


import argparse

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
#
# 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
#
########################################################################


snake_case				:    List[Any]       				=     16
snake_case				:    Tuple       				=     32
def 				__lowerCamelCase						( UpperCAmelCase_						:		Accelerator ,  UpperCAmelCase_						:		int = 16						):




      """simple docstring"""





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

      def tokenize_function(UpperCAmelCase_						:		int						):
            # max_length=None => use the model max length (it's actually the default)
            a						:Optional[int] 							=       tokenizer(examples['''sentence1'''] ,  examples['''sentence2'''] ,  truncation=__lowerCamelCase ,  max_length=__lowerCamelCase						)
            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():
            a						:Optional[Any] 							=       datasets.map(
                __lowerCamelCase ,  batched=__lowerCamelCase ,  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
      a						:List[Any] 							=       tokenized_datasets.rename_column('''label''' ,  '''labels'''						)

      def collate_fn(UpperCAmelCase_						:		Union[str, Any]						):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            a						:int 							=       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":
                  a						:Union[str, Any] 							=       16
            elif accelerator.mixed_precision != "no":
                  a						:Tuple 							=       8
            else:
                  a						:Any 							=       None

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

      # Instantiate dataloaders.
      a						:List[str] 							=       DataLoader(
          tokenized_datasets['''train'''] ,  shuffle=__lowerCamelCase ,  collate_fn=__lowerCamelCase ,  batch_size=__lowerCamelCase ,  drop_last=__lowerCamelCase						)
      a						:Optional[int] 							=       DataLoader(
          tokenized_datasets['''validation'''] ,  shuffle=__lowerCamelCase ,  collate_fn=__lowerCamelCase ,  batch_size=__lowerCamelCase ,  drop_last=(accelerator.mixed_precision == '''fp8''') ,  )

      return train_dataloader, eval_dataloader
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:Dict 							=       Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      a						:Dict 							=       config['''lr''']
      a						:List[Any] 							=       int(config['''num_epochs''']						)
      a						:Union[str, Any] 							=       int(config['''seed''']						)
      a						:Dict 							=       int(config['''batch_size''']						)

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

      # If the batch size is too big we use gradient accumulation
      a						:Tuple 							=       1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            a						:Optional[int] 							=       batch_size // MAX_GPU_BATCH_SIZE
            a						:List[str] 							=       MAX_GPU_BATCH_SIZE

      set_seed(__lowerCamelCase						)
      a							,				a						:str 							=       get_dataloaders(__lowerCamelCase ,  __lowerCamelCase						)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      a						:int 							=       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,  return_dict=__lowerCamelCase						)

      # 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).
      a						:Any 							=       model.to(accelerator.device						)
      # Instantiate optimizer
      a						:Union[str, Any] 							=       AdamW(params=model.parameters() ,  lr=__lowerCamelCase						)

      # Instantiate scheduler
      a						:Union[str, Any] 							=       get_linear_schedule_with_warmup(
          optimizer=__lowerCamelCase ,  num_warmup_steps=100 ,  num_training_steps=(len(__lowerCamelCase						) * num_epochs) // gradient_accumulation_steps ,  )

      # 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						:str 							=       accelerator.prepare(
          __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase						)

      # Now we train the model
      for epoch in range(__lowerCamelCase						):
            model.train()
            for step, batch in enumerate(__lowerCamelCase						):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device						)
                  a						:Optional[Any] 							=       model(**__lowerCamelCase						)
                  a						:Any 							=       outputs.loss
                  a						:Any 							=       loss / gradient_accumulation_steps
                  accelerator.backward(__lowerCamelCase						)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(__lowerCamelCase						):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device						)
                  with torch.no_grad():
                        a						:Tuple 							=       model(**__lowerCamelCase						)
                  a						:str 							=       outputs.logits.argmax(dim=-1						)
                  a							,				a						:Optional[int] 							=       accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                  metric.add_batch(
                      predictions=__lowerCamelCase ,  references=__lowerCamelCase ,  )

            a						:Optional[int] 							=       metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:''' ,  __lowerCamelCase						)
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:int 							=       argparse.ArgumentParser(description='''Simple example of training script.'''						)
      parser.add_argument(
          '''--mixed_precision''' ,  type=__lowerCamelCase ,  default=__lowerCamelCase ,  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.'''						)
      a						:int 							=       parser.parse_args()
      a						:str 							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(__lowerCamelCase ,  __lowerCamelCase						)


if __name__ == "__main__":
     main()












'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_vision_model'''


    def __init__(		self ,		A=768 ,		A=12 ,		A=3 ,		A=16 ,		A=288 ,		A=1 ,		A=1e-05 ,		A=False ,		A=True ,		A=False ,		**A ,		)		->   Dict:
        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			patch_size
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			stop_gradient
        _SCREAMING_SNAKE_CASE									=			share_layernorm
        _SCREAMING_SNAKE_CASE									=			remove_last_layer





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

        if config_dict.get("""model_type"""       ) == "bridgetower":
            _SCREAMING_SNAKE_CASE									=			config_dict["""text_config"""]

        if "model_type" in config_dict and hasattr(cls ,		"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'       )

        return cls.from_dict(A ,		**A       )




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_text_model'''


    def __init__(		self ,		A=5_0265 ,		A=768 ,		A=12 ,		A=12 ,		A=1 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=514 ,		A=1 ,		A=1e-05 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		**A ,		)		->   Union[str, Any]:
        super().__init__(**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			pad_token_id
        _SCREAMING_SNAKE_CASE									=			bos_token_id
        _SCREAMING_SNAKE_CASE									=			eos_token_id





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

        if config_dict.get("""model_type"""       ) == "bridgetower":
            _SCREAMING_SNAKE_CASE									=			config_dict["""text_config"""]

        if "model_type" in config_dict and hasattr(cls ,		"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'       )

        return cls.from_dict(A ,		**A       )




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower'''


    def __init__(		self ,		A=True ,		A="gelu" ,		A=768 ,		A=1 ,		A=1e-05 ,		A=False ,		A="add" ,		A=12 ,		A=6 ,		A=False ,		A=False ,		A=None ,		A=None ,		**A ,		)		->   Tuple:
        # TODO: remove this once the Hub files are updated.
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""text_config_dict""" ,		A       )
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""vision_config_dict""" ,		A       )

        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			share_cross_modal_transformer_layers
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			share_link_tower_layers
        _SCREAMING_SNAKE_CASE									=			link_tower_type
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			tie_word_embeddings
        _SCREAMING_SNAKE_CASE									=			init_layernorm_from_vision_encoder

        if text_config is None:
            _SCREAMING_SNAKE_CASE									=			{}
            logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""       )

        if vision_config is None:
            _SCREAMING_SNAKE_CASE									=			{}
            logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""       )

        _SCREAMING_SNAKE_CASE									=			BridgeTowerTextConfig(**A       )
        _SCREAMING_SNAKE_CASE									=			BridgeTowerVisionConfig(**A       )


    @classmethod
    def 			snake_case_(		cls ,		A ,		A ,		**A       )		->   int:
        return cls(text_config=text_config.to_dict() ,		vision_config=vision_config.to_dict() ,		**A       )





    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			copy.deepcopy(self.__dict__       )
        _SCREAMING_SNAKE_CASE									=			self.text_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.vision_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.__class__.model_type
        return output



