




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





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





				'''simple docstring'''


				snake_case__      :List[str]         =   AutoTokenizer.from_pretrained(__snake_case      )
				snake_case__      :str         =   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__      :Optional[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__      :int         =   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__      :Optional[int]         =   tokenized_datasets.rename_column("label"	,	"labels"      )

				def collate_fn(__snake_case     :							List[str]      ):
								# 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__      :str         =   DataLoader(
				    tokenized_datasets["train"]	,	shuffle=__snake_case	,	collate_fn=__snake_case	,	batch_size=__snake_case      )
				snake_case__      :List[Any]         =   DataLoader(
				    tokenized_datasets["validation"]	,	shuffle=__snake_case	,	collate_fn=__snake_case	,	batch_size=__snake_case      )

				return train_dataloader, eval_dataloader





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





				'''simple docstring'''


				snake_case__      :int         =   Accelerator()

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

				set_seed(__snake_case      )
				snake_case__      :Dict         =   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__      :Union[str, Any]         =   AutoModelForSequenceClassification.from_pretrained(__snake_case	,	return_dict=__snake_case      )

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

				if accelerator.state.deepspeed_plugin is not None:
								snake_case__      :Tuple         =   accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								snake_case__      :Optional[Any]         =   1
				snake_case__      :Optional[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__      :Dict         =   get_linear_schedule_with_warmup(
								    optimizer=__snake_case	,	num_warmup_steps=0	,	num_training_steps=__snake_case	,	)
				else:
								snake_case__      :List[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__      :List[str]         =   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__      :List[str]         =   0
				# We also need to keep track of the stating epoch so files are named properly
				snake_case__      :Optional[int]         =   0

				# Now we train the model
				snake_case__      :List[Any]         =   evaluate.load("glue"	,	"mrpc"      )
				snake_case__      :Tuple         =   0
				snake_case__      :List[str]         =   {}
				for epoch in range(__snake_case	,	__snake_case      ):
								model.train()
								for step, batch in enumerate(__snake_case      ):
												snake_case__      :int         =   model(**__snake_case      )
												snake_case__      :List[Any]         =   outputs.loss
												snake_case__      :Tuple         =   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

								model.eval()
								snake_case__      :Optional[int]         =   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__      :Optional[int]         =   model(**__snake_case      )
												snake_case__      :Optional[int]         =   outputs.logits.argmax(dim=-1      )
												# It is slightly faster to call this once, than multiple times
												snake_case__      :Any         =   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__      :Optional[Any]         =   predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																				snake_case__      :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()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}:'	,	__snake_case      )
								snake_case__      :List[Any]         =   eval_metric["accuracy"]

								if best_performance < eval_metric["accuracy"]:
												snake_case__      :List[str]         =   eval_metric["accuracy"]

				if args.performance_lower_bound is not None:
								assert (
								    args.performance_lower_bound <= best_performance
								), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir	,	"all_results.json"      )	,	"w"      ) as f:
												json.dump(__snake_case	,	__snake_case      )





def 					lowercase_ (    )     -> List[str]:





				'''simple docstring'''


				snake_case__      :Union[str, 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(
				    "--performance_lower_bound"	,	type=__snake_case	,	default=__snake_case	,	help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."	,	)
				parser.add_argument(
				    "--num_epochs"	,	type=__snake_case	,	default=3	,	help="Number of train epochs."	,	)
				snake_case__      :int         =   parser.parse_args()
				snake_case__      :Optional[int]         =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
				training_function(__snake_case	,	__snake_case      )


if __name__ == "__main__":
	main()







import os
import sys
import unittest


__UpperCAmelCase							:   str							=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase							:   Tuple							=  os.path.join(git_repo_path, "src", "diffusers")


class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   find_backend("    if not is_torch_available():"       )
					self.assertEqual(UpperCamelCase					,"torch"       )

					# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
					# self.assertEqual(backend_with_underscore, "tensorflow_text")

					snake_case__      :Tuple         =   find_backend("    if not (is_torch_available() and is_transformers_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers"       )

					# double_backend_with_underscore = find_backend(
					#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
					# )
					# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

					snake_case__      :str         =   find_backend(
					    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers_and_onnx"       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :int         =   read_init()
					# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
					self.assertIn("torch"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers"					,UpperCamelCase       )
					self.assertIn("flax_and_transformers"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers_and_onnx"					,UpperCamelCase       )

					# Likewise, we can't assert on the exact content of a key
					self.assertIn("UNet2DModel"					,objects["torch"]       )
					self.assertIn("FlaxUNet2DConditionModel"					,objects["flax"]       )
					self.assertIn("StableDiffusionPipeline"					,objects["torch_and_transformers"]       )
					self.assertIn("FlaxStableDiffusionPipeline"					,objects["flax_and_transformers"]       )
					self.assertIn("LMSDiscreteScheduler"					,objects["torch_and_scipy"]       )
					self.assertIn("OnnxStableDiffusionPipeline"					,objects["torch_and_transformers_and_onnx"]       )






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Union[str, Any]         =   create_dummy_object("CONSTANT"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,"\nCONSTANT = None\n"       )

					snake_case__      :Optional[Any]         =   create_dummy_object("function"					,"'torch'"       )
					self.assertEqual(
					    UpperCamelCase					,"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"       )

					snake_case__      :str         =   "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
					snake_case__      :List[str]         =   create_dummy_object("FakeClass"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Tuple         =   "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
					snake_case__      :int         =   create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}       )
					self.assertEqual(dummy_files["torch"]					,UpperCamelCase       )