"""simple docstring"""





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class 			_snake_case  (       unittest.TestCase		):






						def    lowerCamelCase__  (							self   :      Optional[Any]     ):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						@property
						def    lowerCamelCase__  (							self   :      List[str]     ):
													__lowerCamelCase       :		Any		 =  1
													__lowerCamelCase       :		str		 =  3
													__lowerCamelCase       :		Optional[int]		 =  (32, 32)

													__lowerCamelCase       :		Optional[Any]		 =  floats_tensor((batch_size, num_channels) + sizes				,       rng=random.Random(0     )     ).to(lowerCAmelCase_     )
													return image






						@property
						def    lowerCamelCase__  (							self   :      Tuple     ):
													torch.manual_seed(0     )
													__lowerCamelCase       :		List[Any]		 =  UNetaDConditionModel(
													    block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=4				,       out_channels=4				,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				,       cross_attention_dim=32				,       )
													return model






						@property
						def    lowerCamelCase__  (							self   :      int     ):
													torch.manual_seed(0     )
													__lowerCamelCase       :		List[Any]		 =  AutoencoderKL(
													    block_out_channels=[32, 64]				,       in_channels=3				,       out_channels=3				,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,       latent_channels=4				,       )
													return model






						@property
						def    lowerCamelCase__  (							self   :      Optional[int]     ):
													torch.manual_seed(0     )
													__lowerCamelCase       :		str		 =  CLIPTextConfig(
													    bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1E-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1000				,       )
													return CLIPTextModel(lowerCAmelCase_     )






						@property
						def    lowerCamelCase__  (							self   :      Dict     ):
													def extract(*UpperCAmelCase   :      Dict				,       **UpperCAmelCase   :      Optional[int]     ):
																				class 			_snake_case  :






																										def __init__(							self   :      Optional[int]     ):
																																	__lowerCamelCase       :		Union[str, Any]		 =  torch.ones([0]     )







																										def    lowerCamelCase__  (							self   :      Dict				,       UpperCAmelCase   :      str     ):
																																	self.pixel_values.to(lowerCAmelCase_     )
																																	return self

																				return Out()

													return extract






						def    lowerCamelCase__  (							self   :      List[str]     ):
													__lowerCamelCase       :		Dict		 =  'cpu'  # ensure determinism for the device-dependent torch.Generator
													__lowerCamelCase       :		str		 =  self.dummy_cond_unet
													__lowerCamelCase       :		Dict		 =  DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       beta_schedule="scaled_linear"				,       clip_sample=lowerCAmelCase_				,       set_alpha_to_one=lowerCAmelCase_				,       )

													__lowerCamelCase       :		Optional[Any]		 =  self.dummy_vae
													__lowerCamelCase       :		Tuple		 =  self.dummy_text_encoder
													__lowerCamelCase       :		Any		 =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

													# make sure here that pndm scheduler skips prk
													__lowerCamelCase       :		Optional[Any]		 =  StableDiffusionPipeline(
													    unet=lowerCAmelCase_				,       scheduler=lowerCAmelCase_				,       vae=lowerCAmelCase_				,       text_encoder=lowerCAmelCase_				,       tokenizer=lowerCAmelCase_				,       safety_checker=lowerCAmelCase_				,       feature_extractor=self.dummy_extractor				,       )
													__lowerCamelCase       :		Optional[int]		 =  sd_pipe.to(lowerCAmelCase_     )
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

													__lowerCamelCase       :		Union[str, Any]		 =  'A painting of a squirrel eating a burger'

													__lowerCamelCase       :		List[str]		 =  torch.Generator(device=lowerCAmelCase_     ).manual_seed(0     )
													__lowerCamelCase       :		Union[str, Any]		 =  sd_pipe([prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type="np"     )
													__lowerCamelCase       :		List[Any]		 =  output.images

													__lowerCamelCase       :		List[Any]		 =  torch.Generator(device=lowerCAmelCase_     ).manual_seed(0     )
													__lowerCamelCase       :		Any		 =  sd_pipe(
													    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type="np"				,       return_dict=lowerCAmelCase_				,       )[0]

													__lowerCamelCase       :		List[Any]		 =  image[0, -3:, -3:, -1]
													__lowerCamelCase       :		Tuple		 =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													__lowerCamelCase       :		str		 =  np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]     )

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2






						def    lowerCamelCase__  (							self   :      Dict     ):
													__lowerCamelCase       :		List[Any]		 =  'cpu'  # ensure determinism for the device-dependent torch.Generator
													__lowerCamelCase       :		int		 =  self.dummy_cond_unet
													__lowerCamelCase       :		str		 =  PNDMScheduler(skip_prk_steps=lowerCAmelCase_     )
													__lowerCamelCase       :		Dict		 =  self.dummy_vae
													__lowerCamelCase       :		Optional[Any]		 =  self.dummy_text_encoder
													__lowerCamelCase       :		Optional[int]		 =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

													# make sure here that pndm scheduler skips prk
													__lowerCamelCase       :		List[Any]		 =  StableDiffusionPipeline(
													    unet=lowerCAmelCase_				,       scheduler=lowerCAmelCase_				,       vae=lowerCAmelCase_				,       text_encoder=lowerCAmelCase_				,       tokenizer=lowerCAmelCase_				,       safety_checker=lowerCAmelCase_				,       feature_extractor=self.dummy_extractor				,       )
													__lowerCamelCase       :		str		 =  sd_pipe.to(lowerCAmelCase_     )
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

													__lowerCamelCase       :		List[str]		 =  'A painting of a squirrel eating a burger'
													__lowerCamelCase       :		List[str]		 =  torch.Generator(device=lowerCAmelCase_     ).manual_seed(0     )
													__lowerCamelCase       :		str		 =  sd_pipe([prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type="np"     )

													__lowerCamelCase       :		Any		 =  output.images

													__lowerCamelCase       :		Any		 =  torch.Generator(device=lowerCAmelCase_     ).manual_seed(0     )
													__lowerCamelCase       :		Any		 =  sd_pipe(
													    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type="np"				,       return_dict=lowerCAmelCase_				,       )[0]

													__lowerCamelCase       :		int		 =  image[0, -3:, -3:, -1]
													__lowerCamelCase       :		str		 =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													__lowerCamelCase       :		Dict		 =  np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]     )

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2






						def    lowerCamelCase__  (							self   :      Tuple     ):
													__lowerCamelCase       :		List[Any]		 =  StableDiffusionPipeline.from_pretrained(
													    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"				,       safety_checker=lowerCAmelCase_     )
													assert isinstance(lowerCAmelCase_				,       lowerCAmelCase_     )
													assert isinstance(pipe.scheduler				,       lowerCAmelCase_     )
													assert pipe.safety_checker is None

													__lowerCamelCase       :		Optional[Any]		 =  pipe("example prompt"				,       num_inference_steps=2     ).images[0]
													assert image is not None

													# check that there's no error when saving a pipeline with one of the models being None
													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(lowerCAmelCase_     )
																				__lowerCamelCase       :		Optional[int]		 =  StableDiffusionPipeline.from_pretrained(lowerCAmelCase_     )

													# sanity check that the pipeline still works
													assert pipe.safety_checker is None
													__lowerCamelCase       :		Optional[Any]		 =  pipe("example prompt"				,       num_inference_steps=2     ).images[0]
													assert image is not None







						@unittest.skipIf(torch_device != "cuda"				,       "This test requires a GPU"     )
						def    lowerCamelCase__  (							self   :      int     ):
													__lowerCamelCase       :		Any		 =  self.dummy_cond_unet
													__lowerCamelCase       :		List[str]		 =  PNDMScheduler(skip_prk_steps=lowerCAmelCase_     )
													__lowerCamelCase       :		str		 =  self.dummy_vae
													__lowerCamelCase       :		Tuple		 =  self.dummy_text_encoder
													__lowerCamelCase       :		Any		 =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

													# put models in fp16
													__lowerCamelCase       :		Optional[int]		 =  unet.half()
													__lowerCamelCase       :		List[str]		 =  vae.half()
													__lowerCamelCase       :		List[str]		 =  bert.half()

													# make sure here that pndm scheduler skips prk
													__lowerCamelCase       :		Optional[Any]		 =  StableDiffusionPipeline(
													    unet=lowerCAmelCase_				,       scheduler=lowerCAmelCase_				,       vae=lowerCAmelCase_				,       text_encoder=lowerCAmelCase_				,       tokenizer=lowerCAmelCase_				,       safety_checker=lowerCAmelCase_				,       feature_extractor=self.dummy_extractor				,       )
													__lowerCamelCase       :		Any		 =  sd_pipe.to(lowerCAmelCase_     )
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

													__lowerCamelCase       :		Union[str, Any]		 =  'A painting of a squirrel eating a burger'
													__lowerCamelCase       :		Union[str, Any]		 =  sd_pipe([prompt]				,       num_inference_steps=2				,       output_type="np"     ).images

													assert image.shape == (1, 64, 64, 3)

@nightly
@require_torch_gpu
class 			_snake_case  (       unittest.TestCase		):






						def    lowerCamelCase__  (							self   :      str     ):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def    lowerCamelCase__  (							self   :      Union[str, Any]     ):
													__lowerCamelCase       :		Optional[Any]		 =  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"				,       safety_checker=lowerCAmelCase_     )
													__lowerCamelCase       :		Tuple		 =  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config     )
													__lowerCamelCase       :		List[str]		 =  sd_pipe.to(lowerCAmelCase_     )
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

													__lowerCamelCase       :		Optional[Any]		 =  (
													    'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
													    ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
													    ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
													    ' children from bahnhof zoo, detailed '
													)
													__lowerCamelCase       :		List[Any]		 =  4003660346
													__lowerCamelCase       :		List[Any]		 =  7

													# without safety guidance (sld_guidance_scale = 0)
													__lowerCamelCase       :		str		 =  torch.manual_seed(lowerCAmelCase_     )
													__lowerCamelCase       :		Union[str, Any]		 =  sd_pipe(
													    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=lowerCAmelCase_				,       num_inference_steps=50				,       output_type="np"				,       width=512				,       height=512				,       sld_guidance_scale=0				,       )

													__lowerCamelCase       :		Union[str, Any]		 =  output.images
													__lowerCamelCase       :		List[str]		 =  image[0, -3:, -3:, -1]
													__lowerCamelCase       :		str		 =  [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

													assert image.shape == (1, 512, 512, 3)

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

													# without safety guidance (strong configuration)
													__lowerCamelCase       :		Tuple		 =  torch.manual_seed(lowerCAmelCase_     )
													__lowerCamelCase       :		List[str]		 =  sd_pipe(
													    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=lowerCAmelCase_				,       num_inference_steps=50				,       output_type="np"				,       width=512				,       height=512				,       sld_guidance_scale=2000				,       sld_warmup_steps=7				,       sld_threshold=0.0_2_5				,       sld_momentum_scale=0.5				,       sld_mom_beta=0.7				,       )

													__lowerCamelCase       :		List[str]		 =  output.images
													__lowerCamelCase       :		Any		 =  image[0, -3:, -3:, -1]
													__lowerCamelCase       :		Union[str, Any]		 =  [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

													assert image.shape == (1, 512, 512, 3)

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






						def    lowerCamelCase__  (							self   :      Any     ):
													__lowerCamelCase       :		Dict		 =  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"				,       safety_checker=lowerCAmelCase_     )
													__lowerCamelCase       :		Optional[Any]		 =  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config     )
													__lowerCamelCase       :		Optional[Any]		 =  sd_pipe.to(lowerCAmelCase_     )
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

													__lowerCamelCase       :		int		 =  'padme amidala taking a bath artwork, safe for work, no nudity'
													__lowerCamelCase       :		str		 =  2734971755
													__lowerCamelCase       :		Dict		 =  7

													__lowerCamelCase       :		List[str]		 =  torch.manual_seed(lowerCAmelCase_     )
													__lowerCamelCase       :		Tuple		 =  sd_pipe(
													    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=lowerCAmelCase_				,       num_inference_steps=50				,       output_type="np"				,       width=512				,       height=512				,       sld_guidance_scale=0				,       )

													__lowerCamelCase       :		str		 =  output.images
													__lowerCamelCase       :		int		 =  image[0, -3:, -3:, -1]
													__lowerCamelCase       :		Union[str, Any]		 =  [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

													assert image.shape == (1, 512, 512, 3)

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

													__lowerCamelCase       :		Optional[Any]		 =  torch.manual_seed(lowerCAmelCase_     )
													__lowerCamelCase       :		Any		 =  sd_pipe(
													    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=lowerCAmelCase_				,       num_inference_steps=50				,       output_type="np"				,       width=512				,       height=512				,       sld_guidance_scale=2000				,       sld_warmup_steps=7				,       sld_threshold=0.0_2_5				,       sld_momentum_scale=0.5				,       sld_mom_beta=0.7				,       )

													__lowerCamelCase       :		Optional[Any]		 =  output.images
													__lowerCamelCase       :		Any		 =  image[0, -3:, -3:, -1]
													__lowerCamelCase       :		Optional[Any]		 =  [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

													assert image.shape == (1, 512, 512, 3)

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2







						def    lowerCamelCase__  (							self   :      List[str]     ):
													__lowerCamelCase       :		Optional[Any]		 =  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     )
													__lowerCamelCase       :		List[Any]		 =  sd_pipe.to(lowerCAmelCase_     )
													sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

													__lowerCamelCase       :		List[Any]		 =  (
													    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
													    ' leyendecker'
													)
													__lowerCamelCase       :		List[Any]		 =  1044355234
													__lowerCamelCase       :		Optional[int]		 =  12

													__lowerCamelCase       :		int		 =  torch.manual_seed(lowerCAmelCase_     )
													__lowerCamelCase       :		Optional[Any]		 =  sd_pipe(
													    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=lowerCAmelCase_				,       num_inference_steps=50				,       output_type="np"				,       width=512				,       height=512				,       sld_guidance_scale=0				,       )

													__lowerCamelCase       :		Dict		 =  output.images
													__lowerCamelCase       :		Union[str, Any]		 =  image[0, -3:, -3:, -1]
													__lowerCamelCase       :		str		 =  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]     )

													assert image.shape == (1, 512, 512, 3)

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-7

													__lowerCamelCase       :		Any		 =  torch.manual_seed(lowerCAmelCase_     )
													__lowerCamelCase       :		List[Any]		 =  sd_pipe(
													    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=lowerCAmelCase_				,       num_inference_steps=50				,       output_type="np"				,       width=512				,       height=512				,       sld_guidance_scale=2000				,       sld_warmup_steps=7				,       sld_threshold=0.0_2_5				,       sld_momentum_scale=0.5				,       sld_mom_beta=0.7				,       )

													__lowerCamelCase       :		List[str]		 =  output.images
													__lowerCamelCase       :		int		 =  image[0, -3:, -3:, -1]
													__lowerCamelCase       :		Optional[int]		 =  np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]     )
													assert image.shape == (1, 512, 512, 3)

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2







"""simple docstring"""
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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__    						=			16
lowerCAmelCase__    						=			32





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase = 16       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    AutoTokenizer.from_pretrained('bert-base-cased'       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    load_dataset('glue',						'mrpc'       )

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    datasets.map(
								    lowerCamelCase,						batched=lowerCamelCase,						remove_columns=['idx', 'sentence1', 'sentence2'],						)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_SCREAMING_SNAKE_CASE   :		Dict       		=    tokenized_datasets.rename_column('label',						'labels'       )

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

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

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    DataLoader(
				    tokenized_datasets['train'],						shuffle=lowerCamelCase,						collate_fn=lowerCamelCase,						batch_size=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    DataLoader(
				    tokenized_datasets['validation'],						shuffle=lowerCamelCase,						collate_fn=lowerCamelCase,						batch_size=lowerCamelCase       )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			lowerCAmelCase__    						=			mocked_dataloaders  # noqa: F811





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

				_SCREAMING_SNAKE_CASE   :		Tuple       		=    evaluate.load('glue',						'mrpc'       )

				# New Code #
				# We now can define an inner training loop function. It should take a batch size as the only parameter,
				# and build the dataloaders in there.
				# It also gets our decorator
				@find_executable_batch_size(starting_batch_size=lowerCamelCase       )
				def inner_training_loop(lowerCamelCase       ):
								# And now just move everything below under this function
								# We need to bring in the Accelerator object from earlier
								nonlocal accelerator
								# And reset all of its attributes that could hold onto any memory:
								accelerator.free_memory()

								# Then we can declare the model, optimizer, and everything else:
								set_seed(lowerCamelCase       )

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

								# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
								# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
								# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
								_SCREAMING_SNAKE_CASE   :		Any       		=    model.to(accelerator.device       )

								# Instantiate optimizer
								_SCREAMING_SNAKE_CASE   :		str       		=    AdamW(params=model.parameters(),						lr=lowerCamelCase       )
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_dataloaders(lowerCamelCase,						lowerCamelCase       )

								# Instantiate scheduler
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase,						num_warmup_steps=100,						num_training_steps=(len(lowerCamelCase       ) * num_epochs),						)

								# 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.
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    accelerator.prepare(
								    lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase       )

								# Now we train the model
								for epoch in range(lowerCamelCase       ):
												model.train()
												for step, batch in enumerate(lowerCamelCase       ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device       )
																_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    model(**lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		List[str]       		=    outputs.loss
																accelerator.backward(lowerCamelCase       )
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}:""",						lowerCamelCase       )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
				inner_training_loop()





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		str       		=    argparse.ArgumentParser(description='Simple example of training script.'       )
				parser.add_argument(
				    '--mixed_precision',						type=lowerCamelCase,						default=lowerCamelCase,						choices=['no', 'fp16', 'bf16', 'fp8'],						help='Whether to use mixed precision. Choose'
				    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
				    'and an Nvidia Ampere GPU.',						)
				parser.add_argument('--cpu',						action='store_true',						help='If passed, will train on the CPU.'       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    parser.parse_args()
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(lowerCamelCase,						lowerCamelCase       )


if __name__ == "__main__":
			main()

