from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__  =					logging.get_logger(__name__)

lowerCamelCase__  =					{
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}




class 					_UpperCAmelCase			(     SCREAMING_SNAKE_CASE__  ):



 '''simple docstring'''




 __A			       =					'''table-transformer'''
 __A			       =					['''past_key_values''']
 __A			       =					{
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }







 def __init__(		self				:   List[Any]							, lowercase_				:   Optional[Any]=True							, lowercase_				:   str=None							, lowercase_				:   Optional[Any]=3							, lowercase_				:   Any=100							, lowercase_				:   List[str]=6							, lowercase_				:   Optional[Any]=2048							, lowercase_				:   Optional[Any]=8							, lowercase_				:   Union[str, Any]=6							, lowercase_				:   int=2048							, lowercase_				:   Union[str, Any]=8							, lowercase_				:   List[str]=0.0							, lowercase_				:   int=0.0							, lowercase_				:   int=True							, lowercase_				:   str="relu"							, lowercase_				:   List[Any]=256							, lowercase_				:   List[str]=0.1							, lowercase_				:   str=0.0							, lowercase_				:   Any=0.0							, lowercase_				:   Optional[int]=0.02							, lowercase_				:   Optional[int]=1.0							, lowercase_				:   List[Any]=False							, lowercase_				:   int="sine"							, lowercase_				:   List[str]="resnet50"							, lowercase_				:   Optional[int]=True							, lowercase_				:   List[Any]=False							, lowercase_				:   List[str]=1							, lowercase_				:   str=5							, lowercase_				:   List[str]=2							, lowercase_				:   Dict=1							, lowercase_				:   str=1							, lowercase_				:   Optional[Any]=5							, lowercase_				:   str=2							, lowercase_				:   Optional[Any]=0.1							, **lowercase_				:   Dict							, )						->	int:


   """simple docstring"""







   if backbone_config is not None and use_timm_backbone:
     raise ValueError("You can\'t specify both `backbone_config` and `use_timm_backbone`.")

   if not use_timm_backbone:
     if backbone_config is None:
       logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
       _UpperCamelCase           =    CONFIG_MAPPING['''resnet'''](out_features=["stage4"])
     elif isinstance(A_							, A_):
       _UpperCamelCase           =    backbone_config.get("model_type")
       _UpperCamelCase           =    CONFIG_MAPPING[backbone_model_type]
       _UpperCamelCase           =    config_class.from_dict(A_)
     # set timm attributes to None
     _UpperCamelCase           =    None, None, None

   _UpperCamelCase           =    use_timm_backbone
   _UpperCamelCase           =    backbone_config
   _UpperCamelCase           =    num_channels
   _UpperCamelCase           =    num_queries
   _UpperCamelCase           =    d_model
   _UpperCamelCase           =    encoder_ffn_dim
   _UpperCamelCase           =    encoder_layers
   _UpperCamelCase           =    encoder_attention_heads
   _UpperCamelCase           =    decoder_ffn_dim
   _UpperCamelCase           =    decoder_layers
   _UpperCamelCase           =    decoder_attention_heads
   _UpperCamelCase           =    dropout
   _UpperCamelCase           =    attention_dropout
   _UpperCamelCase           =    activation_dropout
   _UpperCamelCase           =    activation_function
   _UpperCamelCase           =    init_std
   _UpperCamelCase           =    init_xavier_std
   _UpperCamelCase           =    encoder_layerdrop
   _UpperCamelCase           =    decoder_layerdrop
   _UpperCamelCase           =    encoder_layers
   _UpperCamelCase           =    auxiliary_loss
   _UpperCamelCase           =    position_embedding_type
   _UpperCamelCase           =    backbone
   _UpperCamelCase           =    use_pretrained_backbone
   _UpperCamelCase           =    dilation
   # Hungarian matcher
   _UpperCamelCase           =    class_cost
   _UpperCamelCase           =    bbox_cost
   _UpperCamelCase           =    giou_cost
   # Loss coefficients
   _UpperCamelCase           =    mask_loss_coefficient
   _UpperCamelCase           =    dice_loss_coefficient
   _UpperCamelCase           =    bbox_loss_coefficient
   _UpperCamelCase           =    giou_loss_coefficient
   _UpperCamelCase           =    eos_coefficient
   super().__init__(is_encoder_decoder=A_							, **A_)







 @property
 def 		__UpperCAmelCase				(		self				:   Tuple)						->	int:


   """simple docstring"""







   return self.encoder_attention_heads







 @property
 def 		__UpperCAmelCase				(		self				:   Dict)						->	int:


   """simple docstring"""







   return self.d_model




class 					_UpperCAmelCase			(     SCREAMING_SNAKE_CASE__  ):



 '''simple docstring'''




 __A			       =					version.parse('''1.11'''  )







 @property
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Mapping[str, Mapping[int, str]]:


   """simple docstring"""







   return OrderedDict(
       [
           ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ("pixel_mask", {0: "batch"}),
       ])







 @property
 def 		__UpperCAmelCase				(		self				:   List[Any])						->	float:


   """simple docstring"""







   return 1e-5







 @property
 def 		__UpperCAmelCase				(		self				:   List[str])						->	int:


   """simple docstring"""







   return 12






"""simple docstring"""




import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


A__ :       List[Any]				= 1_6
A__ :       Union[str, Any]				= 3_2




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase = 16    ):






  """simple docstring"""



  _lowercase:      Any   			=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
  _lowercase:      List[str]   			=  load_dataset('''glue'''			,					'''mrpc'''    )

  def tokenize_function(_UpperCamelCase    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase:      Union[str, Any]   			=  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():
    _lowercase:      Tuple   			=  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
  _lowercase:      Optional[Any]   			=  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.
    _lowercase:      Union[str, Any]   			=  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":
      _lowercase:      int   			=  16
    elif accelerator.mixed_precision != "no":
      _lowercase:      Tuple   			=  8
    else:
      _lowercase:      str   			=  None

    return tokenizer.pad(
        _UpperCamelCase			,					padding='''longest'''			,					max_length=_UpperCamelCase			,					pad_to_multiple_of=_UpperCamelCase			,					return_tensors='''pt'''			,					)

  # Instantiate dataloaders.
  _lowercase:      List[Any]   			=  DataLoader(
      tokenized_datasets['''train''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )
  _lowercase:      int   			=  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

  A__ :       List[Any]				= mocked_dataloaders  # noqa: F811




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,					_UpperCamelCase    ) == "1":
    _lowercase:      Tuple   			=  2
  # Initialize accelerator
  _lowercase:      Optional[Any]   			=  Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase:      Optional[Any]   			=  config['''lr''']
  _lowercase:      Tuple   			=  int(config['''num_epochs''']    )
  _lowercase:      Any   			=  int(config['''seed''']    )
  _lowercase:      List[Any]   			=  int(config['''batch_size''']    )

  _lowercase:      Optional[int]   			=  evaluate.load('''glue'''			,					'''mrpc'''    )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=_UpperCamelCase    )
  def inner_training_loop(_UpperCamelCase    ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(_UpperCamelCase    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase:      Optional[int]   			=  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).
    _lowercase:      List[Any]   			=  model.to(accelerator.device    )

    # Instantiate optimizer
    _lowercase:      List[str]   			=  AdamW(params=model.parameters()			,					lr=_UpperCamelCase    )
    _lowercase			,					_lowercase:      Tuple   			=  get_dataloaders(_UpperCamelCase			,					_UpperCamelCase    )

    # Instantiate scheduler
    _lowercase:      str   			=  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.
    _lowercase			,					_lowercase			,					_lowercase			,					_lowercase			,					_lowercase:      Union[str, 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    )
        _lowercase:      Tuple   			=  model(**_UpperCamelCase    )
        _lowercase:      Union[str, Any]   			=  outputs.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():
          _lowercase:      str   			=  model(**_UpperCamelCase    )
        _lowercase:      int   			=  outputs.logits.argmax(dim=-1    )
        _lowercase			,					_lowercase:      List[str]   			=  accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
        metric.add_batch(
            predictions=_UpperCamelCase			,					references=_UpperCamelCase			,					)

      _lowercase:      int   			=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,					_UpperCamelCase    )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  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.'''			,					)
  parser.add_argument('''--cpu'''			,					action='''store_true'''			,					help='''If passed, will train on the CPU.'''    )
  _lowercase:      Tuple   			=  parser.parse_args()
  _lowercase:      str   			=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  main()
