seed''']  )
						UpperCAmelCase         =			int(config['''batch_size''']  )
						set_seed(A  )

						UpperCAmelCase       ,       UpperCAmelCase         =			get_dataloaders(A   ,     A  )
						UpperCAmelCase         =			evaluate.load('''glue'''   ,     '''mrpc'''  )

						# If the batch size is too big we use gradient accumulation
						UpperCAmelCase         =			1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												UpperCAmelCase         =			batch_size // MAX_GPU_BATCH_SIZE
												UpperCAmelCase         =			MAX_GPU_BATCH_SIZE

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

						# 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).
						UpperCAmelCase         =			model.to(accelerator.device  )

						# Instantiate optimizer
						UpperCAmelCase         =			AdamW(params=model.parameters()   ,     lr=A  )

						# Instantiate scheduler
						UpperCAmelCase         =			get_linear_schedule_with_warmup(
						    optimizer=A   ,     num_warmup_steps=1_00   ,     num_training_steps=(len(A  ) * 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.
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			accelerator.prepare(
						    A   ,     A   ,     A   ,     A   ,     A  )

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

						# Now we train the model
						for epoch in range(A  ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		UpperCAmelCase         =			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  )
																		UpperCAmelCase         =			model(**A  )
																		UpperCAmelCase         =			outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		UpperCAmelCase         =			loss / gradient_accumulation_steps
																		accelerator.backward(A  )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

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

												# 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(A  ),
																		        '''epoch''': epoch,
																		    }   ,     step=A   ,     )

    # 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    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			argparse.ArgumentParser(description='''Simple example of training script.'''  )
						parser.add_argument(
						    '''--mixed_precision'''   ,     type=A   ,     default=A   ,     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=A   ,     default='''logs'''   ,     help='''Location on where to store experiment tracking logs` and relevent project information'''   ,     )
						UpperCAmelCase         =			parser.parse_args()
						UpperCAmelCase         =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(A   ,     A  )


if __name__ == "__main__":
	main()





