


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


A       :		Tuple    =   1_6
A       :		Tuple    =   3_2






def  UpperCamelCase					(	__magic_name__     :							Accelerator    ,    __magic_name__     :							int = 16    ,    __magic_name__     :							str = "bert-base-cased"     )   ->  Union[str, Any]:
		"""simple docstring"""




		lowercase__     			=		AutoTokenizer.from_pretrained(snake_case_     )
		lowercase__     			=		load_dataset("""glue"""    ,    """mrpc"""     )

		def tokenize_function(__magic_name__     :							Optional[Any]     ):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__     			=		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
		lowercase__     			=		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
		lowercase__     			=		tokenized_datasets.rename_column("""label"""    ,    """labels"""     )

		def collate_fn(__magic_name__     :							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=128    ,    return_tensors="""pt"""     )
				return tokenizer.pad(snake_case_    ,    padding="""longest"""    ,    return_tensors="""pt"""     )

		# Instantiate dataloaders.
		lowercase__     			=		DataLoader(
		    tokenized_datasets["""train"""]    ,    shuffle=snake_case_    ,    collate_fn=snake_case_    ,    batch_size=snake_case_     )
		lowercase__     			=		DataLoader(
		    tokenized_datasets["""validation"""]    ,    shuffle=snake_case_    ,    collate_fn=snake_case_    ,    batch_size=snake_case_     )

		return train_dataloader, eval_dataloader






def  UpperCamelCase					(	__magic_name__     :							int    ,    __magic_name__     :							Union[str, Any]    ,    __magic_name__     :							Optional[int]    ,    __magic_name__     :							Union[str, Any]     )   ->  Union[str, Any]:
		"""simple docstring"""




		model.eval()
		lowercase__     			=		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():
						lowercase__     			=		model(**snake_case_     )
				lowercase__     			=		outputs.logits.argmax(dim=-1     )
				# It is slightly faster to call this once, than multiple times
				lowercase__			,	lowercase__     			=		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:
								lowercase__     			=		predictions[: len(eval_dataloader.dataset     ) - samples_seen]
								lowercase__     			=		references[: len(eval_dataloader.dataset     ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=snake_case_    ,    references=snake_case_    ,    )

		lowercase__     			=		metric.compute()
		return eval_metric["accuracy"]






def  UpperCamelCase					(	__magic_name__     :							Tuple    ,    __magic_name__     :							Optional[int]     )   ->  Optional[Any]:
		"""simple docstring"""




		lowercase__     			=		Accelerator()

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

		set_seed(snake_case_     )
		lowercase__			,	lowercase__     			=		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)
		lowercase__     			=		AutoModelForSequenceClassification.from_pretrained(snake_case_    ,    return_dict=snake_case_     )

		# Instantiate optimizer
		lowercase__     			=		(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowercase__     			=		optimizer_cls(params=model.parameters()    ,    lr=snake_case_     )

		if accelerator.state.deepspeed_plugin is not None:
				lowercase__     			=		accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				lowercase__     			=		1
		lowercase__     			=		(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
		):
				lowercase__     			=		get_linear_schedule_with_warmup(
				    optimizer=snake_case_    ,    num_warmup_steps=0    ,    num_training_steps=snake_case_    ,    )
		else:
				lowercase__     			=		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.
		lowercase__			,	lowercase__			,	lowercase__			,	lowercase__			,	lowercase__     			=		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
		lowercase__     			=		0
		# We also need to keep track of the stating epoch so files are named properly
		lowercase__     			=		0
		lowercase__     			=		evaluate.load("""glue"""    ,    """mrpc"""     )
		lowercase__     			=		num_epochs

		if args.partial_train_epoch is not None:
				lowercase__     			=		args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint     )
				lowercase__     			=		args.resume_from_checkpoint.split("""epoch_"""     )[1]
				lowercase__     			=		""""""
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowercase__     			=		int(snake_case_     ) + 1
				lowercase__     			=		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:
						lowercase__     			=		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
		lowercase__     			=		{}
		for epoch in range(snake_case_    ,    snake_case_     ):
				model.train()
				for step, batch in enumerate(snake_case_     ):
						lowercase__     			=		model(**snake_case_     )
						lowercase__     			=		outputs.loss
						lowercase__     			=		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
				lowercase__     			=		f'''epoch_{epoch}'''
				lowercase__     			=		os.path.join(args.output_dir    ,    snake_case_     )
				accelerator.save_state(snake_case_     )
				lowercase__     			=		evaluation_loop(snake_case_    ,    snake_case_    ,    snake_case_    ,    snake_case_     )
				lowercase__     			=		accuracy
				lowercase__     			=		lr_scheduler.get_lr()[0]
				lowercase__     			=		optimizer.param_groups[0]["""lr"""]
				lowercase__     			=		epoch
				lowercase__     			=		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  UpperCamelCase					(	)   ->  List[str]:
		"""simple docstring"""




		lowercase__     			=		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."""    ,    )
		lowercase__     			=		parser.parse_args()
		lowercase__     			=		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

		training_function(snake_case_    ,    snake_case_     )


if __name__ == "__main__":
	main()


'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class        __A      ( unittest.TestCase	):



    def __init__(self    :    str   ,			__a    :    Optional[Any]   ,			__a    :    Optional[Any]=13   ,			__a    :    int=30   ,			__a    :    Union[str, Any]=2   ,			__a    :    Dict=3   ,			__a    :    List[Any]=True   ,			__a    :    Optional[Any]=True   ,			__a    :    List[Any]=32   ,			__a    :    Any=5   ,			__a    :    str=4   ,			__a    :    Optional[int]=37   ,			__a    :    Optional[int]="gelu"   ,			__a    :    List[str]=0.1   ,			__a    :    Tuple=0.1   ,			__a    :    List[str]=10   ,			__a    :    Optional[int]=0.02   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   image_size
         UpperCAmelCase_           =   patch_size
         UpperCAmelCase_           =   num_channels
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   type_sequence_label_size
         UpperCAmelCase_           =   initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =   (image_size // patch_size) ** 2
         UpperCAmelCase_           =   num_patches + 1



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase_           =   ViTConfig(
             image_size=self.image_size   ,			patch_size=self.patch_size   ,			num_channels=self.num_channels   ,			hidden_size=self.hidden_size   ,			num_hidden_layers=self.num_hidden_layers   ,			num_attention_heads=self.num_attention_heads   ,			intermediate_size=self.intermediate_size   ,			hidden_act=self.hidden_act   ,			hidden_dropout_prob=self.hidden_dropout_prob   ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,			is_decoder=__a   ,			initializer_range=self.initializer_range   ,			)

         return config, pixel_values



    def 					_lowercase						(self    :    Dict   ,			__a    :    Any   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   FlaxViTModel(config=__a )
         UpperCAmelCase_           =   model(__a )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =   (self.image_size, self.image_size)
         UpperCAmelCase_           =   (self.patch_size, self.patch_size)
         UpperCAmelCase_           =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, num_patches + 1, self.hidden_size) )



    def 					_lowercase						(self    :    Tuple   ,			__a    :    str   ,			__a    :    Any ):
         UpperCAmelCase_           =   self.type_sequence_label_size
         UpperCAmelCase_           =   FlaxViTForImageClassification(config=__a )
         UpperCAmelCase_           =   model(__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.type_sequence_label_size) )

         # test greyscale images
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   FlaxViTForImageClassification(__a )

         UpperCAmelCase_           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         UpperCAmelCase_           =   model(__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , 
         )           =   config_and_inputs
         UpperCAmelCase_           =   {"pixel_values": pixel_values}
         return config, inputs_dict




@require_flax
class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Tuple          =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   FlaxViTModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			has_text_modality=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    Tuple ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   model_class(__a )
              UpperCAmelCase_           =   inspect.signature(model.__call__ )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_           =   [*signature.parameters.keys()]

              UpperCAmelCase_           =   ["pixel_values"]
              self.assertListEqual(arg_names[:1]   ,			__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                   UpperCAmelCase_           =   self._prepare_for_class(__a   ,			__a )
                   UpperCAmelCase_           =   model_class(__a )

                   @jax.jit
                   def model_jitted(__a    :    Tuple   ,			**__a    :    List[Any] ):
                        return model(pixel_values=__a   ,			**__a )

                   with self.subTest("JIT Enabled" ):
                        UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   with self.subTest("JIT Disabled" ):
                        with jax.disable_jit():
                             UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   self.assertEqual(len(__a )   ,			len(__a ) )
                   for jitted_output, output in zip(__a   ,			__a ):
                        self.assertEqual(jitted_output.shape   ,			output.shape )






    @slow
    def 					_lowercase						(self    :    Tuple ):
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_           =   model_class_name.from_pretrained("google/vit-base-patch16-224" )
              UpperCAmelCase_           =   model(np.ones((1, 3, 224, 224) ) )
              self.assertIsNotNone(__a )




