

"""simple docstring"""





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       =							16
_a       =							32




def   _A  (  UpperCamelCase_     :       Accelerator,			UpperCamelCase_     :       int = 16,			UpperCamelCase_     :       str = "bert-base-cased")	->						List[str]:
   '''simple docstring'''


   __lowercase								=						AutoTokenizer.from_pretrained(UpperCamelCase_)
   __lowercase								=						load_dataset("glue",			"mrpc")

   def tokenize_function(UpperCamelCase_     :       Optional[Any]):
      # max_length=None => use the model max length (it's actually the default)
      __lowercase								=						tokenizer(examples["sentence1"],			examples["sentence2"],			truncation=UpperCamelCase_,			max_length=UpperCamelCase_)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   __lowercase								=						datasets.map(
       UpperCamelCase_,			batched=UpperCamelCase_,			remove_columns=["idx", "sentence1", "sentence2"],			load_from_cache_file=UpperCamelCase_)

   # 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(UpperCamelCase_     :       Tuple):
      # 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(UpperCamelCase_,			padding="max_length",			max_length=128,			return_tensors="pt")
      return tokenizer.pad(UpperCamelCase_,			padding="longest",			return_tensors="pt")

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

   return train_dataloader, eval_dataloader




def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Tuple)	->						Tuple:
   '''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(UpperCamelCase_)
   __lowercase		,__lowercase								=						get_dataloaders(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowercase								=						AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_,			return_dict=UpperCamelCase_)

   # 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=UpperCamelCase_)

   if accelerator.state.deepspeed_plugin is not None:
      __lowercase								=						accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      __lowercase								=						1
   __lowercase								=						(len(UpperCamelCase_) * 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=UpperCamelCase_,			num_warmup_steps=0,			num_training_steps=UpperCamelCase_,			)
   else:
      __lowercase								=						DummyScheduler(UpperCamelCase_,			total_num_steps=UpperCamelCase_,			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(
       UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)

   # 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

   # Now we train the model
   __lowercase								=						evaluate.load("glue",			"mrpc")
   __lowercase								=						0
   __lowercase								=						{}
   for epoch in range(UpperCamelCase_,			UpperCamelCase_):
      model.train()
      for step, batch in enumerate(UpperCamelCase_):
         __lowercase								=						model(**UpperCamelCase_)
         __lowercase								=						outputs.loss
         __lowercase								=						loss / gradient_accumulation_steps
         accelerator.backward(UpperCamelCase_)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

      model.eval()
      __lowercase								=						0
      for step, batch in enumerate(UpperCamelCase_):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device)
         with torch.no_grad():
            __lowercase								=						model(**UpperCamelCase_)
         __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(UpperCamelCase_) - 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=UpperCamelCase_,			references=UpperCamelCase_,			)

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

      if best_performance < eval_metric["accuracy"]:
         __lowercase								=						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(UpperCamelCase_,			UpperCamelCase_)




def   _A  (  )	->						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=UpperCamelCase_,			default="bert-base-cased",			help="Path to pretrained model or model identifier from huggingface.co/models.",			required=UpperCamelCase_,			)
   parser.add_argument(
       "--output_dir",			type=UpperCamelCase_,			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=UpperCamelCase_,			default=UpperCamelCase_,			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=UpperCamelCase_,			default=3,			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(UpperCamelCase_,			UpperCamelCase_)


if __name__ == "__main__":
   main()







"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

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


if is_flax_available():
   import jax

   from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor




class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def __init__(			self							:			str,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			int=1_0_0,					UpperCAmelCase__							:			Any=1_3,					UpperCAmelCase__							:			List[Any]=3_0,					UpperCAmelCase__							:			Dict=2,					UpperCAmelCase__							:			Any=3,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			List[str]=True,					UpperCAmelCase__							:			Optional[Any]=3_2,					UpperCAmelCase__							:			Any=5,					UpperCAmelCase__							:			Any=4,					UpperCAmelCase__							:			Any=3_7,					UpperCAmelCase__							:			Optional[int]="gelu",					UpperCAmelCase__							:			Dict=0.1,					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Dict=1_0,					UpperCAmelCase__							:			Tuple=0.02,					UpperCAmelCase__							:			List[Any]=3,					):
    __lowercase								=						parent
    __lowercase								=						vocab_size
    __lowercase								=						batch_size
    __lowercase								=						image_size
    __lowercase								=						patch_size
    __lowercase								=						num_channels
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range

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


 def 					_lowercase (			self							:			int	):
    __lowercase								=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

    __lowercase								=						None
    if self.use_labels:
       __lowercase								=						ids_tensor([self.batch_size],					self.type_sequence_label_size	)

    __lowercase								=						BeitConfig(
        vocab_size=self.vocab_size,					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=UpperCAmelCase__,					initializer_range=self.initializer_range,					)

    return config, pixel_values, labels


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						FlaxBeitModel(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length - 1, self.vocab_size)	)


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any]	):
    __lowercase								=						self.type_sequence_label_size
    __lowercase								=						FlaxBeitForImageClassification(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)	)

    # test greyscale images
    __lowercase								=						1
    __lowercase								=						FlaxBeitForImageClassification(UpperCAmelCase__	)

    __lowercase								=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
    __lowercase								=						model(UpperCAmelCase__	)



 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs
    __lowercase								=						{"pixel_values": pixel_values}
    return config, inputs_dict







@require_flax
class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						FlaxBeitModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					has_text_modality=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowercase								=						["pixel_values"]
       self.assertListEqual(arg_names[:1],					UpperCAmelCase__	)


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

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__	):
          __lowercase								=						self._prepare_for_class(UpperCAmelCase__,					UpperCAmelCase__	)
          __lowercase								=						model_class(UpperCAmelCase__	)

          @jax.jit
          def model_jitted(UpperCAmelCase__							:			str,					**UpperCAmelCase__							:			Dict	):
             return model(pixel_values=UpperCAmelCase__,					**UpperCAmelCase__	)

          with self.subTest("JIT Enabled"	):
             __lowercase								=						model_jitted(**UpperCAmelCase__	).to_tuple()

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

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


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			Union[str, Any]	):
    for model_class_name in self.all_model_classes:
       __lowercase								=						model_class_name.from_pretrained("microsoft/beit-base-patch16-224"	)
       __lowercase								=						model(np.ones((1, 3, 2_2_4, 2_2_4)	)	)
       self.assertIsNotNone(UpperCAmelCase__	)







def   _A  (  )	->						str:
   '''simple docstring'''


   __lowercase								=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
   return image




@require_vision
@require_flax
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @cached_property
 def 					_lowercase (			self							:			Optional[int]	):
    return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"	) if is_vision_available() else None


 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	).pixel_values

    # prepare bool_masked_pos
    __lowercase								=						np.ones((1, 1_9_6),					dtype=UpperCAmelCase__	)

    # forward pass
    __lowercase								=						model(pixel_values=UpperCAmelCase__,					bool_masked_pos=UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_9_6, 8_1_9_2)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array(
        [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]	)

    self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3],					UpperCAmelCase__,					atol=1E-2	)	)


 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_0_0_0)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array([-1.2_385, -1.0_987, -1.0_108]	)

    self.assertTrue(np.allclose(logits[0, :3],					UpperCAmelCase__,					atol=1E-4	)	)

    __lowercase								=						2_8_1
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 2_1_8_4_1)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array([1.6_881, -0.2_787, 0.5_901]	)

    self.assertTrue(np.allclose(logits[0, :3],					UpperCAmelCase__,					atol=1E-4	)	)

    __lowercase								=						2_3_9_6
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)




