





"""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 		lowercase					(					unittest.TestCase					):





      def __init__(       self      ,						lowercase      ,						lowercase=100      ,						lowercase=13      ,						lowercase=30      ,						lowercase=2      ,						lowercase=3      ,						lowercase=True      ,						lowercase=True      ,						lowercase=32      ,						lowercase=5      ,						lowercase=4      ,						lowercase=37      ,						lowercase="gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=10      ,						lowercase=0.02      ,						lowercase=3      ,						)		->     str:
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       batch_size
            lowerCAmelCase  						=       image_size
            lowerCAmelCase  						=       patch_size
            lowerCAmelCase  						=       num_channels
            lowerCAmelCase  						=       is_training
            lowerCAmelCase  						=       use_labels
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       intermediate_size
            lowerCAmelCase  						=       hidden_act
            lowerCAmelCase  						=       hidden_dropout_prob
            lowerCAmelCase  						=       attention_probs_dropout_prob
            lowerCAmelCase  						=       type_sequence_label_size
            lowerCAmelCase  						=       initializer_range

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





      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

            lowerCAmelCase  						=       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=lowerCAmelCase_      ,						initializer_range=self.initializer_range      ,						)

            return config, pixel_values, labels





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     Any:
            lowerCAmelCase  						=       FlaxBeitModel(config=lowerCAmelCase_ )
            lowerCAmelCase  						=       model(lowerCAmelCase_ )
            self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     List[Any]:
            lowerCAmelCase  						=       FlaxBeitForMaskedImageModeling(config=lowerCAmelCase_ )
            lowerCAmelCase  						=       model(lowerCAmelCase_ )
            self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length - 1, self.vocab_size) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     List[str]:
            lowerCAmelCase  						=       self.type_sequence_label_size
            lowerCAmelCase  						=       FlaxBeitForImageClassification(config=lowerCAmelCase_ )
            lowerCAmelCase  						=       model(lowerCAmelCase_ )
            self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size) )

            # test greyscale images
            lowerCAmelCase  						=       1
            lowerCAmelCase  						=       FlaxBeitForImageClassification(lowerCAmelCase_ )

            lowerCAmelCase  						=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
            lowerCAmelCase  						=       model(lowerCAmelCase_ )





      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.prepare_config_and_inputs()
            (
                (
                lowerCAmelCase  
            )						,		(
                lowerCAmelCase  
            )						,		(
                lowerCAmelCase  
            )						,		  
            )  						=       config_and_inputs
            lowerCAmelCase  						=       {"""pixel_values""": pixel_values}
            return config, inputs_dict







@require_flax
class 		lowercase					(					__lowerCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      (
          (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
      )





      def     _snake_case			(       self )		->     None:
            lowerCAmelCase  						=       FlaxBeitModelTester(self )
            lowerCAmelCase  						=       ConfigTester(self      ,						config_class=lowerCAmelCase_      ,						has_text_modality=lowerCAmelCase_      ,						hidden_size=37 )





      def     _snake_case			(       self )		->     Tuple:
            self.config_tester.run_common_tests()





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowerCAmelCase  						=       ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]      ,						lowerCAmelCase_ )





      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__ ):
                        lowerCAmelCase  						=       self._prepare_for_class(lowerCAmelCase_      ,						lowerCAmelCase_ )
                        lowerCAmelCase  						=       model_class(lowerCAmelCase_ )

                        @jax.jit
                        def model_jitted(lowercase      ,						**lowercase ):
                              return model(pixel_values=lowerCAmelCase_      ,						**lowerCAmelCase_ )

                        with self.subTest("""JIT Enabled""" ):
                              lowerCAmelCase  						=       model_jitted(**lowerCAmelCase_ ).to_tuple()

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

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





      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase_ )





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_ )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_ )





      @slow
      def     _snake_case			(       self )		->     Optional[int]:
            for model_class_name in self.all_model_classes:
                  lowerCAmelCase  						=       model_class_name.from_pretrained("""microsoft/beit-base-patch16-224""" )
                  lowerCAmelCase  						=       model(np.ones((1, 3, 224, 224) ) )
                  self.assertIsNotNone(lowerCAmelCase_ )







def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




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






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





      @cached_property
      def     _snake_case			(       self )		->     str:
            return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224""" ) if is_vision_available() else None





      @slow
      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""" )

            lowerCAmelCase  						=       self.default_image_processor
            lowerCAmelCase  						=       prepare_img()
            lowerCAmelCase  						=       image_processor(images=lowerCAmelCase_      ,						return_tensors="""np""" ).pixel_values

            # prepare bool_masked_pos
            lowerCAmelCase  						=       np.ones((1, 196)      ,						dtype=lowerCAmelCase_ )

            # forward pass
            lowerCAmelCase  						=       model(pixel_values=lowerCAmelCase_      ,						bool_masked_pos=lowerCAmelCase_ )
            lowerCAmelCase  						=       outputs.logits

            # verify the logits
            lowerCAmelCase  						=       (1, 196, 8_192)
            self.assertEqual(logits.shape      ,						lowerCAmelCase_ )

            lowerCAmelCase  						=       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]      ,						lowerCAmelCase_      ,						atol=1e-2 ) )





      @slow
      def     _snake_case			(       self )		->     Dict:
            lowerCAmelCase  						=       FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""" )

            lowerCAmelCase  						=       self.default_image_processor
            lowerCAmelCase  						=       prepare_img()
            lowerCAmelCase  						=       image_processor(images=lowerCAmelCase_      ,						return_tensors="""np""" )

            # forward pass
            lowerCAmelCase  						=       model(**lowerCAmelCase_ )
            lowerCAmelCase  						=       outputs.logits

            # verify the logits
            lowerCAmelCase  						=       (1, 1_000)
            self.assertEqual(logits.shape      ,						lowerCAmelCase_ )

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

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

            lowerCAmelCase  						=       281
            self.assertEqual(logits.argmax(-1 ).item()      ,						lowerCAmelCase_ )





      @slow
      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""" )

            lowerCAmelCase  						=       self.default_image_processor
            lowerCAmelCase  						=       prepare_img()
            lowerCAmelCase  						=       image_processor(images=lowerCAmelCase_      ,						return_tensors="""np""" )

            # forward pass
            lowerCAmelCase  						=       model(**lowerCAmelCase_ )
            lowerCAmelCase  						=       outputs.logits

            # verify the logits
            lowerCAmelCase  						=       (1, 21_841)
            self.assertEqual(logits.shape      ,						lowerCAmelCase_ )

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

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

            lowerCAmelCase  						=       2_396
            self.assertEqual(logits.argmax(-1 ).item()      ,						lowerCAmelCase_ )







import argparse
import gc
import json
import os

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


_SCREAMING_SNAKE_CASE										=						16
_SCREAMING_SNAKE_CASE										=						32






def  snake_case  (       snake_case__			:Optional[int])						->   str:
       return int(x / 2**20)



class 				a							:







 """simple docstring"""






 def __enter__(					self							)  ->					List[str]:
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        _A        =  torch.cuda.memory_allocated()
        return self



 def __exit__(					self		,				*lowerCAmelCase_							)  ->					Optional[int]:
        gc.collect()
        torch.cuda.empty_cache()
        _A        =  torch.cuda.memory_allocated()
        _A        =  torch.cuda.max_memory_allocated()
        _A        =  bamb(self.end - self.begin							)
        _A        =  bamb(self.peak - self.begin							)
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  snake_case  (       snake_case__			:Accelerator   ,	snake_case__			:int = 16   ,	snake_case__			:str = "bert-base-cased"   ,	snake_case__			:int = 320   ,	snake_case__			:int = 160   ,	)						->   Dict:
       _A        =  AutoTokenizer.from_pretrained(snake_case__)
       _A        =  load_dataset(
           """glue"""   ,	"""mrpc"""   ,	split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''})

       def tokenize_function(snake_case__			:Optional[int]):
              # max_length=None => use the model max length (it's actually the default)
              _A        =  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
       _A        =  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
       _A        =  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=128   ,	return_tensors="""pt""")
              return tokenizer.pad(snake_case__   ,	padding="""longest"""   ,	return_tensors="""pt""")

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

       return train_dataloader, eval_dataloader






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Optional[int])						->   Optional[int]:
       # Initialize accelerator
       _A        =  Accelerator()

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

       set_seed(snake_case__)
       _A   ,  _A        =  get_dataloaders(snake_case__   ,	snake_case__   ,	snake_case__   ,	args.n_train   ,	args.n_val)

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

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

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

       # Now we train the model
       _A        =  {}
       for epoch in range(snake_case__   ,	snake_case__):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(snake_case__):
                            _A        =  model(**snake_case__)
                            _A        =  outputs.loss
                            _A        =  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

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
              accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin)))
              accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used))
              accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked))
              accelerator.print(
                  """Total Peak Memory consumed during the train (max): {}""".format(
                      tracemalloc.peaked + bamb(tracemalloc.begin)))
              _A        =  tracemalloc.peaked + bamb(tracemalloc.begin)
              if args.peak_memory_upper_bound is not None:
                     assert (
                         train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
                     ), "Peak memory usage exceeded the upper bound"

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






def  snake_case  (       )						->   Optional[int]:
       _A        =  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(
           """--peak_memory_upper_bound"""   ,	type=snake_case__   ,	default=snake_case__   ,	help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""   ,	)
       parser.add_argument(
           """--n_train"""   ,	type=snake_case__   ,	default=320   ,	help="""Number of training examples to use."""   ,	)
       parser.add_argument(
           """--n_val"""   ,	type=snake_case__   ,	default=160   ,	help="""Number of validation examples to use."""   ,	)
       parser.add_argument(
           """--num_epochs"""   ,	type=snake_case__   ,	default=1   ,	help="""Number of train epochs."""   ,	)
       _A        =  parser.parse_args()
       _A        =  {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(snake_case__   ,	snake_case__)


if __name__ == "__main__":
    main()




