



"""simple docstring"""







from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
							from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor






class   UpperCAmelCase__		(   UpperCamelCase  ):



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






									'''simple docstring'''

									A    =       self.config_class(**self.inputs_dict					)
									self.parent.assertTrue(hasattr(snake_case   ,				'hidden_sizes'					)					)
									self.parent.assertTrue(hasattr(snake_case   ,				'neck_hidden_sizes'					)					)
									self.parent.assertTrue(hasattr(snake_case   ,				'num_attention_heads'					)					)






class   UpperCAmelCase__		:



		def __init__(			self	:     Tuple   ,				snake_case	:     List[str]   ,				snake_case	:     Dict=13   ,				snake_case	:     Dict=32   ,				snake_case	:     Dict=2   ,				snake_case	:     Dict=3   ,				snake_case	:     Dict=640   ,				snake_case	:     Optional[int]=4   ,				snake_case	:     int="silu"   ,				snake_case	:     Optional[int]=3   ,				snake_case	:     Union[str, Any]=32   ,				snake_case	:     Optional[Any]=0.1   ,				snake_case	:     str=0.1   ,				snake_case	:     List[Any]=0.1   ,				snake_case	:     List[str]=0.02   ,				snake_case	:     Dict=True   ,				snake_case	:     str=True   ,				snake_case	:     Optional[int]=10   ,				snake_case	:     Dict=None   ,				)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       parent
									A    =       batch_size
									A    =       image_size
									A    =       patch_size
									A    =       num_channels
									A    =       last_hidden_size
									A    =       num_attention_heads
									A    =       hidden_act
									A    =       conv_kernel_size
									A    =       output_stride
									A    =       hidden_dropout_prob
									A    =       attention_probs_dropout_prob
									A    =       classifier_dropout_prob
									A    =       use_labels
									A    =       is_training
									A    =       num_labels
									A    =       initializer_range
									A    =       scope



		def        A_		(			self	:     Any					)      ->    Any:






									'''simple docstring'''

									A    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									A    =       None
									A    =       None
									if self.use_labels:
																A    =       ids_tensor([self.batch_size]   ,				self.num_labels					)
																A    =       ids_tensor([self.batch_size, self.image_size, self.image_size]   ,				self.num_labels					)

									A    =       self.get_config()

									return config, pixel_values, labels, pixel_labels



		def        A_		(			self	:     Any					)      ->    List[Any]:






									'''simple docstring'''

									return MobileViTConfig(
									    image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				num_attention_heads=self.num_attention_heads   ,				hidden_act=self.hidden_act   ,				conv_kernel_size=self.conv_kernel_size   ,				output_stride=self.output_stride   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				classifier_dropout_prob=self.classifier_dropout_prob   ,				initializer_range=self.initializer_range   ,				)



		def        A_		(			self	:     Any   ,				snake_case	:     Any   ,				snake_case	:     Optional[Any]   ,				snake_case	:     Tuple   ,				snake_case	:     Optional[Any]					)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       TFMobileViTModel(config=snake_case					)
									A    =       model(snake_case   ,				training=snake_case					)
									A    =       A    =       self.image_size // self.output_stride
									self.parent.assertEqual(
									    result.last_hidden_state.shape   ,				(self.batch_size, self.last_hidden_size, expected_height, expected_width)					)



		def        A_		(			self	:     List[Any]   ,				snake_case	:     str   ,				snake_case	:     Tuple   ,				snake_case	:     int   ,				snake_case	:     Union[str, Any]					)      ->    str:






									'''simple docstring'''

									A    =       self.num_labels
									A    =       TFMobileViTForImageClassification(snake_case					)
									A    =       model(snake_case   ,				labels=snake_case   ,				training=snake_case					)
									self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)					)



		def        A_		(			self	:     str   ,				snake_case	:     int   ,				snake_case	:     List[str]   ,				snake_case	:     int   ,				snake_case	:     Union[str, Any]					)      ->    List[str]:






									'''simple docstring'''

									A    =       self.num_labels
									A    =       TFMobileViTForSemanticSegmentation(snake_case					)
									A    =       A    =       self.image_size // self.output_stride

									A    =       model(snake_case   ,				training=snake_case					)
									self.parent.assertEqual(
									    result.logits.shape   ,				(self.batch_size, self.num_labels, expected_height, expected_width)					)

									A    =       model(snake_case   ,				labels=snake_case   ,				training=snake_case					)
									self.parent.assertEqual(
									    result.logits.shape   ,				(self.batch_size, self.num_labels, expected_height, expected_width)					)



		def        A_		(			self	:     Optional[Any]					)      ->    str:






									'''simple docstring'''

									A    =       self.prepare_config_and_inputs()
									A   ,	A   ,	A   ,	A    =       config_and_inputs
									A    =       {'pixel_values': pixel_values}
									return config, inputs_dict






@require_tf
class   UpperCAmelCase__		(   UpperCamelCase ,UpperCamelCase ,unittest.TestCase  ):
		lowerCAmelCase_							:						List[Any]							=  (
		    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
		    if is_tf_available()
		    else ()
		)
		lowerCAmelCase_							:						List[str]							=  (
		    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
		    if is_tf_available()
		    else {}
		)

		lowerCAmelCase_							:						str							=  False
		lowerCAmelCase_							:						List[str]							=  False
		lowerCAmelCase_							:						Union[str, Any]							=  False
		lowerCAmelCase_							:						Optional[int]							=  False
		lowerCAmelCase_							:						Union[str, Any]							=  False



		def        A_		(			self	:     List[str]					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       TFMobileViTModelTester(self					)
									A    =       TFMobileViTConfigTester(self   ,				config_class=snake_case   ,				has_text_modality=snake_case					)



		def        A_		(			self	:     Dict					)      ->    Any:






									'''simple docstring'''

									self.config_tester.run_common_tests()



		@unittest.skip(reason='MobileViT does not use inputs_embeds'					)
		def        A_		(			self	:     Optional[Any]					)      ->    Optional[int]:






									'''simple docstring'''

									pass



		@unittest.skip(reason='MobileViT does not support input and output embeddings'					)
		def        A_		(			self	:     List[Any]					)      ->    str:






									'''simple docstring'''

									pass



		@unittest.skip(reason='MobileViT does not output attentions'					)
		def        A_		(			self	:     Optional[int]					)      ->    str:






									'''simple docstring'''

									pass



		def        A_		(			self	:     Dict					)      ->    List[Any]:






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																A    =       model_class(snake_case					)
																A    =       inspect.signature(model.call					)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																A    =       [*signature.parameters.keys()]

																A    =       ['pixel_values']
																self.assertListEqual(arg_names[:1]   ,				snake_case					)



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






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case					)



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






									'''simple docstring'''

									def check_hidden_states_output(snake_case	:     List[str]   ,				snake_case	:     str   ,				snake_case	:     str					):
																A    =       model_class(snake_case					)

																A    =       model(**self._prepare_for_class(snake_case   ,				snake_case					)					)

																A    =       outputs.hidden_states

																A    =       5
																self.assertEqual(len(snake_case					)   ,				snake_case					)

																# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																# with the width and height being successively divided by 2.
																A    =       2
																for i in range(len(snake_case					)					):
																							self.assertListEqual(
																							    list(hidden_states[i].shape[-2:]					)   ,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]   ,				)
																							divisor *= 2

																self.assertEqual(self.model_tester.output_stride   ,				divisor // 2					)

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																A    =       True
																check_hidden_states_output(snake_case   ,				snake_case   ,				snake_case					)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																A    =       True

																check_hidden_states_output(snake_case   ,				snake_case   ,				snake_case					)



		def        A_		(			self	:     str					)      ->    str:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case					)



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






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*snake_case					)



		@unittest.skipIf(
		    not is_tf_available() or len(tf.config.list_physical_devices('GPU'					)					) == 0   ,				reason='TF does not support backprop for grouped convolutions on CPU.'   ,				)
		def        A_		(			self	:     Dict					)      ->    int:






									'''simple docstring'''

									super().test_dataset_conversion()



		def        A_		(			self	:     Optional[Any]   ,				snake_case	:     str   ,				snake_case	:     int   ,				snake_case	:     Tuple=2E-1   ,				snake_case	:     Optional[int]=2E-1					)      ->    Union[str, Any]:






									'''simple docstring'''

									self.assertTrue(np.allclose(snake_case   ,				snake_case   ,				atol=snake_case   ,				rtol=snake_case					)					)



		@unittest.skipIf(
		    not is_tf_available() or len(tf.config.list_physical_devices('GPU'					)					) == 0   ,				reason='TF does not support backprop for grouped convolutions on CPU.'   ,				)
		@slow
		def        A_		(			self	:     Dict					)      ->    Optional[int]:






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
																if model_class.__name__ != "TFMobileViTModel":
																							A    =       model_class(snake_case					)
																							if getattr(snake_case   ,				'hf_compute_loss'   ,				snake_case					):
																														super().test_keras_fit()



		def        A_		(			self	:     Union[str, Any]					)      ->    List[Any]:






									'''simple docstring'''

									A   ,	A    =       self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
																# set an ignore index to correctly test the masked loss used in
																# `TFMobileViTForSemanticSegmentation`.
																if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																							A    =       5

																A    =       model_class(snake_case					)
																if getattr(snake_case   ,				'hf_compute_loss'   ,				snake_case					):
																							# The number of elements in the loss should be the same as the number of elements in the label
																							A    =       self._prepare_for_class(inputs_dict.copy()   ,				snake_case   ,				return_labels=snake_case					)
																							A    =       prepared_for_class[
																							    sorted(prepared_for_class.keys() - inputs_dict.keys()   ,				reverse=snake_case					)[0]
																							]
																							A    =       added_label.shape.as_list()[:1]

																							# Test that model correctly compute the loss with kwargs
																							A    =       self._prepare_for_class(inputs_dict.copy()   ,				snake_case   ,				return_labels=snake_case					)
																							A    =       {'input_ids', 'pixel_values', 'input_features'}
																							A    =       possible_input_names.intersection(set(snake_case					)					).pop()
																							A    =       prepared_for_class.pop(snake_case					)

																							A    =       model(snake_case   ,				**snake_case					)[0]
																							self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

																							# Test that model correctly compute the loss when we mask some positions
																							A    =       self._prepare_for_class(inputs_dict.copy()   ,				snake_case   ,				return_labels=snake_case					)
																							A    =       {'input_ids', 'pixel_values', 'input_features'}
																							A    =       possible_input_names.intersection(set(snake_case					)					).pop()
																							A    =       prepared_for_class.pop(snake_case					)
																							if "labels" in prepared_for_class:
																														A    =       prepared_for_class['labels'].numpy()
																														if len(labels.shape					) > 1 and labels.shape[1] != 1:
																																					# labels[0] = -100
																																					A    =       tf.convert_to_tensor(snake_case					)
																																					A    =       model(snake_case   ,				**snake_case					)[0]
																																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)
																																					self.assertTrue(not np.any(np.isnan(loss.numpy()					)					)					)

                # Test that model correctly compute the loss with a dict
																							A    =       self._prepare_for_class(inputs_dict.copy()   ,				snake_case   ,				return_labels=snake_case					)
																							A    =       model(snake_case					)[0]
																							self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

																							# Test that model correctly compute the loss with a tuple
																							A    =       self._prepare_for_class(inputs_dict.copy()   ,				snake_case   ,				return_labels=snake_case					)

																							# Get keys that were added with the _prepare_for_class function
																							A    =       prepared_for_class.keys() - inputs_dict.keys()
																							A    =       inspect.signature(model.call					).parameters
																							A    =       list(signature.keys()					)

																							# Create a dictionary holding the location of the tensors in the tuple
																							A    =       {0: input_name}
																							for label_key in label_keys:
																														A    =       signature_names.index(snake_case					)
																														A    =       label_key
																							A    =       sorted(tuple_index_mapping.items()					)
																							# Initialize a list with their default values, update the values and convert to a tuple
																							A    =       []

																							for name in signature_names:
																														if name != "kwargs":
																																					list_input.append(signature[name].default					)

																							for index, value in sorted_tuple_index_mapping:
																														A    =       prepared_for_class[value]

																							A    =       tuple(snake_case					)

																							# Send to model
																							A    =       model(tuple_input[:-1]					)[0]

																							self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)



		@slow
		def        A_		(			self	:     List[Any]					)      ->    str:






									'''simple docstring'''

									for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																A    =       TFMobileViTModel.from_pretrained(snake_case					)
																self.assertIsNotNone(snake_case					)



def 	lowerCAmelCase__      (  )			->				Tuple:
							A    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							return image






@require_tf
class   UpperCAmelCase__		(   unittest.TestCase  ):



		@slow
		def        A_		(			self	:     Any					)      ->    int:






									'''simple docstring'''

									A    =       TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'					)

									A    =       MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'					)
									A    =       prepare_img()
									A    =       image_processor(images=snake_case   ,				return_tensors='tf'					)

									# forward pass
									A    =       model(**snake_case   ,				training=snake_case					)

									# verify the logits
									A    =       tf.TensorShape((1, 1_000)					)
									self.assertEqual(outputs.logits.shape   ,				snake_case					)

									A    =       tf.constant([-1.9364, -1.2327, -0.4653]					)

									tf.debugging.assert_near(outputs.logits[0, :3]   ,				snake_case   ,				atol=1E-4   ,				rtol=1E-0_4					)



		@slow
		def        A_		(			self	:     str					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'					)

									A    =       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'					)

									A    =       prepare_img()
									A    =       image_processor(images=snake_case   ,				return_tensors='tf'					)

									# forward pass
									A    =       model(inputs.pixel_values   ,				training=snake_case					)
									A    =       outputs.logits

									# verify the logits
									A    =       tf.TensorShape((1, 21, 32, 32)					)
									self.assertEqual(logits.shape   ,				snake_case					)

									A    =       tf.constant(
									    [
									        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
									        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
									        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
									    ]					)

									tf.debugging.assert_near(logits[0, :3, :3, :3]   ,				snake_case   ,				rtol=1E-4   ,				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 checkpointing 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     =      1_6
A     =      3_2

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__ = 16      )			->				List[Any]:
							A    =       AutoTokenizer.from_pretrained('bert-base-cased'      )
							A    =       load_dataset('glue'					,   'mrpc'      )

							def tokenize_function(lowerCamelCase__      ):
														# max_length=None => use the model max length (it's actually the default)
														A    =       tokenizer(examples['sentence1']					,   examples['sentence2']					,   truncation=lowerCamelCase__					,   max_length=lowerCamelCase__      )
														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():
														A    =       datasets.map(
														    lowerCamelCase__					,   batched=lowerCamelCase__					,   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
							A    =       tokenized_datasets.rename_column('label'					,   'labels'      )

							def collate_fn(lowerCamelCase__      ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														A    =       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":
																					A    =       16
														elif accelerator.mixed_precision != "no":
																					A    =       8
														else:
																					A    =       None

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

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

							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     =      mocked_dataloaders  # noqa: F811

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				List[str]:
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'					,   lowerCamelCase__      ) == "1":
														A    =       2
							# Initialize accelerator
							A    =       Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision      )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							A    =       config['lr']
							A    =       int(config['num_epochs']      )
							A    =       int(config['seed']      )
							A    =       int(config['batch_size']      )

							# New Code #
							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps					,   'isdigit'      ):
														if args.checkpointing_steps == "epoch":
																					A    =       args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A    =       int(args.checkpointing_steps      )
														else:
																					raise ValueError(
																					    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
							else:
														A    =       None

							set_seed(lowerCamelCase__      )

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

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

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

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

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

							# Instantiate scheduler
							A    =       get_linear_schedule_with_warmup(
							    optimizer=lowerCamelCase__					,   num_warmup_steps=100					,   num_training_steps=(len(lowerCamelCase__      ) * 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.
							A   ,	A   ,	A   ,	A   ,	A    =       accelerator.prepare(
							    lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )

							# New Code #
							# We need to keep track of how many total steps we have iterated over
							A    =       0
							# We also need to keep track of the stating epoch so files are named properly
							A    =       0

							# We need to load the checkpoint back in before training here with `load_state`
							# The total number of epochs is adjusted based on where the state is being loaded from,
							# as we assume continuation of the same training script
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""      )
																					accelerator.load_state(args.resume_from_checkpoint      )
																					A    =       os.path.basename(args.resume_from_checkpoint      )
														else:
																					# Get the most recent checkpoint
																					A    =       [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime      )
																					A    =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														A    =       os.path.splitext(lowerCamelCase__      )[0]

														if "epoch" in training_difference:
																					A    =       int(training_difference.replace('epoch_'					,   ''      )      ) + 1
																					A    =       None
														else:
																					A    =       int(training_difference.replace('step_'					,   ''      )      )
																					A    =       resume_step // len(lowerCamelCase__      )
																					resume_step -= starting_epoch * len(lowerCamelCase__      )

    # Now we train the model
							for epoch in range(lowerCamelCase__					,   lowerCamelCase__      ):
														model.train()
														# New Code #
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					A    =       accelerator.skip_first_batches(lowerCamelCase__					,   lowerCamelCase__      )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					A    =       train_dataloader
														for step, batch in enumerate(lowerCamelCase__      ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device      )
																					A    =       model(**lowerCamelCase__      )
																					A    =       outputs.loss
																					A    =       loss / gradient_accumulation_steps
																					accelerator.backward(lowerCamelCase__      )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()
																					# New Code #
																					overall_step += 1

																					# New Code #
																					# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																					# These are saved to folders named `step_{overall_step}`
																					# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																					# If mixed precision was used, will also save a "scalar.bin" file
																					if isinstance(lowerCamelCase__					,   lowerCamelCase__      ):
																												A    =       f"""step_{overall_step}"""
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										A    =       os.path.join(args.output_dir					,   lowerCamelCase__      )
																																			accelerator.save_state(lowerCamelCase__      )

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

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

														# New Code #
														# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
														# These are saved to folders named `epoch_{epoch}`
														# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
														# If mixed precision was used, will also save a "scalar.bin" file
														if checkpointing_steps == "epoch":
																					A    =       f"""epoch_{epoch}"""
																					if args.output_dir is not None:
																												A    =       os.path.join(args.output_dir					,   lowerCamelCase__      )
																					accelerator.save_state(lowerCamelCase__      )

def 	lowerCAmelCase__      (  )			->				Optional[int]:
							A    =       argparse.ArgumentParser(description='Simple example of training script.'      )
							parser.add_argument(
							    '--mixed_precision'					,   type=lowerCamelCase__					,   default=lowerCamelCase__					,   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(
							    '--checkpointing_steps'					,   type=lowerCamelCase__					,   default=lowerCamelCase__					,   help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'					,   )
							parser.add_argument(
							    '--output_dir'					,   type=lowerCamelCase__					,   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=lowerCamelCase__					,   default=lowerCamelCase__					,   help='If the training should continue from a checkpoint folder.'					,   )
							A    =       parser.parse_args()
							A    =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(lowerCamelCase__					,   lowerCamelCase__      )


if __name__ == "__main__":
							main()



