




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser





@dataclass
class 	lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       =   field(
     metadata={"help": "The output directory where the model will be written."}	,       )
 lowercase_       =   field(
     metadata={
         "help": (
             "The encoder model checkpoint for weights initialization."
             "Don't set if you want to train an encoder model from scratch."
         )
     }	,       )
 lowercase_       =   field(
     metadata={
         "help": (
             "The decoder model checkpoint for weights initialization."
             "Don't set if you want to train a decoder model from scratch."
         )
     }	,       )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"} )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"} )


def  UpperCAmelCase_							(				) ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE_     						=      HfArgumentParser((ModelArguments,)							)
      ((SCREAMING_SNAKE_CASE_)						,			)     						=      parser.parse_args_into_dataclasses()

      # Load pretrained model and tokenizer

      # Use explicit specified encoder config
      if model_args.encoder_config_name:
            SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(model_args.encoder_config_name							)
      # Use pretrained encoder model's config
      else:
            SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(model_args.encoder_model_name_or_path							)

      # Use explicit specified decoder config
      if model_args.decoder_config_name:
            SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(model_args.decoder_config_name							)
      # Use pretrained decoder model's config
      else:
            SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(model_args.decoder_model_name_or_path							)

      # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
      SCREAMING_SNAKE_CASE_     						=      True
      SCREAMING_SNAKE_CASE_     						=      True

      SCREAMING_SNAKE_CASE_     						=      FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
          encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path      ,      decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path      ,      encoder_config=__UpperCAmelCase      ,      decoder_config=__UpperCAmelCase      ,      )

      # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
      SCREAMING_SNAKE_CASE_     						=      decoder_config.decoder_start_token_id
      SCREAMING_SNAKE_CASE_     						=      decoder_config.pad_token_id
      if decoder_start_token_id is None:
            SCREAMING_SNAKE_CASE_     						=      decoder_config.bos_token_id
      if pad_token_id is None:
            SCREAMING_SNAKE_CASE_     						=      decoder_config.eos_token_id

      # This is necessary to make Flax's generate() work
      SCREAMING_SNAKE_CASE_     						=      decoder_config.eos_token_id
      SCREAMING_SNAKE_CASE_     						=      decoder_start_token_id
      SCREAMING_SNAKE_CASE_     						=      pad_token_id

      SCREAMING_SNAKE_CASE_     						=      AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path							)

      SCREAMING_SNAKE_CASE_     						=      AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path							)
      SCREAMING_SNAKE_CASE_     						=      tokenizer.convert_ids_to_tokens(model.config.pad_token_id							)

      model.save_pretrained(model_args.output_dir							)
      image_processor.save_pretrained(model_args.output_dir							)
      tokenizer.save_pretrained(model_args.output_dir							)


if __name__ == "__main__":
   main()





import gc
import threading
import time

import psutil
import torch





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      psutil.Process()
       SCREAMING_SNAKE_CASE_     						=      False



 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      -1

       while True:
             SCREAMING_SNAKE_CASE_     						=      max(self.process.memory_info().rss    ,    self.cpu_memory_peak						)

             # can't sleep or will not catch the peak right (this comment is here on purpose)
             if not self.peak_monitoring:
                   break



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      True
       SCREAMING_SNAKE_CASE_     						=      threading.Thread(target=self.peak_monitor						)
       SCREAMING_SNAKE_CASE_     						=      True
       self.thread.start()




 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      False
       self.thread.join()
       return self.cpu_memory_peak


lowerCamelCase__			:       List[str]         =							PeakCPUMemory()

def  UpperCAmelCase_							(				) ->			Tuple:
      # Time
      SCREAMING_SNAKE_CASE_     						=      {'time': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      SCREAMING_SNAKE_CASE_     						=      psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()							):
            SCREAMING_SNAKE_CASE_     						=      torch.cuda.memory_allocated(__UpperCAmelCase							)
      torch.cuda.reset_peak_memory_stats()

      return measures

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			Optional[Any]:
      # Time
      SCREAMING_SNAKE_CASE_     						=      {'time': time.time() - start_measures['time']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      SCREAMING_SNAKE_CASE_     						=      (psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
      SCREAMING_SNAKE_CASE_     						=      (cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()							):
            SCREAMING_SNAKE_CASE_     						=      (torch.cuda.memory_allocated(__UpperCAmelCase							) - start_measures[str(__UpperCAmelCase							)]) / 2**20
            SCREAMING_SNAKE_CASE_     						=      (torch.cuda.max_memory_allocated(__UpperCAmelCase							) - start_measures[str(__UpperCAmelCase							)]) / 2**20

      return measures





def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     Any							) ->			Optional[Any]:
      print(f"{description}:"							)
      print(f"- Time: {measures['time']:.2f}s"							)
      for i in range(torch.cuda.device_count()							):
            print(f"- GPU {i} allocated: {measures[str(__UpperCAmelCase							)]:.2f}MiB"							)
            SCREAMING_SNAKE_CASE_     						=      measures[f"{i}-peak"]
            print(f"- GPU {i} peak: {peak:.2f}MiB"							)
      print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB"							)
      print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"							)