





import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 					_snake_case						(			UpperCamelCase_       ,    unittest.TestCase ):
	_A          =   UnCLIPImageVariationPipeline
	_A          =   IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
	_A          =   IMAGE_VARIATION_BATCH_PARAMS

	_A          =   [
	    """generator""",
	    """return_dict""",
	    """decoder_num_inference_steps""",
	    """super_res_num_inference_steps""",
	]
	_A          =   False






	@property
	def    lowerCAmelCase_							(		self       )						->    int:
					return 32






	@property
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					return 32






	@property
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					return self.time_input_dim






	@property
	def    lowerCAmelCase_							(		self       )						->    Dict:
					return self.time_input_dim * 4






	@property
	def    lowerCAmelCase_							(		self       )						->    Any:
					return 100






	@property
	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :str         =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
					return tokenizer






	@property
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					torch.manual_seed(0       )
					snake_case__      :Any         =   CLIPTextConfig(
					    bos_token_id=0					,eos_token_id=2					,hidden_size=self.text_embedder_hidden_size					,projection_dim=self.text_embedder_hidden_size					,intermediate_size=37					,layer_norm_eps=1E-05					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=1_000					,)
					return CLIPTextModelWithProjection(_a       )






	@property
	def    lowerCAmelCase_							(		self       )						->    Dict:
					torch.manual_seed(0       )
					snake_case__      :str         =   CLIPVisionConfig(
					    hidden_size=self.text_embedder_hidden_size					,projection_dim=self.text_embedder_hidden_size					,num_hidden_layers=5					,num_attention_heads=4					,image_size=32					,intermediate_size=37					,patch_size=1					,)
					return CLIPVisionModelWithProjection(_a       )






	@property
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					torch.manual_seed(0       )

					snake_case__      :Optional[int]         =   {
					    """clip_embeddings_dim""": self.text_embedder_hidden_size,
					    """time_embed_dim""": self.time_embed_dim,
					    """cross_attention_dim""": self.cross_attention_dim,
					}

					snake_case__      :List[Any]         =   UnCLIPTextProjModel(**_a       )
					return model






	@property
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					torch.manual_seed(0       )

					snake_case__      :Tuple         =   {
					    """sample_size""": 32,
					    # RGB in channels
					    """in_channels""": 3,
					    # Out channels is double in channels because predicts mean and variance
					    """out_channels""": 6,
					    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
					    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
					    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
					    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    """layers_per_block""": 1,
					    """cross_attention_dim""": self.cross_attention_dim,
					    """attention_head_dim""": 4,
					    """resnet_time_scale_shift""": """scale_shift""",
					    """class_embed_type""": """identity""",
					}

					snake_case__      :List[str]         =   UNetaDConditionModel(**_a       )
					return model






	@property
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					return {
					    "sample_size": 64,
					    "layers_per_block": 1,
					    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
					    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
					    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    "in_channels": 6,
					    "out_channels": 3,
					}






	@property
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					torch.manual_seed(0       )

					snake_case__      :List[Any]         =   UNetaDModel(**self.dummy_super_res_kwargs       )
					return model






	@property
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					torch.manual_seed(1       )

					snake_case__      :List[Any]         =   UNetaDModel(**self.dummy_super_res_kwargs       )
					return model






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :str         =   self.dummy_decoder
					snake_case__      :Dict         =   self.dummy_text_proj
					snake_case__      :Any         =   self.dummy_text_encoder
					snake_case__      :Optional[Any]         =   self.dummy_tokenizer
					snake_case__      :Any         =   self.dummy_super_res_first
					snake_case__      :Dict         =   self.dummy_super_res_last

					snake_case__      :int         =   UnCLIPScheduler(
					    variance_type="learned_range"					,prediction_type="epsilon"					,num_train_timesteps=1_000					,)

					snake_case__      :Dict         =   UnCLIPScheduler(
					    variance_type="fixed_small_log"					,prediction_type="epsilon"					,num_train_timesteps=1_000					,)

					snake_case__      :List[Any]         =   CLIPImageProcessor(crop_size=32					,size=32       )

					snake_case__      :Optional[int]         =   self.dummy_image_encoder

					return {
					    "decoder": decoder,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "text_proj": text_proj,
					    "feature_extractor": feature_extractor,
					    "image_encoder": image_encoder,
					    "super_res_first": super_res_first,
					    "super_res_last": super_res_last,
					    "decoder_scheduler": decoder_scheduler,
					    "super_res_scheduler": super_res_scheduler,
					}






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase=0					,UpperCamelCase=True       )						->    Dict:
					snake_case__      :Dict         =   floats_tensor((1, 3, 32, 32)					,rng=random.Random(_a       )       ).to(_a       )
					if str(_a       ).startswith("mps"       ):
									snake_case__      :Optional[Any]         =   torch.manual_seed(_a       )
					else:
									snake_case__      :Tuple         =   torch.Generator(device=_a       ).manual_seed(_a       )

					if pil_image:
									snake_case__      :Union[str, Any]         =   input_image * 0.5 + 0.5
									snake_case__      :int         =   input_image.clamp(0					,1       )
									snake_case__      :int         =   input_image.cpu().permute(0					,2					,3					,1       ).float().numpy()
									snake_case__      :str         =   DiffusionPipeline.numpy_to_pil(_a       )[0]

					return {
					    "image": input_image,
					    "generator": generator,
					    "decoder_num_inference_steps": 2,
					    "super_res_num_inference_steps": 2,
					    "output_type": "np",
					}






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   """cpu"""

					snake_case__      :str         =   self.get_dummy_components()

					snake_case__      :Optional[int]         =   self.pipeline_class(**_a       )
					snake_case__      :str         =   pipe.to(_a       )

					pipe.set_progress_bar_config(disable=_a       )

					snake_case__      :Dict         =   self.get_dummy_inputs(_a					,pil_image=_a       )

					snake_case__      :Optional[int]         =   pipe(**_a       )
					snake_case__      :List[Any]         =   output.images

					snake_case__      :int         =   self.get_dummy_inputs(_a					,pil_image=_a       )

					snake_case__      :Union[str, Any]         =   pipe(
					    **_a					,return_dict=_a					,)[0]

					snake_case__      :Optional[int]         =   image[0, -3:, -3:, -1]
					snake_case__      :Optional[int]         =   image_from_tuple[0, -3:, -3:, -1]

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

					snake_case__      :str         =   np.array(
					    [
					        0.9997,
					        0.0002,
					        0.9997,
					        0.9997,
					        0.9969,
					        0.0023,
					        0.9997,
					        0.9969,
					        0.9970,
					    ]       )

					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:
					snake_case__      :Optional[int]         =   """cpu"""

					snake_case__      :List[Any]         =   self.get_dummy_components()

					snake_case__      :Optional[Any]         =   self.pipeline_class(**_a       )
					snake_case__      :Optional[Any]         =   pipe.to(_a       )

					pipe.set_progress_bar_config(disable=_a       )

					snake_case__      :int         =   self.get_dummy_inputs(_a					,pil_image=_a       )

					snake_case__      :str         =   pipe(**_a       )
					snake_case__      :List[str]         =   output.images

					snake_case__      :Union[str, Any]         =   self.get_dummy_inputs(_a					,pil_image=_a       )

					snake_case__      :Any         =   pipe(
					    **_a					,return_dict=_a					,)[0]

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

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

					snake_case__      :Any         =   np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]       )

					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       )						->    int:
					snake_case__      :Dict         =   """cpu"""

					snake_case__      :Dict         =   self.get_dummy_components()

					snake_case__      :str         =   self.pipeline_class(**_a       )
					snake_case__      :Dict         =   pipe.to(_a       )

					pipe.set_progress_bar_config(disable=_a       )

					snake_case__      :Union[str, Any]         =   self.get_dummy_inputs(_a					,pil_image=_a       )
					snake_case__      :int         =   [
					    pipeline_inputs["""image"""],
					    pipeline_inputs["""image"""],
					]

					snake_case__      :int         =   pipe(**_a       )
					snake_case__      :List[Any]         =   output.images

					snake_case__      :List[str]         =   self.get_dummy_inputs(_a					,pil_image=_a       )
					snake_case__      :List[str]         =   [
					    tuple_pipeline_inputs["""image"""],
					    tuple_pipeline_inputs["""image"""],
					]

					snake_case__      :int         =   pipe(
					    **_a					,return_dict=_a					,)[0]

					snake_case__      :List[Any]         =   image[0, -3:, -3:, -1]
					snake_case__      :Union[str, Any]         =   image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (2, 64, 64, 3)

					snake_case__      :Union[str, Any]         =   np.array(
					    [
					        0.9997,
					        0.9989,
					        0.0008,
					        0.0021,
					        0.9960,
					        0.0018,
					        0.0014,
					        0.0002,
					        0.9933,
					    ]       )

					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       )						->    Optional[int]:
					snake_case__      :List[Any]         =   torch.device("cpu"       )


					class 					_snake_case						:
						_A          =   1




					snake_case__      :str         =   self.get_dummy_components()

					snake_case__      :str         =   self.pipeline_class(**_a       )
					snake_case__      :Optional[int]         =   pipe.to(_a       )

					pipe.set_progress_bar_config(disable=_a       )

					snake_case__      :Union[str, Any]         =   torch.Generator(device=_a       ).manual_seed(0       )
					snake_case__      :List[Any]         =   pipe.decoder.dtype
					snake_case__      :List[Any]         =   1

					snake_case__      :List[Any]         =   (
					    batch_size,
					    pipe.decoder.config.in_channels,
					    pipe.decoder.config.sample_size,
					    pipe.decoder.config.sample_size,
					)
					snake_case__      :Any         =   pipe.prepare_latents(
					    _a					,dtype=_a					,device=_a					,generator=_a					,latents=_a					,scheduler=DummyScheduler()       )

					snake_case__      :Tuple         =   (
					    batch_size,
					    pipe.super_res_first.config.in_channels // 2,
					    pipe.super_res_first.config.sample_size,
					    pipe.super_res_first.config.sample_size,
					)
					snake_case__      :List[Any]         =   pipe.prepare_latents(
					    _a					,dtype=_a					,device=_a					,generator=_a					,latents=_a					,scheduler=DummyScheduler()       )

					snake_case__      :int         =   self.get_dummy_inputs(_a					,pil_image=_a       )

					snake_case__      :List[Any]         =   pipe(
					    **_a					,decoder_latents=_a					,super_res_latents=_a       ).images

					snake_case__      :Tuple         =   self.get_dummy_inputs(_a					,pil_image=_a       )
					# Don't pass image, instead pass embedding
					snake_case__      :Dict         =   pipeline_inputs.pop("image"       )
					snake_case__      :Union[str, Any]         =   pipe.image_encoder(_a       ).image_embeds

					snake_case__      :Optional[int]         =   pipe(
					    **_a					,decoder_latents=_a					,super_res_latents=_a					,image_embeddings=_a					,).images

					# make sure passing text embeddings manually is identical
					assert np.abs(img_out_a - img_out_a       ).max() < 1E-4






	@skip_mps
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :List[str]         =   torch_device == """cpu"""

					# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
					snake_case__      :Tuple         =   1E-2

					self._test_attention_slicing_forward_pass(
					    test_max_difference=_a					,expected_max_diff=_a       )






	@skip_mps
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :List[str]         =   torch_device == """cpu"""
					snake_case__      :List[str]         =   True
					snake_case__      :List[str]         =   [
					    """decoder_num_inference_steps""",
					    """super_res_num_inference_steps""",
					]

					self._test_inference_batch_single_identical(
					    test_max_difference=_a					,relax_max_difference=_a					,additional_params_copy_to_batched_inputs=_a					,)






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Optional[int]         =   [
					    """decoder_num_inference_steps""",
					    """super_res_num_inference_steps""",
					]

					if torch_device == "mps":
									# TODO: MPS errors with larger batch sizes
									snake_case__      :List[Any]         =   [2, 3]
									self._test_inference_batch_consistent(
									    batch_sizes=_a					,additional_params_copy_to_batched_inputs=_a					,)
					else:
									self._test_inference_batch_consistent(
									    additional_params_copy_to_batched_inputs=_a       )






	@skip_mps
	def    lowerCAmelCase_							(		self       )						->    int:
					return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					return super().test_save_load_local()






	@skip_mps
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					return super().test_save_load_optional_components()




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






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Tuple         =   load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"       )
					snake_case__      :str         =   load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"       )

					snake_case__      :List[Any]         =   UnCLIPImageVariationPipeline.from_pretrained(
					    "kakaobrain/karlo-v1-alpha-image-variations"					,torch_dtype=torch.floataa       )
					snake_case__      :int         =   pipeline.to(_a       )
					pipeline.set_progress_bar_config(disable=_a       )

					snake_case__      :List[Any]         =   torch.Generator(device="cpu"       ).manual_seed(0       )
					snake_case__      :Any         =   pipeline(
					    _a					,generator=_a					,output_type="np"					,)

					snake_case__      :Any         =   output.images[0]

					assert image.shape == (256, 256, 3)

					assert_mean_pixel_difference(_a					,_a					,15       )







import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__UpperCAmelCase							:   Optional[Any]							=  1_6
__UpperCAmelCase							:   Optional[int]							=  3_2





def 					lowercase_ (    __snake_case     :							Accelerator	,	__snake_case     :							int = 16	,	__snake_case     :							str = "bert-base-cased"      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :int         =   AutoTokenizer.from_pretrained(__snake_case      )
				snake_case__      :Optional[int]         =   load_dataset("glue"	,	"mrpc"      )

				def tokenize_function(__snake_case     :							Tuple      ):
								# max_length=None => use the model max length (it's actually the default)
								snake_case__      :Any         =   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
				snake_case__      :List[Any]         =   datasets.map(
				    __snake_case	,	batched=__snake_case	,	remove_columns=["idx", "sentence1", "sentence2"]	,	load_from_cache_file=__snake_case      )

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

				def collate_fn(__snake_case     :							Dict      ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(__snake_case	,	padding="max_length"	,	max_length=1_28	,	return_tensors="pt"      )
								return tokenizer.pad(__snake_case	,	padding="longest"	,	return_tensors="pt"      )

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

				return train_dataloader, eval_dataloader





def 					lowercase_ (    __snake_case     :							List[Any]	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							int	,	__snake_case     :							Optional[int]      )     -> Tuple:





				'''simple docstring'''


				model.eval()
				snake_case__      :Union[str, Any]         =   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      )
								with torch.no_grad():
												snake_case__      :List[Any]         =   model(**__snake_case      )
								snake_case__      :Any         =   outputs.logits.argmax(dim=-1      )
								# It is slightly faster to call this once, than multiple times
								snake_case__    ,						snake_case__      :Tuple         =   accelerator.gather(
								    (predictions, batch["labels"])      )  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(__snake_case      ) - 1:
																snake_case__      :List[str]         =   predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																snake_case__      :Optional[int]         =   references[: len(eval_dataloader.dataset      ) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=__snake_case	,	references=__snake_case	,	)

				snake_case__      :int         =   metric.compute()
				return eval_metric["accuracy"]





def 					lowercase_ (    __snake_case     :							Union[str, Any]	,	__snake_case     :							Optional[Any]      )     -> Any:





				'''simple docstring'''


				snake_case__      :Any         =   Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				snake_case__      :Union[str, Any]         =   config["lr"]
				snake_case__      :List[str]         =   int(config["num_epochs"]      )
				snake_case__      :Optional[Any]         =   int(config["seed"]      )
				snake_case__      :List[Any]         =   int(config["batch_size"]      )
				snake_case__      :List[Any]         =   args.model_name_or_path

				set_seed(__snake_case      )
				snake_case__    ,						snake_case__      :List[Any]         =   get_dataloaders(__snake_case	,	__snake_case	,	__snake_case      )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case__      :List[Any]         =   AutoModelForSequenceClassification.from_pretrained(__snake_case	,	return_dict=__snake_case      )

				# Instantiate optimizer
				snake_case__      :int         =   (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				snake_case__      :Tuple         =   optimizer_cls(params=model.parameters()	,	lr=__snake_case      )

				if accelerator.state.deepspeed_plugin is not None:
								snake_case__      :List[str]         =   accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								snake_case__      :Any         =   1
				snake_case__      :List[Any]         =   (len(__snake_case      ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								snake_case__      :Optional[Any]         =   get_linear_schedule_with_warmup(
								    optimizer=__snake_case	,	num_warmup_steps=0	,	num_training_steps=__snake_case	,	)
				else:
								snake_case__      :Any         =   DummyScheduler(__snake_case	,	total_num_steps=__snake_case	,	warmup_num_steps=0      )

				# 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.
				snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :int         =   accelerator.prepare(
				    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )

				# We need to keep track of how many total steps we have iterated over
				snake_case__      :Dict         =   0
				# We also need to keep track of the stating epoch so files are named properly
				snake_case__      :Union[str, Any]         =   0
				snake_case__      :List[str]         =   evaluate.load("glue"	,	"mrpc"      )
				snake_case__      :Optional[Any]         =   num_epochs

				if args.partial_train_epoch is not None:
								snake_case__      :List[Any]         =   args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint      )
								snake_case__      :Union[str, Any]         =   args.resume_from_checkpoint.split("epoch_"      )[1]
								snake_case__      :Dict         =   ""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								snake_case__      :str         =   int(__snake_case      ) + 1
								snake_case__      :List[Any]         =   evaluation_loop(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )
								accelerator.print("resumed checkpoint performance:"	,	__snake_case      )
								accelerator.print("resumed checkpoint's scheduler's lr:"	,	lr_scheduler.get_lr()[0]      )
								accelerator.print("resumed optimizers's lr:"	,	optimizer.param_groups[0]["lr"]      )
								with open(os.path.join(args.output_dir	,	F'state_{starting_epoch-1}.json'      )	,	"r"      ) as f:
												snake_case__      :Tuple         =   json.load(__snake_case      )
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				snake_case__      :Optional[int]         =   {}
				for epoch in range(__snake_case	,	__snake_case      ):
								model.train()
								for step, batch in enumerate(__snake_case      ):
												snake_case__      :str         =   model(**__snake_case      )
												snake_case__      :List[str]         =   outputs.loss
												snake_case__      :List[Any]         =   loss / gradient_accumulation_steps
												accelerator.backward(__snake_case      )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								snake_case__      :int         =   F'epoch_{epoch}'
								snake_case__      :str         =   os.path.join(args.output_dir	,	__snake_case      )
								accelerator.save_state(__snake_case      )
								snake_case__      :Union[str, Any]         =   evaluation_loop(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )
								snake_case__      :List[str]         =   accuracy
								snake_case__      :List[str]         =   lr_scheduler.get_lr()[0]
								snake_case__      :List[Any]         =   optimizer.param_groups[0]["lr"]
								snake_case__      :Dict         =   epoch
								snake_case__      :List[Any]         =   overall_step
								accelerator.print(F'epoch {epoch}:'	,	__snake_case      )

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir	,	F'state_{epoch}.json'      )	,	"w"      ) as f:
																json.dump(__snake_case	,	__snake_case      )





def 					lowercase_ (    )     -> Any:





				'''simple docstring'''


				snake_case__      :List[Any]         =   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
				parser.add_argument(
				    "--model_name_or_path"	,	type=__snake_case	,	default="bert-base-cased"	,	help="Path to pretrained model or model identifier from huggingface.co/models."	,	required=__snake_case	,	)
				parser.add_argument(
				    "--output_dir"	,	type=__snake_case	,	default="."	,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,	)
				parser.add_argument(
				    "--resume_from_checkpoint"	,	type=__snake_case	,	default=__snake_case	,	help="If the training should continue from a checkpoint folder."	,	)
				parser.add_argument(
				    "--partial_train_epoch"	,	type=__snake_case	,	default=__snake_case	,	help="If passed, the training will stop after this number of epochs."	,	)
				parser.add_argument(
				    "--num_epochs"	,	type=__snake_case	,	default=2	,	help="Number of train epochs."	,	)
				snake_case__      :Any         =   parser.parse_args()
				snake_case__      :int         =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

				training_function(__snake_case	,	__snake_case      )


if __name__ == "__main__":
	main()