


from dataclasses import dataclass, field
from typing import Optional

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


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



def  _a			(						):
     a_    :     Union[str, Any]										=    HfArgumentParser((ModelArguments,) )
     (a_			)    :     str										=    parser.parse_args_into_dataclasses()

     # Load pretrained model and tokenizer

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

     # Use explicit specified decoder config
     if model_args.decoder_config_name:
          a_    :     List[str]										=    AutoConfig.from_pretrained(model_args.decoder_config_name )
     # Use pretrained decoder model's config
     else:
          a_    :     int										=    AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )

     # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
     a_    :     Union[str, Any]										=    True
     a_    :     Any										=    True

     a_    :     Tuple										=    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
     a_    :     Optional[int]										=    decoder_config.decoder_start_token_id
     a_    :     Optional[Any]										=    decoder_config.pad_token_id
     if decoder_start_token_id is None:
          a_    :     List[Any]										=    decoder_config.bos_token_id
     if pad_token_id is None:
          a_    :     List[str]										=    decoder_config.eos_token_id

     # This is necessary to make Flax's generate() work
     a_    :     Dict										=    decoder_config.eos_token_id
     a_    :     List[Any]										=    decoder_start_token_id
     a_    :     Tuple										=    pad_token_id

     a_    :     Tuple										=    AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )

     a_    :     str										=    AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
     a_    :     Union[str, Any]										=    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 dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowerCamelCase        =     logging.get_logger(__name__)



def  _a			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


@dataclass
class   a__	:
       lowerCamelCase__:     List[str]							    =     list_field(
           default=[]						,       metadata={
               """help""": (
                   """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                   """ of all available models"""
               )
           }						,       )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8]						,       metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}    )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8, 32, 128, 512]						,       metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}						,       )

       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Use FP16 to accelerate inference."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Benchmark training of model"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Verbose memory tracing"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
           }						,       )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Trace memory line by line"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save result to a CSV file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save all print statements in a log file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Whether to print environment information"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                   """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                   """ for debugging / testing and on TPU."""
               )
           }						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""env_info_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving environment information."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""log_{round(time()    )}.csv"""						,       metadata={"""help""": """Log filename used if print statements are saved in log."""}						,       )
       lowerCamelCase__:     int							    =     field(default=3						,       metadata={"""help""": """Times an experiment will be run."""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                   """ model weights."""
               )
           }						,       )

       def 						UpperCAmelCase(   self      :     Any    ):
            warnings.warn(
                F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
                """ are deprecated in general and it is advised to use external Benchmarking libraries """
                """ to benchmark Transformer models."""      ,							lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return json.dumps(dataclasses.asdict(self    )      ,							indent=2    )

       @property
       def 						UpperCAmelCase(   self      :     List[str]    ):
            if len(self.models    ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""    )
            return self.models

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            if not self.multi_process:
                 return False
            elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""    )
                 return False
            else:
                 return True



