


'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional

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





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






def   lowerCamelCase							():
					__a			:       Dict					    =  HfArgumentParser((ModelArguments,)   )
					((__a)    ,				)			:       Tuple					    =  parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

					# Use explicit specified encoder config
					if model_args.encoder_config_name:
										__a			:       int					    =  AutoConfig.from_pretrained(model_args.encoder_config_name   )
					# Use pretrained encoder model's config
					else:
										__a			:       List[str]					    =  AutoConfig.from_pretrained(model_args.encoder_model_name_or_path   )

					# Use explicit specified decoder config
					if model_args.decoder_config_name:
										__a			:       Dict					    =  AutoConfig.from_pretrained(model_args.decoder_config_name   )
					# Use pretrained decoder model's config
					else:
										__a			:       Optional[Any]					    =  AutoConfig.from_pretrained(model_args.decoder_model_name_or_path   )

					# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
					__a			:       Dict					    =  True
					__a			:       List[Any]					    =  True

					__a			:       List[str]					    =  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=_SCREAMING_SNAKE_CASE						,      decoder_config=_SCREAMING_SNAKE_CASE						,      )

					# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
					__a			:       Dict					    =  decoder_config.decoder_start_token_id
					__a			:       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			:       Union[str, Any]					    =  decoder_config.eos_token_id

					# This is necessary to make Flax's generate() work
					__a			:       List[str]					    =  decoder_config.eos_token_id
					__a			:       int					    =  decoder_start_token_id
					__a			:       List[str]					    =  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			:       List[str]					    =  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 importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Any					    =  test_file.split(os.path.sep   )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
										    F"""{test_file} instead."""   )
					__a			:       Tuple					    =  components[-1]
					if not test_fn.endswith('py'   ):
										raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""   )
					if not test_fn.startswith('test_modeling_'   ):
										raise ValueError(
										    F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""   )

					__a			:       List[str]					    =  components[:-1] + [test_fn.replace('.py'						,      ''   )]
					__a			:       Optional[Any]					    =  '.'.join(_SCREAMING_SNAKE_CASE   )

					return test_module_path






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  get_module_path(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  importlib.import_module(_SCREAMING_SNAKE_CASE   )

					return test_module






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  []
					__a			:       List[str]					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										if attr.endswith('ModelTester'   ):
															tester_classes.append(getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       Any					    =  []
					__a			:       str					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										__a			:       int					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										__a			:       Optional[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      'all_model_classes'						,      []   )
										if len(_SCREAMING_SNAKE_CASE   ) > 0:
															test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes   )

					# sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Tuple					    =  test_class()
					if hasattr(_SCREAMING_SNAKE_CASE						,      'setUp'   ):
										test.setUp()

					__a			:       List[Any]					    =  None
					if hasattr(_SCREAMING_SNAKE_CASE						,      'model_tester'   ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															__a			:       List[str]					    =  test.model_tester.__class__

					return model_tester






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )

					__a			:       int					    =  []
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[Any]					    =  get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Any					    =  []
					for test_class in test_classes:
										__a			:       Any					    =  get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   )
										if tester_class is not None:
															tester_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   ) for test_class in test_classes}
					return test_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  {
					    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_test_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       str					    =  {
					    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_to_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					if isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o.__name__
					elif isinstance(_SCREAMING_SNAKE_CASE						,      (list, tuple)   ):
										return [to_json(_SCREAMING_SNAKE_CASE   ) for x in o]
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return {to_json(_SCREAMING_SNAKE_CASE   ): to_json(_SCREAMING_SNAKE_CASE   ) for k, v in o.items()}
					else:
										return o



