

"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional

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




@dataclass
class  _lowerCAmelCase  :







 """simple docstring"""







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







def   _A  (  )     ->						List[Any]:
   '''simple docstring'''


   __lowercase								=						HfArgumentParser((ModelArguments,))
   (__lowercase							)								=						parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

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

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

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

   __lowercase								=						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=snake_case_,      decoder_config=snake_case_,      )

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

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

   __lowercase								=						AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path)

   __lowercase								=						AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
   __lowercase								=						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()







"""simple docstring"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_a							=							5_00_00
_a							=							50_00

_a				,						_a							=							os.path.split(__file__)
_a							=							os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[str])     ->						List[str]:
   '''simple docstring'''


   for i in range(UpperCamelCase_):
      __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	int)     ->						Dict:
   '''simple docstring'''


   for i in range(0,      len(UpperCamelCase_),      UpperCamelCase_):
      __lowercase								=						dataset[i : i + batch_size]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[int])     ->						List[str]:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(UpperCamelCase_):
         __lowercase								=						dataset[i]




@get_duration
def   _A  (  UpperCamelCase_					:	datasets.Dataset,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


   with dataset.formatted_as(type=UpperCamelCase_):
      for i in range(0,      UpperCamelCase_,      UpperCamelCase_):
         __lowercase								=						dataset[i : i + batch_size]




def   _A  (  )     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						{"num examples": SPEED_TEST_N_EXAMPLES}
   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
       (read_formatted, {"type": "torch", "length": SMALL_TEST}),
       (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]

   __lowercase								=						[
       (read, {"length": SMALL_TEST}),
       (read, {"length": SPEED_TEST_N_EXAMPLES}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
       (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
       (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
       (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print("generating dataset")
      __lowercase								=						datasets.Features(
          {"list": datasets.Sequence(datasets.Value("float32")), "numbers": datasets.Value("float32")})
      __lowercase								=						generate_example_dataset(
          os.path.join(UpperCamelCase_,      "dataset.arrow"),      UpperCamelCase_,      num_examples=UpperCamelCase_,      seq_shapes={"list": (100,)},      )
      print("first set of iterations")
      for func, kwargs in functions:
         print(func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(UpperCamelCase_,      **UpperCamelCase_)

      print("shuffling dataset")
      __lowercase								=						dataset.shuffle()
      print("Second set of iterations (after shuffling")
      for func, kwargs in functions_shuffled:
         print("shuffled ",      func.__name__,      str(UpperCamelCase_))
         __lowercase								=						func(
             UpperCamelCase_,      **UpperCamelCase_)

   with open(UpperCamelCase_,      "wb") as f:
      f.write(json.dumps(UpperCamelCase_).encode("utf-8"))


if __name__ == "__main__":  # useful to run the profiler
   benchmark_iterating()




