




import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class        __snake_case  (_a						,	_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     AutoencoderKL
				lowerCAmelCase__				=     "sample"
				lowerCAmelCase__				=     1e-2



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      4
								_lowerCAmelCase    :    Dict										=      3
								_lowerCAmelCase    :    Dict										=      (32, 32)

								_lowerCAmelCase    :    Union[str, Any]										=      floats_tensor((batch_size, num_channels) + sizes      ).to(_UpperCAmelCase      )

								return {"sample": image}



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Dict:





								'''simple docstring'''




								return (3, 32, 32)



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




								return (3, 32, 32)



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      {
								    """block_out_channels""": [32, 64],
								    """in_channels""": 3,
								    """out_channels""": 3,
								    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
								    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
								    """latent_channels""": 4,
								}
								_lowerCAmelCase    :    Union[str, Any]										=      self.dummy_input
								return init_dict, inputs_dict



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Optional[int]:





								'''simple docstring'''




								pass



				@unittest.skipIf(torch_device == """mps"""		,	"""Gradient checkpointing skipped on MPS"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      self.prepare_init_args_and_inputs_for_common()
								_lowerCAmelCase    :    Any										=      self.model_class(**_UpperCAmelCase      )
								model.to(_UpperCAmelCase      )

								assert not model.is_gradient_checkpointing and model.training

								_lowerCAmelCase    :    Dict										=      model(**_UpperCAmelCase      ).sample
								# run the backwards pass on the model. For backwards pass, for simplicity purpose,
								# we won't calculate the loss and rather backprop on out.sum()
								model.zero_grad()

								_lowerCAmelCase    :    List[str]										=      torch.randn_like(_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      (out - labels).mean()
								loss.backward()

								# re-instantiate the model now enabling gradient checkpointing
								_lowerCAmelCase    :    Optional[int]										=      self.model_class(**_UpperCAmelCase      )
								# clone model
								model_a.load_state_dict(model.state_dict()      )
								model_a.to(_UpperCAmelCase      )
								model_a.enable_gradient_checkpointing()

								assert model_a.is_gradient_checkpointing and model_a.training

								_lowerCAmelCase    :    Any										=      model_a(**_UpperCAmelCase      ).sample
								# run the backwards pass on the model. For backwards pass, for simplicity purpose,
								# we won't calculate the loss and rather backprop on out.sum()
								model_a.zero_grad()
								_lowerCAmelCase    :    str										=      (out_a - labels).mean()
								loss_a.backward()

								# compare the output and parameters gradients
								self.assertTrue((loss - loss_a).abs() < 1E-5      )
								_lowerCAmelCase    :    List[Any]										=      dict(model.named_parameters()      )
								_lowerCAmelCase    :    Optional[int]										=      dict(model_a.named_parameters()      )
								for name, param in named_params.items():
												self.assertTrue(torch_all_close(param.grad.data		,	named_params_a[name].grad.data		,	atol=5E-5      )      )



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





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""		,	output_loading_info=_UpperCAmelCase      )
								self.assertIsNotNone(_UpperCAmelCase      )
								self.assertEqual(len(loading_info["""missing_keys"""]      )		,	0      )

								model.to(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      model(**self.dummy_input      )

								assert image is not None, "Make sure output is not None"



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""      )
								_lowerCAmelCase    :    Dict										=      model.to(_UpperCAmelCase      )
								model.eval()

								if torch_device == "mps":
												_lowerCAmelCase    :    Tuple										=      torch.manual_seed(0      )
								else:
												_lowerCAmelCase    :    Tuple										=      torch.Generator(device=_UpperCAmelCase      ).manual_seed(0      )

								_lowerCAmelCase    :    Any										=      torch.randn(
								    1		,	model.config.in_channels		,	model.config.sample_size		,	model.config.sample_size		,	generator=torch.manual_seed(0      )		,	)
								_lowerCAmelCase    :    Optional[int]										=      image.to(_UpperCAmelCase      )
								with torch.no_grad():
												_lowerCAmelCase    :    Tuple										=      model(_UpperCAmelCase		,	sample_posterior=_UpperCAmelCase		,	generator=_UpperCAmelCase      ).sample

								_lowerCAmelCase    :    int										=      output[0, -1, -3:, -3:].flatten().cpu()

								# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
								# the expected output slices are not the same for CPU and GPU.
								if torch_device == "mps":
												_lowerCAmelCase    :    Optional[Any]										=      torch.tensor(
												    [
												        -4.0_078E-01,
												        -3.8_323E-04,
												        -1.2_681E-01,
												        -1.1_462E-01,
												        2.0_095E-01,
												        1.0_893E-01,
												        -8.8_247E-02,
												        -3.0_361E-01,
												        -9.8_644E-03,
												    ]      )
								elif torch_device == "cpu":
												_lowerCAmelCase    :    Optional[Any]										=      torch.tensor(
												    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]      )
								else:
												_lowerCAmelCase    :    Tuple										=      torch.tensor(
												    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]      )

								self.assertTrue(torch_all_close(_UpperCAmelCase		,	_UpperCAmelCase		,	rtol=1E-2      )      )






@slow
class        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								return f"gaussian_noise_s={seed}_shape={'_'.join([str(_UpperCAmelCase      ) for s in shape]      )}.npy"



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





								'''simple docstring'''




								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Optional[int]=0		,	_UpperCAmelCase      :		List[str]=(4, 3, 512, 512)		,	_UpperCAmelCase      :		List[Any]=False      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      torch.floataa if fpaa else torch.floataa
								_lowerCAmelCase    :    int										=      torch.from_numpy(load_hf_numpy(self.get_file_format(_UpperCAmelCase		,	_UpperCAmelCase      )      )      ).to(_UpperCAmelCase      ).to(_UpperCAmelCase      )
								return image



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Union[str, Any]="CompVis/stable-diffusion-v1-4"		,	_UpperCAmelCase      :		Union[str, Any]=False      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      """fp16""" if fpaa else None
								_lowerCAmelCase    :    Dict										=      torch.floataa if fpaa else torch.floataa

								_lowerCAmelCase    :    Optional[Any]										=      AutoencoderKL.from_pretrained(
								    _UpperCAmelCase		,	subfolder="""vae"""		,	torch_dtype=_UpperCAmelCase		,	revision=_UpperCAmelCase		,	)
								model.to(_UpperCAmelCase      ).eval()

								return model



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		Dict=0      )  ->    List[str]:





								'''simple docstring'''




								if torch_device == "mps":
												return torch.manual_seed(_UpperCAmelCase      )
								return torch.Generator(device=_UpperCAmelCase      ).manual_seed(_UpperCAmelCase      )



				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		str      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      self.get_sd_vae_model()
								_lowerCAmelCase    :    Optional[Any]										=      self.get_sd_image(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.get_generator(_UpperCAmelCase      )

								with torch.no_grad():
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	generator=_UpperCAmelCase		,	sample_posterior=_UpperCAmelCase      ).sample

								assert sample.shape == image.shape

								_lowerCAmelCase    :    Any										=      sample[-1, -2:, -2:, :2].flatten().float().cpu()
								_lowerCAmelCase    :    List[str]										=      torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice      )

								assert torch_all_close(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=3E-3      )



				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
				        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
				        # fmt: on
				    ]      )
				@require_torch_gpu
				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Tuple      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      self.get_sd_vae_model(fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      self.get_sd_image(_UpperCAmelCase		,	fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    int										=      self.get_generator(_UpperCAmelCase      )

								with torch.no_grad():
												_lowerCAmelCase    :    Tuple										=      model(_UpperCAmelCase		,	generator=_UpperCAmelCase		,	sample_posterior=_UpperCAmelCase      ).sample

								assert sample.shape == image.shape

								_lowerCAmelCase    :    Optional[int]										=      sample[-1, -2:, :2, -2:].flatten().float().cpu()
								_lowerCAmelCase    :    Any										=      torch.tensor(_UpperCAmelCase      )

								assert torch_all_close(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1E-2      )



				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]      )
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Optional[int]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.get_sd_vae_model()
								_lowerCAmelCase    :    Optional[int]										=      self.get_sd_image(_UpperCAmelCase      )

								with torch.no_grad():
												_lowerCAmelCase    :    int										=      model(_UpperCAmelCase      ).sample

								assert sample.shape == image.shape

								_lowerCAmelCase    :    List[str]										=      sample[-1, -2:, -2:, :2].flatten().float().cpu()
								_lowerCAmelCase    :    Optional[Any]										=      torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice      )

								assert torch_all_close(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=3E-3      )



				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
				        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
				        # fmt: on
				    ]      )
				@require_torch_gpu
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.get_sd_vae_model()
								_lowerCAmelCase    :    Any										=      self.get_sd_image(_UpperCAmelCase		,	shape=(3, 4, 64, 64)      )

								with torch.no_grad():
												_lowerCAmelCase    :    Union[str, Any]										=      model.decode(_UpperCAmelCase      ).sample

								assert list(sample.shape      ) == [3, 3, 512, 512]

								_lowerCAmelCase    :    Union[str, Any]										=      sample[-1, -2:, :2, -2:].flatten().cpu()
								_lowerCAmelCase    :    Any										=      torch.tensor(_UpperCAmelCase      )

								assert torch_all_close(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1E-3      )



				@parameterized.expand(
				    [
				        # fmt: off
				        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
				        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
				        # fmt: on
				    ]      )
				@require_torch_gpu
				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      self.get_sd_vae_model(fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      self.get_sd_image(_UpperCAmelCase		,	shape=(3, 4, 64, 64)		,	fpaa=_UpperCAmelCase      )

								with torch.no_grad():
												_lowerCAmelCase    :    List[str]										=      model.decode(_UpperCAmelCase      ).sample

								assert list(sample.shape      ) == [3, 3, 512, 512]

								_lowerCAmelCase    :    Union[str, Any]										=      sample[-1, -2:, :2, -2:].flatten().float().cpu()
								_lowerCAmelCase    :    List[str]										=      torch.tensor(_UpperCAmelCase      )

								assert torch_all_close(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=5E-3      )



				@parameterized.expand([(13,), (16,), (27,)]      )
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available()		,	reason="""xformers is not required when using PyTorch 2.0."""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		int      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      self.get_sd_vae_model(fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    int										=      self.get_sd_image(_UpperCAmelCase		,	shape=(3, 4, 64, 64)		,	fpaa=_UpperCAmelCase      )

								with torch.no_grad():
												_lowerCAmelCase    :    Union[str, Any]										=      model.decode(_UpperCAmelCase      ).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
												_lowerCAmelCase    :    Optional[Any]										=      model.decode(_UpperCAmelCase      ).sample

								assert list(sample.shape      ) == [3, 3, 512, 512]

								assert torch_all_close(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1E-1      )



				@parameterized.expand([(13,), (16,), (37,)]      )
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available()		,	reason="""xformers is not required when using PyTorch 2.0."""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      self.get_sd_vae_model()
								_lowerCAmelCase    :    Union[str, Any]										=      self.get_sd_image(_UpperCAmelCase		,	shape=(3, 4, 64, 64)      )

								with torch.no_grad():
												_lowerCAmelCase    :    str										=      model.decode(_UpperCAmelCase      ).sample

								model.enable_xformers_memory_efficient_attention()
								with torch.no_grad():
												_lowerCAmelCase    :    Tuple										=      model.decode(_UpperCAmelCase      ).sample

								assert list(sample.shape      ) == [3, 3, 512, 512]

								assert torch_all_close(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1E-2      )



				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
				        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
				        # fmt: on
				    ]      )
				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		Tuple      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      self.get_sd_vae_model()
								_lowerCAmelCase    :    Union[str, Any]										=      self.get_sd_image(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.get_generator(_UpperCAmelCase      )

								with torch.no_grad():
												_lowerCAmelCase    :    Any										=      model.encode(_UpperCAmelCase      ).latent_dist
												_lowerCAmelCase    :    Any										=      dist.sample(generator=_UpperCAmelCase      )

								assert list(sample.shape      ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

								_lowerCAmelCase    :    List[Any]										=      sample[0, -1, -3:, -3:].flatten().cpu()
								_lowerCAmelCase    :    List[Any]										=      torch.tensor(_UpperCAmelCase      )

								_lowerCAmelCase    :    List[Any]										=      3E-3 if torch_device != """mps""" else 1E-2
								assert torch_all_close(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=_UpperCAmelCase      )







import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


_lowerCamelCase      :  Any      						=						1_6
_lowerCamelCase      :  Tuple      						=						3_2




def 							_UpperCAmelCase  (UpperCamelCase_ : Accelerator      ,    UpperCamelCase_ : DatasetDict      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : int = 16       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      AutoTokenizer.from_pretrained("""bert-base-cased"""       )
				_lowerCAmelCase    :    int										=      DatasetDict(
				    {
				        """train""": dataset["""train"""].select(UpperCamelCase_       ),
				        """validation""": dataset["""train"""].select(UpperCamelCase_       ),
				        """test""": dataset["""validation"""],
				    }       )

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

				def collate_fn(UpperCamelCase_ : Optional[Any]       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_lowerCAmelCase    :    List[str]										=      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":
												_lowerCAmelCase    :    Optional[Any]										=      16
								elif accelerator.mixed_precision != "no":
												_lowerCAmelCase    :    int										=      8
								else:
												_lowerCAmelCase    :    Dict										=      None

								return tokenizer.pad(
								    UpperCamelCase_      ,    padding="""longest"""      ,    max_length=UpperCamelCase_      ,    pad_to_multiple_of=UpperCamelCase_      ,    return_tensors="""pt"""      ,    )

				# Instantiate dataloaders.
				_lowerCAmelCase    :    Optional[Any]										=      DataLoader(
				    tokenized_datasets["""train"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )
				_lowerCAmelCase    :    Tuple										=      DataLoader(
				    tokenized_datasets["""validation"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				_lowerCAmelCase    :    List[str]										=      DataLoader(
				    tokenized_datasets["""test"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				return train_dataloader, eval_dataloader, test_dataloader




def 							_UpperCAmelCase  (UpperCamelCase_ : Any      ,    UpperCamelCase_ : Optional[int]       ):
				'''simple docstring'''
				# New Code #
				_lowerCAmelCase    :    Dict										=      []
				# Download the dataset
				_lowerCAmelCase    :    Optional[int]										=      load_dataset("""glue"""      ,    """mrpc"""       )
				# Create our splits
				_lowerCAmelCase    :    Any										=      StratifiedKFold(n_splits=int(args.num_folds       )       )
				# Initialize accelerator
				_lowerCAmelCase    :    int										=      Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_lowerCAmelCase    :    List[str]										=      config["""lr"""]
				_lowerCAmelCase    :    Any										=      int(config["""num_epochs"""]       )
				_lowerCAmelCase    :    Dict										=      int(config["""seed"""]       )
				_lowerCAmelCase    :    Union[str, Any]										=      int(config["""batch_size"""]       )

				_lowerCAmelCase    :    Optional[Any]										=      evaluate.load("""glue"""      ,    """mrpc"""       )

				# If the batch size is too big we use gradient accumulation
				_lowerCAmelCase    :    Optional[Any]										=      1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								_lowerCAmelCase    :    Optional[Any]										=      batch_size // MAX_GPU_BATCH_SIZE
								_lowerCAmelCase    :    int										=      MAX_GPU_BATCH_SIZE

				set_seed(UpperCamelCase_       )

				# New Code #
				# Create our folds:
				_lowerCAmelCase    :    Optional[int]										=      kfold.split(np.zeros(datasets["""train"""].num_rows       )      ,    datasets["""train"""]["""label"""]       )
				_lowerCAmelCase    :    Any										=      []
				# Iterate over them
				for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase_       ):
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      get_fold_dataloaders(
								    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    )
								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								_lowerCAmelCase    :    int										=      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,    return_dict=UpperCamelCase_       )

								# 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).
								_lowerCAmelCase    :    Union[str, Any]										=      model.to(accelerator.device       )

								# Instantiate optimizer
								_lowerCAmelCase    :    Any										=      AdamW(params=model.parameters()      ,    lr=UpperCamelCase_       )

								# Instantiate scheduler
								_lowerCAmelCase    :    Tuple										=      get_linear_schedule_with_warmup(
								    optimizer=UpperCamelCase_      ,    num_warmup_steps=100      ,    num_training_steps=(len(UpperCamelCase_       ) * 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.
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      accelerator.prepare(
								    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_       )

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

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

												_lowerCAmelCase    :    List[Any]										=      metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"epoch {epoch}:"      ,    UpperCamelCase_       )

								# New Code #
								# We also run predictions on the test set at the very end
								_lowerCAmelCase    :    Dict										=      []
								for step, batch in enumerate(UpperCamelCase_       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												with torch.no_grad():
																_lowerCAmelCase    :    Dict										=      model(**UpperCamelCase_       )
												_lowerCAmelCase    :    List[Any]										=      outputs.logits
												_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[int]										=      accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
												fold_predictions.append(predictions.cpu()       )
												if i == 0:
																# We need all of the test predictions
																test_references.append(references.cpu()       )
        # Use accelerator.print to print only on the main process.
								test_predictions.append(torch.cat(UpperCamelCase_      ,    dim=0       )       )
								# We now need to release all our memory and get rid of the current model, optimizer, etc
								accelerator.free_memory()
				# New Code #
				# Finally we check the accuracy of our folded results:
				_lowerCAmelCase    :    Tuple										=      torch.cat(UpperCamelCase_      ,    dim=0       )
				_lowerCAmelCase    :    Tuple										=      torch.stack(UpperCamelCase_      ,    dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
				_lowerCAmelCase    :    Union[str, Any]										=      metric.compute(predictions=UpperCamelCase_      ,    references=UpperCamelCase_       )
				accelerator.print("""Average test metrics from all folds:"""      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      argparse.ArgumentParser(description="""Simple example of training script."""       )
				parser.add_argument(
				    """--mixed_precision"""      ,    type=UpperCamelCase_      ,    default=UpperCamelCase_      ,    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."""       )
				# New Code #
				parser.add_argument("""--num_folds"""      ,    type=UpperCamelCase_      ,    default=3      ,    help="""The number of splits to perform across the dataset"""       )
				_lowerCAmelCase    :    Optional[Any]										=      parser.parse_args()
				_lowerCAmelCase    :    List[Any]										=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(UpperCamelCase_      ,    UpperCamelCase_       )


if __name__ == "__main__":
	main()

