




import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_MAPPING,
					    SegformerForImageClassification,
					    SegformerForSemanticSegmentation,
					    SegformerModel,
					)
					from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import SegformerImageProcessor


class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.config_class(**self.inputs_dict	)
				self.parent.assertTrue(hasattr(A_    ,				'hidden_sizes'	)	)
				self.parent.assertTrue(hasattr(A_    ,				'num_attention_heads'	)	)
				self.parent.assertTrue(hasattr(A_    ,				'num_encoder_blocks'	)	)






class     lowercase :



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=64    ,				A_=3    ,				A_=4    ,				A_=[2, 2, 2, 2]    ,				A_=[8, 4, 2, 1]    ,				A_=[16, 32, 64, 128]    ,				A_=[1, 4, 8, 16]    ,				A_=[1, 2, 4, 8]    ,				A_=True    ,				A_=True    ,				A_="gelu"    ,				A_=0.1    ,				A_=0.1    ,				A_=0.02    ,				A_=3    ,				A_=None    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					image_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					num_encoder_blocks
				UpperCamelCase          =					sr_ratios
				UpperCamelCase          =					depths
				UpperCamelCase          =					hidden_sizes
				UpperCamelCase          =					downsampling_rates
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					scope



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

				UpperCamelCase          =					self.get_config()
				return config, pixel_values, labels



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				return SegformerConfig(
				    image_size=self.image_size    ,				num_channels=self.num_channels    ,				num_encoder_blocks=self.num_encoder_blocks    ,				depths=self.depths    ,				hidden_sizes=self.hidden_sizes    ,				num_attention_heads=self.num_attention_heads    ,				hidden_act=self.hidden_act    ,				hidden_dropout_prob=self.hidden_dropout_prob    ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,				initializer_range=self.initializer_range    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					SegformerModel(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_	)
				UpperCamelCase          =					UpperCamelCase          =					self.image_size // (self.downsampling_rates[-1] * 2)
				self.parent.assertEqual(
				    result.last_hidden_state.shape    ,				(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					SegformerForSemanticSegmentation(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(
				    result.logits.shape    ,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)	)
				UpperCamelCase          =					model(A_    ,				labels=A_	)
				self.parent.assertEqual(
				    result.logits.shape    ,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)	)
				self.parent.assertGreater(result.loss    ,				0.0	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					1
				UpperCamelCase          =					SegformerForSemanticSegmentation(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					torch.randint(0    ,				1    ,				(self.batch_size, self.image_size, self.image_size)	).to(A_	)
				UpperCamelCase          =					model(A_    ,				labels=A_	)
				self.parent.assertGreater(result.loss    ,				0.0	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					config_and_inputs
				UpperCamelCase          =					{'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	List[str]     					=    (
		    (
		        SegformerModel,
		        SegformerForSemanticSegmentation,
		        SegformerForImageClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		__lowercase	:	Optional[Any]     					=    (
		    {
		        "feature-extraction": SegformerModel,
		        "image-classification": SegformerForImageClassification,
		        "image-segmentation": SegformerForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		__lowercase	:	List[str]     					=    True
		__lowercase	:	int     					=    False
		__lowercase	:	str     					=    False
		__lowercase	:	Optional[int]     					=    False



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					SegformerModelTester(self	)
				UpperCamelCase          =					SegformerConfigTester(self    ,				config_class=A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_binary_image_segmentation(*A_	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_segmentation(*A_	)



		@unittest.skip('SegFormer does not use inputs_embeds'	)
		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				pass



		@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'	)
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(A_	)
						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]    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					True

				for model_class in self.all_model_classes:
						UpperCamelCase          =					True
						UpperCamelCase          =					False
						UpperCamelCase          =					True
						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.eval()
						with torch.no_grad():
								UpperCamelCase          =					model(**self._prepare_for_class(A_    ,				A_	)	)
						UpperCamelCase          =					outputs.attentions

						UpperCamelCase          =					sum(self.model_tester.depths	)
						self.assertEqual(len(A_	)    ,				A_	)

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						UpperCamelCase          =					True
						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.eval()
						with torch.no_grad():
								UpperCamelCase          =					model(**self._prepare_for_class(A_    ,				A_	)	)
						UpperCamelCase          =					outputs.attentions

						self.assertEqual(len(A_	)    ,				A_	)

						# verify the first attentions (first block, first layer)
						UpperCamelCase          =					(self.model_tester.image_size // 4) ** 2
						UpperCamelCase          =					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
						self.assertListEqual(
						    list(attentions[0].shape[-3:]	)    ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]    ,				)

						# verify the last attentions (last block, last layer)
						UpperCamelCase          =					(self.model_tester.image_size // 32) ** 2
						UpperCamelCase          =					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
						self.assertListEqual(
						    list(attentions[-1].shape[-3:]	)    ,				[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]    ,				)
						UpperCamelCase          =					len(A_	)

						# Check attention is always last and order is fine
						UpperCamelCase          =					True
						UpperCamelCase          =					True
						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.eval()
						with torch.no_grad():
								UpperCamelCase          =					model(**self._prepare_for_class(A_    ,				A_	)	)

						self.assertEqual(out_len + 1    ,				len(A_	)	)

						UpperCamelCase          =					outputs.attentions

						self.assertEqual(len(A_	)    ,				A_	)
						# verify the first attentions (first block, first layer)
						UpperCamelCase          =					(self.model_tester.image_size // 4) ** 2
						UpperCamelCase          =					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
						self.assertListEqual(
						    list(self_attentions[0].shape[-3:]	)    ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]    ,				)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				def check_hidden_states_output(A_    ,				A_    ,				A_	):
						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.eval()

						with torch.no_grad():
								UpperCamelCase          =					model(**self._prepare_for_class(A_    ,				A_	)	)

						UpperCamelCase          =					outputs.hidden_states

						UpperCamelCase          =					self.model_tester.num_encoder_blocks
						self.assertEqual(len(A_	)    ,				A_	)

						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-3:]	)    ,				[
						        self.model_tester.hidden_sizes[0],
						        self.model_tester.image_size // 4,
						        self.model_tester.image_size // 4,
						    ]    ,				)

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					True
						check_hidden_states_output(A_    ,				A_    ,				A_	)

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

						check_hidden_states_output(A_    ,				A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				if not self.model_tester.is_training:
						return

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					True

				for model_class in self.all_model_classes:
						if model_class in get_values(A_	):
								continue

						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.train()
						UpperCamelCase          =					self._prepare_for_class(A_    ,				A_    ,				return_labels=A_	)
						UpperCamelCase          =					model(**A_	).loss
						loss.backward()



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					SegformerModel.from_pretrained(A_	)
						self.assertIsNotNone(A_	)






def 				A	( )       ->  List[str]:
		'''simple docstring'''

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


@require_torch
class     lowercase (		unittest.TestCase       ):



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					SegformerImageProcessor(
				    image_scale=(512, 512)    ,				keep_ratio=A_    ,				align=A_    ,				do_random_crop=A_	)
				UpperCamelCase          =					SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'	).to(
				    A_	)

				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='pt'	)
				UpperCamelCase          =					encoded_inputs.pixel_values.to(A_	)

				with torch.no_grad():
						UpperCamelCase          =					model(A_	)

				UpperCamelCase          =					torch.Size((1, model.config.num_labels, 128, 128)	)
				self.assertEqual(outputs.logits.shape    ,				A_	)

				UpperCamelCase          =					torch.tensor(
				    [
				        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
				        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
				        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
				    ]	).to(A_	)
				self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,				A_    ,				atol=1e-4	)	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					SegformerImageProcessor(
				    image_scale=(512, 512)    ,				keep_ratio=A_    ,				align=A_    ,				do_random_crop=A_	)
				UpperCamelCase          =					SegformerForSemanticSegmentation.from_pretrained(
				    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'	).to(A_	)

				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='pt'	)
				UpperCamelCase          =					encoded_inputs.pixel_values.to(A_	)

				with torch.no_grad():
						UpperCamelCase          =					model(A_	)

				UpperCamelCase          =					torch.Size((1, model.config.num_labels, 128, 128)	)
				self.assertEqual(outputs.logits.shape    ,				A_	)

				UpperCamelCase          =					torch.tensor(
				    [
				        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
				        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
				        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
				    ]	).to(A_	)
				self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,				A_    ,				atol=1e-1	)	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					SegformerImageProcessor(
				    image_scale=(512, 512)    ,				keep_ratio=A_    ,				align=A_    ,				do_random_crop=A_	)
				UpperCamelCase          =					SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'	).to(
				    A_	)

				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='pt'	)
				UpperCamelCase          =					encoded_inputs.pixel_values.to(A_	)

				with torch.no_grad():
						UpperCamelCase          =					model(A_	)

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

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

				UpperCamelCase          =					image_processor.post_process_semantic_segmentation(outputs=A_	)
				UpperCamelCase          =					torch.Size((128, 128)	)
				self.assertEqual(segmentation[0].shape    ,				A_	)


import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase :			Union[str, Any]              =	16
_UpperCAmelCase :			Dict              =	32


def 				A	( lowercase   ,	lowercase = 16			)       ->  str:
		'''simple docstring'''

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

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

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				UpperCamelCase          =					datasets.map(
				    lowercase   ,	batched=lowercase   ,	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(lowercase			):
				# 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(
				    lowercase   ,	padding='longest'   ,	max_length=lowercase   ,	pad_to_multiple_of=lowercase   ,	return_tensors='pt'   ,	)

		# Instantiate dataloaders.
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['train']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=lowercase			)
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['validation']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=(accelerator.mixed_precision == 'fp8')   ,	)

		return train_dataloader, eval_dataloader


def 				A	( lowercase   ,	lowercase			)       ->  Optional[Any]:
		'''simple docstring'''

		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']			)

		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

		set_seed(lowercase			)
		UpperCamelCase     ,  UpperCamelCase          =					get_dataloaders(lowercase   ,	lowercase			)
		# 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=lowercase			)

		# 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=lowercase			)

		# Instantiate scheduler
		UpperCamelCase          =					get_linear_schedule_with_warmup(
		    optimizer=lowercase   ,	num_warmup_steps=100   ,	num_training_steps=(len(lowercase			) * 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(
		    lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)

		# Now we train the model
		for epoch in range(lowercase			):
				model.train()
				for step, batch in enumerate(lowercase			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device			)
						UpperCamelCase          =					model(**lowercase			)
						UpperCamelCase          =					outputs.loss
						UpperCamelCase          =					loss / gradient_accumulation_steps
						accelerator.backward(lowercase			)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

				UpperCamelCase          =					metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''   ,	lowercase			)


def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					argparse.ArgumentParser(description='Simple example of training script.'			)
		parser.add_argument(
		    '--mixed_precision'   ,	type=lowercase   ,	default=lowercase   ,	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.'			)
		UpperCamelCase          =					parser.parse_args()
		UpperCamelCase          =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(lowercase   ,	lowercase			)


if __name__ == "__main__":
					main()
