

import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase =				16
__UpperCAmelCase =				32
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase = 16      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Any					   =					AutoTokenizer.from_pretrained("""bert-base-cased"""      )
 UpperCAmelCase__					:  str					   =					load_dataset("""glue"""							,    """mrpc"""      )

 def tokenize_function(__UpperCamelCase      ):
  # max_length=None => use the model max length (it's actually the default)
  UpperCAmelCase__					:  Tuple					   =					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
 # starting with the main process first:
 with accelerator.main_process_first():
  UpperCAmelCase__					:  Any					   =					datasets.map(
      __UpperCamelCase							,    batched=__UpperCamelCase							,    remove_columns=["""idx""", """sentence1""", """sentence2"""]							,    )

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

 def collate_fn(__UpperCamelCase      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCAmelCase__					:  Dict					   =					128 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   UpperCAmelCase__					:  int					   =					16
  elif accelerator.mixed_precision != "no":
   UpperCAmelCase__					:  List[str]					   =					8
  else:
   UpperCAmelCase__					:  str					   =					None

  return tokenizer.pad(
      __UpperCamelCase							,    padding="""longest"""							,    max_length=__UpperCamelCase							,    pad_to_multiple_of=__UpperCamelCase							,    return_tensors="""pt"""							,    )

 # Instantiate dataloaders.
 UpperCAmelCase__					:  str					   =					DataLoader(
     tokenized_datasets["""train"""]							,    shuffle=__UpperCamelCase							,    collate_fn=__UpperCamelCase							,    batch_size=__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					DataLoader(
     tokenized_datasets["""validation"""]							,    shuffle=__UpperCamelCase							,    collate_fn=__UpperCamelCase							,    batch_size=__UpperCamelCase      )

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      __UpperCAmelCase =				mocked_dataloaders  # noqa: F811
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,    __UpperCamelCase      ) == "1":
  UpperCAmelCase__					:  List[str]					   =					2
 # New Code #
 UpperCAmelCase__					:  Union[str, Any]					   =					int(args.gradient_accumulation_steps      )
 # Initialize accelerator
 UpperCAmelCase__					:  Dict					   =					Accelerator(
     cpu=args.cpu							,    mixed_precision=args.mixed_precision							,    gradient_accumulation_steps=__UpperCamelCase      )
 if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
  raise NotImplementedError(
      """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""      )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCAmelCase__					:  Optional[int]					   =					config["""lr"""]
 UpperCAmelCase__					:  List[Any]					   =					int(config["""num_epochs"""]      )
 UpperCAmelCase__					:  Any					   =					int(config["""seed"""]      )
 UpperCAmelCase__					:  Any					   =					int(config["""batch_size"""]      )

 UpperCAmelCase__					:  Tuple					   =					evaluate.load("""glue"""							,    """mrpc"""      )

 set_seed(__UpperCamelCase      )
 UpperCAmelCase__					:  Any					   =					get_dataloaders(__UpperCamelCase							,    __UpperCamelCase      )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCAmelCase__					:  Dict					   =					AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,    return_dict=__UpperCamelCase      )

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 UpperCAmelCase__					:  Union[str, Any]					   =					model.to(accelerator.device      )

 # Instantiate optimizer
 UpperCAmelCase__					:  Optional[int]					   =					AdamW(params=model.parameters()							,    lr=__UpperCamelCase      )

 # Instantiate scheduler
 UpperCAmelCase__					:  Tuple					   =					get_linear_schedule_with_warmup(
     optimizer=__UpperCamelCase							,    num_warmup_steps=100							,    num_training_steps=(len(__UpperCamelCase      ) * num_epochs)							,    )

 # 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.
 UpperCAmelCase__					:  List[Any]					   =					accelerator.prepare(
     __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      )

 # Now we train the model
 for epoch in range(__UpperCamelCase      ):
  model.train()
  for step, batch in enumerate(__UpperCamelCase      ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device      )
   # New code #
   # We use the new `accumulate` context manager to perform gradient accumulation
   # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
   with accelerator.accumulate(__UpperCamelCase      ):
    UpperCAmelCase__					:  Optional[Any]					   =					model(**__UpperCamelCase      )
    UpperCAmelCase__					:  Optional[Any]					   =					output.loss
    accelerator.backward(__UpperCamelCase      )
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  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():
    UpperCAmelCase__					:  Optional[Any]					   =					model(**__UpperCamelCase      )
   UpperCAmelCase__					:  Optional[int]					   =					outputs.logits.argmax(dim=-1      )
   UpperCAmelCase__					:  List[Any]					   =					accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
   metric.add_batch(
       predictions=__UpperCamelCase							,    references=__UpperCamelCase							,    )

  UpperCAmelCase__					:  Optional[int]					   =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"							,    __UpperCamelCase      )
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					argparse.ArgumentParser(description="""Simple example of training script."""      )
 parser.add_argument(
     """--mixed_precision"""							,    type=__UpperCamelCase							,    default=__UpperCamelCase							,    choices=["""no""", """fp16""", """bf16""", """fp8"""]							,    help="""Whether to use mixed precision. Choose"""
     """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
     """and an Nvidia Ampere GPU."""							,    )
 # New Code #
 parser.add_argument(
     """--gradient_accumulation_steps"""							,    type=__UpperCamelCase							,    default=1							,    help="""The number of minibatches to be ran before gradients are accumulated."""							,    )
 parser.add_argument("""--cpu"""							,    action="""store_true"""							,    help="""If passed, will train on the CPU."""      )
 UpperCAmelCase__					:  int					   =					parser.parse_args()
 UpperCAmelCase__					:  Optional[int]					   =					{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(__UpperCamelCase							,    __UpperCamelCase      )


if __name__ == "__main__":
      main()








"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
          TFDeiTModel,
      )
      from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor







class      __lowercase			:

  def __init__(  self		:			Optional[int]					,A		:			Optional[Any]					,A		:			int=13					,A		:			Any=30					,A		:			str=2					,A		:			Tuple=3					,A		:			Optional[Any]=True					,A		:			Union[str, Any]=True					,A		:			Dict=32					,A		:			Optional[int]=2					,A		:			Any=4					,A		:			str=37					,A		:			int="gelu"					,A		:			Dict=0.1					,A		:			str=0.1					,A		:			Optional[Any]=10					,A		:			List[str]=0.0_2					,A		:			Any=3					,A		:			Any=None					,A		:			int=2					,):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					parent
   UpperCAmelCase__					:  str					   =					batch_size
   UpperCAmelCase__					:  Tuple					   =					image_size
   UpperCAmelCase__					:  int					   =					patch_size
   UpperCAmelCase__					:  Dict					   =					num_channels
   UpperCAmelCase__					:  List[Any]					   =					is_training
   UpperCAmelCase__					:  Optional[Any]					   =					use_labels
   UpperCAmelCase__					:  Tuple					   =					hidden_size
   UpperCAmelCase__					:  List[str]					   =					num_hidden_layers
   UpperCAmelCase__					:  Dict					   =					num_attention_heads
   UpperCAmelCase__					:  List[Any]					   =					intermediate_size
   UpperCAmelCase__					:  Optional[Any]					   =					hidden_act
   UpperCAmelCase__					:  Optional[Any]					   =					hidden_dropout_prob
   UpperCAmelCase__					:  str					   =					attention_probs_dropout_prob
   UpperCAmelCase__					:  Dict					   =					type_sequence_label_size
   UpperCAmelCase__					:  Optional[int]					   =					initializer_range
   UpperCAmelCase__					:  Tuple					   =					scope
   UpperCAmelCase__					:  str					   =					encoder_stride

   # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
   UpperCAmelCase__					:  str					   =					(image_size // patch_size) ** 2
   UpperCAmelCase__					:  int					   =					num_patches + 2

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

   UpperCAmelCase__					:  List[str]					   =					None
   if self.use_labels:
    UpperCAmelCase__					:  Optional[Any]					   =					ids_tensor([self.batch_size]					,self.type_sequence_label_size    )

   UpperCAmelCase__					:  int					   =					self.get_config()

   return config, pixel_values, labels

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   return DeiTConfig(
       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					,encoder_stride=self.encoder_stride					,)

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Optional[int]					,A		:			Optional[Any]					,A		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					TFDeiTModel(config=A    )
   UpperCAmelCase__					:  Dict					   =					model(A    )
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)    )

  def    __lowercase       (  self		:			Optional[int]					,A		:			Union[str, Any]					,A		:			Optional[int]					,A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					TFDeiTForMaskedImageModeling(config=A    )
   UpperCAmelCase__					:  Any					   =					model(A    )
   self.parent.assertEqual(
       result.reconstruction.shape					,(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

   # test greyscale images
   UpperCAmelCase__					:  Union[str, Any]					   =					1
   UpperCAmelCase__					:  Any					   =					TFDeiTForMaskedImageModeling(A    )

   UpperCAmelCase__					:  str					   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
   UpperCAmelCase__					:  str					   =					model(A    )
   self.parent.assertEqual(result.reconstruction.shape					,(self.batch_size, 1, self.image_size, self.image_size)    )

  def    __lowercase       (  self		:			Dict					,A		:			Any					,A		:			Optional[Any]					,A		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.type_sequence_label_size
   UpperCAmelCase__					:  int					   =					TFDeiTForImageClassification(A    )
   UpperCAmelCase__					:  int					   =					model(A					,labels=A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)    )

   # test greyscale images
   UpperCAmelCase__					:  Optional[int]					   =					1
   UpperCAmelCase__					:  int					   =					TFDeiTForImageClassification(A    )

   UpperCAmelCase__					:  List[str]					   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
   UpperCAmelCase__					:  str					   =					model(A					,labels=A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)    )




  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.prepare_config_and_inputs()
   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[Any]					   =					config_and_inputs
   UpperCAmelCase__					:  Dict					   =					{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_tf
class      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       (
      (
          TFDeiTModel,
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  snake_case_				       =       (
      {
          """feature-extraction""": TFDeiTModel,
          """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
      }
      if is_tf_available()
      else {}
  )

  snake_case_				       =       False
  snake_case_				       =       False
  snake_case_				       =       False
  snake_case_				       =       False

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					TFDeiTModelTester(self    )
   UpperCAmelCase__					:  int					   =					ConfigTester(self					,config_class=A					,has_text_modality=A					,hidden_size=37    )

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   self.config_tester.run_common_tests()

  @unittest.skip(reason="""DeiT does not use inputs_embeds"""    )
  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__					:  int					   =					model_class(A    )
    self.assertIsInstance(model.get_input_embeddings()					,(tf.keras.layers.Layer)    )
    UpperCAmelCase__					:  Tuple					   =					model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(A					,tf.keras.layers.Dense    )    )

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					self.model_tester.prepare_config_and_inputs_for_common()

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

    UpperCAmelCase__					:  Optional[int]					   =					["""pixel_values"""]
    self.assertListEqual(arg_names[:1]					,A    )

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*A    )

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*A    )

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*A    )

  def    __lowercase       (  self		:			str					,A		:			List[str]					,A		:			Optional[int]					,A		:			Union[str, Any]=False    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					super()._prepare_for_class(A					,A					,return_labels=A    )

   if return_labels:
    if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call    ).parameters:
     del inputs_dict["labels"]

   return inputs_dict




  @slow
  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__					:  List[Any]					   =					TFDeiTModel.from_pretrained(A    )
    self.assertIsNotNone(A    )







def   lowerCAmelCase						(					):
 '''simple docstring'''





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







@require_tf
@require_vision
class      __lowercase			(    unittest.TestCase					):

  @cached_property
  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   return (
       DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""    )
       if is_vision_available()
       else None
   )




  @slow
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""    )

   UpperCAmelCase__					:  Union[str, Any]					   =					self.default_image_processor
   UpperCAmelCase__					:  Optional[int]					   =					prepare_img()
   UpperCAmelCase__					:  Optional[int]					   =					image_processor(images=A					,return_tensors="""tf"""    )

   # forward pass
   UpperCAmelCase__					:  Optional[Any]					   =					model(**A    )

   # verify the logits
   UpperCAmelCase__					:  int					   =					tf.TensorShape((1, 1_000)    )
   self.assertEqual(outputs.logits.shape					,A    )

   UpperCAmelCase__					:  Any					   =					tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]    )

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





