
"""simple docstring"""




import copy
import os
from typing import Union

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


_A			:       Union[str, Any]   		=       logging.get_logger(__name__)

_A			:       List[str]   		=       {
    '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__						(      lowerCAmelCase_						):
   __lowerCAmelCase            =				"""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	,    ):
          super().__init__(**__a				)
          lowercase    :				Dict       		=							hidden_size
          lowercase    :				Any       		=							num_hidden_layers
          lowercase    :				Optional[int]       		=							num_channels
          lowercase    :				int       		=							patch_size
          lowercase    :				Any       		=							image_size
          lowercase    :				Union[str, Any]       		=							initializer_factor
          lowercase    :				Optional[int]       		=							layer_norm_eps
          lowercase    :				Tuple       		=							stop_gradient
          lowercase    :				int       		=							share_layernorm
          lowercase    :				str       		=							remove_last_layer

   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    **_a				):
          lowercase    :				Optional[int]       		=							cls.get_config_dict(__a	,    **__a				)

          if config_dict.get("model_type"				) == "bridgetower":
                 lowercase    :				str       		=							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__						(      lowerCAmelCase_						):
   __lowerCAmelCase            =				"""bridgetower_text_model"""
   def __init__(			self	,    _a=50_265	,    _a=768	,    _a=12	,    _a=12	,    _a=1	,    _a=3_072	,    _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	,    ):
          super().__init__(**__a				)

          lowercase    :				Union[str, Any]       		=							vocab_size
          lowercase    :				Any       		=							hidden_size
          lowercase    :				Optional[int]       		=							num_hidden_layers
          lowercase    :				List[Any]       		=							num_attention_heads
          lowercase    :				str       		=							hidden_act
          lowercase    :				Optional[int]       		=							initializer_factor
          lowercase    :				Optional[Any]       		=							intermediate_size
          lowercase    :				Any       		=							hidden_dropout_prob
          lowercase    :				str       		=							attention_probs_dropout_prob
          lowercase    :				Dict       		=							max_position_embeddings
          lowercase    :				int       		=							type_vocab_size
          lowercase    :				Any       		=							layer_norm_eps
          lowercase    :				Union[str, Any]       		=							position_embedding_type
          lowercase    :				Optional[int]       		=							use_cache
          lowercase    :				Dict       		=							pad_token_id
          lowercase    :				Tuple       		=							bos_token_id
          lowercase    :				int       		=							eos_token_id

   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    **_a				):
          lowercase    :				Union[str, Any]       		=							cls.get_config_dict(__a	,    **__a				)

          if config_dict.get("model_type"				) == "bridgetower":
                 lowercase    :				Union[str, Any]       		=							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__						(      lowerCAmelCase_						):
   __lowerCAmelCase            =				"""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	,    ):
          lowercase    :				Dict       		=							kwargs.pop("text_config_dict"	,    __a				)
          lowercase    :				Tuple       		=							kwargs.pop("vision_config_dict"	,    __a				)

          super().__init__(**__a				)
          lowercase    :				int       		=							share_cross_modal_transformer_layers
          lowercase    :				Any       		=							hidden_act
          lowercase    :				str       		=							hidden_size
          lowercase    :				int       		=							initializer_factor
          lowercase    :				List[str]       		=							layer_norm_eps
          lowercase    :				Optional[int]       		=							share_link_tower_layers
          lowercase    :				List[Any]       		=							link_tower_type
          lowercase    :				Union[str, Any]       		=							num_attention_heads
          lowercase    :				Optional[Any]       		=							num_hidden_layers
          lowercase    :				int       		=							tie_word_embeddings
          lowercase    :				int       		=							init_layernorm_from_vision_encoder

          if text_config is None:
                 lowercase    :				Union[str, Any]       		=							{}
                 logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."				)

          if vision_config is None:
                 lowercase    :				Optional[Any]       		=							{}
                 logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."				)

          lowercase    :				Optional[Any]       		=							BridgeTowerTextConfig(**__a				)
          lowercase    :				Optional[int]       		=							BridgeTowerVisionConfig(**__a				)
   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    _a	,    **_a				):
          return cls(text_config=text_config.to_dict()	,    vision_config=vision_config.to_dict()	,    **__a				)

   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							copy.deepcopy(self.__dict__				)
          lowercase    :				Any       		=							self.text_config.to_dict()
          lowercase    :				Union[str, Any]       		=							self.vision_config.to_dict()
          lowercase    :				Union[str, Any]       		=							self.__class__.model_type
          return output






'''simple docstring'''



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


__lowercase	:						Dict       =	16
__lowercase	:						List[Any]       =	32






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					return int(x / 2**20   )





class 							__UpperCamelCase				:




	def __enter__(   self							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						__a			:       Optional[int]					    =  torch.cuda.memory_allocated()
						return self





	def __exit__(   self						,				*__a							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						__a			:       Dict					    =  torch.cuda.memory_allocated()
						__a			:       List[Any]					    =  torch.cuda.max_memory_allocated()
						__a			:       Tuple					    =  bamb(self.end - self.begin							)
						__a			:       Tuple					    =  bamb(self.peak - self.begin							)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Accelerator						,      _SCREAMING_SNAKE_CASE      :   int = 16						,      _SCREAMING_SNAKE_CASE      :   str = "bert-base-cased"						,      _SCREAMING_SNAKE_CASE      :   int = 320						,      _SCREAMING_SNAKE_CASE      :   int = 160						,      ):
					__a			:       int					    =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       List[Any]					    =  load_dataset(
					    'glue'						,      'mrpc'						,      split={'train': F"""train[:{n_train}]""", 'validation': F"""validation[:{n_val}]"""}   )

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

					# Apply the method we just defined to all the examples in all the splits of the dataset
					__a			:       List[str]					    =  datasets.map(
					    _SCREAMING_SNAKE_CASE						,      batched=_SCREAMING_SNAKE_CASE						,      remove_columns=['idx', 'sentence1', 'sentence2']						,      load_from_cache_file=_SCREAMING_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			:       Tuple					    =  tokenized_datasets.rename_column('label'						,      'labels'   )

					def collate_fn(_SCREAMING_SNAKE_CASE      :   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(_SCREAMING_SNAKE_CASE						,      padding='max_length'						,      max_length=128						,      return_tensors='pt'   )
										return tokenizer.pad(_SCREAMING_SNAKE_CASE						,      padding='longest'						,      return_tensors='pt'   )

					# Instantiate dataloaders.
					__a			:       int					    =  DataLoader(
					    tokenized_datasets['train']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  DataLoader(
					    tokenized_datasets['validation']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )

					return train_dataloader, eval_dataloader






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int   ):
					# Initialize accelerator
					__a			:       str					    =  Accelerator()

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

					set_seed(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       int					    =  get_dataloaders(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_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			:       Optional[int]					    =  AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE						,      return_dict=_SCREAMING_SNAKE_CASE   )

					# Instantiate optimizer
					__a			:       Optional[Any]					    =  (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					__a			:       Optional[Any]					    =  optimizer_cls(params=model.parameters()						,      lr=_SCREAMING_SNAKE_CASE   )

					if accelerator.state.deepspeed_plugin is not None:
										__a			:       int					    =  accelerator.state.deepspeed_plugin.deepspeed_config[
										    'gradient_accumulation_steps'
										]
					else:
										__a			:       Union[str, Any]					    =  1
					__a			:       Tuple					    =  (len(_SCREAMING_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			:       str					    =  get_linear_schedule_with_warmup(
										    optimizer=_SCREAMING_SNAKE_CASE						,      num_warmup_steps=0						,      num_training_steps=_SCREAMING_SNAKE_CASE						,      )
					else:
										__a			:       List[Any]					    =  DummyScheduler(_SCREAMING_SNAKE_CASE						,      total_num_steps=_SCREAMING_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			:       Optional[Any]					    =  accelerator.prepare(
					    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# We need to keep track of how many total steps we have iterated over
					__a			:       Union[str, Any]					    =  0
					# We also need to keep track of the stating epoch so files are named properly
					__a			:       Dict					    =  0

					# Now we train the model
					__a			:       str					    =  {}
					for epoch in range(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
																				__a			:       List[Any]					    =  model(**_SCREAMING_SNAKE_CASE   )
																				__a			:       str					    =  outputs.loss
																				__a			:       str					    =  loss / gradient_accumulation_steps
																				accelerator.backward(_SCREAMING_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			:       List[Any]					    =  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(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							():
					__a			:       int					    =  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'   )
					parser.add_argument(
					    '--model_name_or_path'						,      type=_SCREAMING_SNAKE_CASE						,      default='bert-base-cased'						,      help='Path to pretrained model or model identifier from huggingface.co/models.'						,      required=_SCREAMING_SNAKE_CASE						,      )
					parser.add_argument(
					    '--output_dir'						,      type=_SCREAMING_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=_SCREAMING_SNAKE_CASE						,      default=_SCREAMING_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=_SCREAMING_SNAKE_CASE						,      default=320						,      help='Number of training examples to use.'						,      )
					parser.add_argument(
					    '--n_val'						,      type=_SCREAMING_SNAKE_CASE						,      default=160						,      help='Number of validation examples to use.'						,      )
					parser.add_argument(
					    '--num_epochs'						,      type=_SCREAMING_SNAKE_CASE						,      default=1						,      help='Number of train epochs.'						,      )
					__a			:       List[str]					    =  parser.parse_args()
					__a			:       List[Any]					    =  {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
					training_function(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	main()



