



import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    MODEL_MAPPING,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				)
				from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				import PIL
				from PIL import Image

				from transformers import BeitImageProcessor






class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=100     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=[0, 1, 2, 3]     ,	)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			parent
													__UpperCamelCase       =			100
													__UpperCamelCase       =			batch_size
													__UpperCamelCase       =			image_size
													__UpperCamelCase       =			patch_size
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			is_training
													__UpperCamelCase       =			use_labels
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			type_sequence_label_size
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			scope
													__UpperCamelCase       =			out_indices
													__UpperCamelCase       =			num_labels

													# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
													__UpperCamelCase       =			(image_size // patch_size) ** 2
													__UpperCamelCase       =			num_patches + 1



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






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

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

													__UpperCamelCase       =			self.get_config()

													return config, pixel_values, labels, pixel_labels



							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													return BeitConfig(
													    vocab_size=self.vocab_size     ,	image_size=self.image_size     ,	patch_size=self.patch_size     ,	num_channels=self.num_channels     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	is_decoder=_lowercase     ,	initializer_range=self.initializer_range     ,	out_indices=self.out_indices     ,	)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			BeitModel(config=_lowercase			)
													model.to(_lowercase			)
													model.eval()
													__UpperCamelCase       =			model(_lowercase			)
													self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			BeitForMaskedImageModeling(config=_lowercase			)
													model.to(_lowercase			)
													model.eval()
													__UpperCamelCase       =			model(_lowercase			)
													self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length - 1, self.vocab_size)			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			self.type_sequence_label_size
													__UpperCamelCase       =			BeitForImageClassification(_lowercase			)
													model.to(_lowercase			)
													model.eval()
													__UpperCamelCase       =			model(_lowercase     ,	labels=_lowercase			)
													self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)			)

													# test greyscale images
													__UpperCamelCase       =			1
													__UpperCamelCase       =			BeitForImageClassification(_lowercase			)
													model.to(_lowercase			)
													model.eval()

													__UpperCamelCase       =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
													__UpperCamelCase       =			model(_lowercase     ,	labels=_lowercase			)
													self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.num_labels
													__UpperCamelCase       =			BeitForSemanticSegmentation(_lowercase			)
													model.to(_lowercase			)
													model.eval()
													__UpperCamelCase       =			model(_lowercase			)
													self.parent.assertEqual(
													    result.logits.shape     ,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)
													__UpperCamelCase       =			model(_lowercase     ,	labels=_lowercase			)
													self.parent.assertEqual(
													    result.logits.shape     ,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.prepare_config_and_inputs()
													__UpperCamelCase       =			config_and_inputs
													__UpperCamelCase       =			{"""pixel_values""": pixel_values}
													return config, inputs_dict






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE__   ,		SCREAMING_SNAKE_CASE__   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  (
							    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							_snake_case     		=  (
							    {
							        'feature-extraction': BeitModel,
							        'image-classification': BeitForImageClassification,
							        'image-segmentation': BeitForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			BeitModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=_lowercase     ,	has_text_modality=_lowercase     ,	hidden_size=37			)



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





													'''simple docstring'''






													self.config_tester.run_common_tests()



							@unittest.skip(reason='''BEiT does not use inputs_embeds'''			)
							def     A__		(  self			)->   int:





													'''simple docstring'''






													pass



							@require_torch_multi_gpu
							@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''			)
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													pass



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(_lowercase			)
																			self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)			)
																			__UpperCamelCase       =			model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(_lowercase     ,	nn.Linear			)			)



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





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

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

																			__UpperCamelCase       =			["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]     ,	_lowercase			)



							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_lowercase			)



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





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*_lowercase			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*_lowercase			)



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





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase			)



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





													'''simple docstring'''






													if not self.model_tester.is_training:
																			return

													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCamelCase       =			True

													for model_class in self.all_model_classes:
																			# we don't test BeitForMaskedImageModeling
																			if model_class in [*get_values(_lowercase			), BeitForMaskedImageModeling]:
																									continue

																			__UpperCamelCase       =			model_class(_lowercase			)
																			model.to(_lowercase			)
																			model.train()
																			__UpperCamelCase       =			self._prepare_for_class(_lowercase     ,	_lowercase     ,	return_labels=_lowercase			)
																			__UpperCamelCase       =			model(**_lowercase			).loss
																			loss.backward()



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





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()
													if not self.model_tester.is_training:
																			return

													__UpperCamelCase       =			False
													__UpperCamelCase       =			True

													for model_class in self.all_model_classes:
																			# we don't test BeitForMaskedImageModeling
																			if (
																			    model_class in [*get_values(_lowercase			), BeitForMaskedImageModeling]
																			    or not model_class.supports_gradient_checkpointing
																			):
																									continue

																			__UpperCamelCase       =			model_class(_lowercase			)
																			model.gradient_checkpointing_enable()
																			model.to(_lowercase			)
																			model.train()
																			__UpperCamelCase       =			self._prepare_for_class(_lowercase     ,	_lowercase     ,	return_labels=_lowercase			)
																			__UpperCamelCase       =			model(**_lowercase			).loss
																			loss.backward()



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase       =			_config_zero_init(_lowercase			)
													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(config=_lowercase			)
																			for name, param in model.named_parameters():
																									# we skip lambda parameters as these require special initial values
																									# determined by config.layer_scale_init_value
																									if "lambda" in name:
																															continue
																									if param.requires_grad:
																															self.assertIn(
																															    ((param.data.mean() * 1E9).round() / 1E9).item()     ,	[0.0, 1.0]     ,	msg=F"Parameter {name} of model {model_class} seems not properly initialized"     ,	)



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





													'''simple docstring'''






													for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCamelCase       =			BeitModel.from_pretrained(_lowercase			)
																			self.assertIsNotNone(_lowercase			)





def 	A_ (							)      ->    List[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
						return image






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							@cached_property
							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''			) if is_vision_available() else None



							@slow
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''			).to(_lowercase			)

													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(images=_lowercase     ,	return_tensors='''pt'''			).pixel_values.to(_lowercase			)

													# prepare bool_masked_pos
													__UpperCamelCase       =			torch.ones((1, 196)     ,	dtype=torch.bool			).to(_lowercase			)

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       =			model(pixel_values=_lowercase     ,	bool_masked_pos=_lowercase			)
													__UpperCamelCase       =			outputs.logits

													# verify the logits
													__UpperCamelCase       =			torch.Size((1, 196, 8192)			)
													self.assertEqual(logits.shape     ,	_lowercase			)

													__UpperCamelCase       =			torch.tensor(
													    [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]			).to(_lowercase			)

													self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,	_lowercase     ,	atol=1E-2			)			)



							@slow
							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''			).to(_lowercase			)

													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(images=_lowercase     ,	return_tensors='''pt'''			).to(_lowercase			)

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       =			model(**_lowercase			)
													__UpperCamelCase       =			outputs.logits

													# verify the logits
													__UpperCamelCase       =			torch.Size((1, 1000)			)
													self.assertEqual(logits.shape     ,	_lowercase			)

													__UpperCamelCase       =			torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			).to(_lowercase			)

													self.assertTrue(torch.allclose(logits[0, :3]     ,	_lowercase     ,	atol=1E-4			)			)

													__UpperCamelCase       =			281
													self.assertEqual(logits.argmax(-1			).item()     ,	_lowercase			)



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





													'''simple docstring'''






													__UpperCamelCase       =			BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''			).to(
													    _lowercase			)

													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(images=_lowercase     ,	return_tensors='''pt'''			).to(_lowercase			)

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       =			model(**_lowercase			)
													__UpperCamelCase       =			outputs.logits

													# verify the logits
													__UpperCamelCase       =			torch.Size((1, 21841)			)
													self.assertEqual(logits.shape     ,	_lowercase			)

													__UpperCamelCase       =			torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			).to(_lowercase			)

													self.assertTrue(torch.allclose(logits[0, :3]     ,	_lowercase     ,	atol=1E-4			)			)

													__UpperCamelCase       =			2396
													self.assertEqual(logits.argmax(-1			).item()     ,	_lowercase			)



							@slow
							def     A__		(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''			)
													__UpperCamelCase       =			model.to(_lowercase			)

													__UpperCamelCase       =			BeitImageProcessor(do_resize=_lowercase     ,	size=640     ,	do_center_crop=_lowercase			)

													__UpperCamelCase       =			load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,	split='''test'''			)
													__UpperCamelCase       =			Image.open(ds[0]['''file''']			)
													__UpperCamelCase       =			image_processor(images=_lowercase     ,	return_tensors='''pt'''			).to(_lowercase			)

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       =			model(**_lowercase			)
													__UpperCamelCase       =			outputs.logits

													# verify the logits
													__UpperCamelCase       =			torch.Size((1, 150, 160, 160)			)
													self.assertEqual(logits.shape     ,	_lowercase			)

													__UpperCamelCase       =			version.parse(PIL.__version__			) < version.parse('''9.0.0'''			)

													if is_pillow_less_than_a:
																			__UpperCamelCase       =			torch.tensor(
																			    [
																			        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
																			        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
																			        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
																			    ]     ,	device=_lowercase     ,	)
													else:
																			__UpperCamelCase       =			torch.tensor(
																			    [
																			        [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
																			        [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
																			        [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
																			    ]     ,	device=_lowercase     ,	)

													self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,	_lowercase     ,	atol=1E-4			)			)



							@slow
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''			)
													__UpperCamelCase       =			model.to(_lowercase			)

													__UpperCamelCase       =			BeitImageProcessor(do_resize=_lowercase     ,	size=640     ,	do_center_crop=_lowercase			)

													__UpperCamelCase       =			load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,	split='''test'''			)
													__UpperCamelCase       =			Image.open(ds[0]['''file''']			)
													__UpperCamelCase       =			image_processor(images=_lowercase     ,	return_tensors='''pt'''			).to(_lowercase			)

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       =			model(**_lowercase			)

													__UpperCamelCase       =			outputs.logits.detach().cpu()

													__UpperCamelCase       =			image_processor.post_process_semantic_segmentation(outputs=_lowercase     ,	target_sizes=[(500, 300)]			)
													__UpperCamelCase       =			torch.Size((500, 300)			)
													self.assertEqual(segmentation[0].shape     ,	_lowercase			)

													__UpperCamelCase       =			image_processor.post_process_semantic_segmentation(outputs=_lowercase			)
													__UpperCamelCase       =			torch.Size((160, 160)			)
													self.assertEqual(segmentation[0].shape     ,	_lowercase			)






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__							:							Union[str, Any]									=			1_6
lowercase__							:							Tuple									=			3_2




def 	A_ (							snake_case							:   Accelerator ,      snake_case							:   int = 16 )      ->    List[Any]:



						'''simple docstring'''

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

						def tokenize_function(snake_case							:   Tuple ):
												# max_length=None => use the model max length (it's actually the default)
												__UpperCamelCase       =			tokenizer(examples['''sentence1'''] ,      examples['''sentence2'''] ,      truncation=snake_case ,      max_length=snake_case )
												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(
												    snake_case ,      batched=snake_case ,      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(snake_case							:   Union[str, Any] ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__UpperCamelCase       =			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":
																		__UpperCamelCase       =			16
												elif accelerator.mixed_precision != "no":
																		__UpperCamelCase       =			8
												else:
																		__UpperCamelCase       =			None

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

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

						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__							:							List[str]									=			mocked_dataloaders  # noqa: F811




def 	A_ (							snake_case							:   str ,      snake_case							:   Optional[Any] )      ->    Optional[int]:



						'''simple docstring'''

						if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,      snake_case ) == "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(snake_case )

						__UpperCamelCase  ,							__UpperCamelCase       =			get_dataloaders(snake_case ,      snake_case )
						__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=snake_case )

						# 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=snake_case )

						# Instantiate scheduler
						__UpperCamelCase       =			get_linear_schedule_with_warmup(
						    optimizer=snake_case ,      num_warmup_steps=100 ,      num_training_steps=(len(snake_case ) * 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(
						    snake_case ,      snake_case ,      snake_case ,      snake_case ,      snake_case )

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

						# Now we train the model
						for epoch in range(snake_case ):
												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(snake_case ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device )
																		__UpperCamelCase       =			model(**snake_case )
																		__UpperCamelCase       =			outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		__UpperCamelCase       =			loss / gradient_accumulation_steps
																		accelerator.backward(snake_case )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(snake_case ):
																		# 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(**snake_case )
																		__UpperCamelCase       =			outputs.logits.argmax(dim=-1 )
																		__UpperCamelCase  ,							__UpperCamelCase       =			accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																		metric.add_batch(
																		    predictions=snake_case ,      references=snake_case ,      )

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

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

    # 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 	A_ (							)      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			argparse.ArgumentParser(description='''Simple example of training script.''' )
						parser.add_argument(
						    '''--mixed_precision''' ,      type=snake_case ,      default=snake_case ,      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=snake_case ,      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(snake_case ,      snake_case )


if __name__ == "__main__":
				main()




