
from dataclasses import dataclass, field
from typing import Optional

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





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



def   __lowerCAmelCase    (				)	->     Optional[Any]:
							lowerCamelCase_					=       HfArgumentParser((ModelArguments,)							)
							((lowerCamelCase_   )	,    )					=       parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

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

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

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

							lowerCamelCase_					=       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=lowerCAmelCase__      ,					decoder_config=lowerCAmelCase__      ,					)

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

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

							lowerCamelCase_					=       AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path							)

							lowerCamelCase_					=       AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path							)
							lowerCamelCase_					=       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 unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible



class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  List[str]       )		->     Any:
											A      						=    [
											    'safety_checker/pytorch_model.bin',
											    'safety_checker/model.safetensors',
											    'vae/diffusion_pytorch_model.bin',
											    'vae/diffusion_pytorch_model.safetensors',
											    'text_encoder/pytorch_model.bin',
											    'text_encoder/model.safetensors',
											    'unet/diffusion_pytorch_model.bin',
											    'unet/diffusion_pytorch_model.safetensors',
											]
											self.assertTrue(is_safetensors_compatible(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  str       )		->     List[Any]:
											A      						=    [
											    'unet/diffusion_pytorch_model.bin',
											    'unet/diffusion_pytorch_model.safetensors',
											]
											self.assertTrue(is_safetensors_compatible(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											A      						=    [
											    'safety_checker/pytorch_model.bin',
											    'safety_checker/model.safetensors',
											    'vae/diffusion_pytorch_model.bin',
											    'vae/diffusion_pytorch_model.safetensors',
											    'text_encoder/pytorch_model.bin',
											    'text_encoder/model.safetensors',
											    'unet/diffusion_pytorch_model.bin',
											    # Removed: 'unet/diffusion_pytorch_model.safetensors',
											]
											self.assertFalse(is_safetensors_compatible(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Dict       )		->     Any:
											A      						=    [
											    'text_encoder/pytorch_model.bin',
											    'text_encoder/model.safetensors',
											]
											self.assertTrue(is_safetensors_compatible(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     List[str]:
											A      						=    [
											    'safety_checker/pytorch_model.bin',
											    'safety_checker/model.safetensors',
											    'vae/diffusion_pytorch_model.bin',
											    'vae/diffusion_pytorch_model.safetensors',
											    'text_encoder/pytorch_model.bin',
											    # Removed: 'text_encoder/model.safetensors',
											    'unet/diffusion_pytorch_model.bin',
											    'unet/diffusion_pytorch_model.safetensors',
											]
											self.assertFalse(is_safetensors_compatible(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     List[str]:
											A      						=    [
											    'safety_checker/pytorch_model.fp16.bin',
											    'safety_checker/model.fp16.safetensors',
											    'vae/diffusion_pytorch_model.fp16.bin',
											    'vae/diffusion_pytorch_model.fp16.safetensors',
											    'text_encoder/pytorch_model.fp16.bin',
											    'text_encoder/model.fp16.safetensors',
											    'unet/diffusion_pytorch_model.fp16.bin',
											    'unet/diffusion_pytorch_model.fp16.safetensors',
											]
											A      						=    'fp16'
											self.assertTrue(is_safetensors_compatible(__UpperCamelCase						, variant=__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Optional[Any]:
											A      						=    [
											    'unet/diffusion_pytorch_model.fp16.bin',
											    'unet/diffusion_pytorch_model.fp16.safetensors',
											]
											A      						=    'fp16'
											self.assertTrue(is_safetensors_compatible(__UpperCamelCase						, variant=__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  int       )		->     Optional[int]:
											# pass variant but use the non-variant filenames
											A      						=    [
											    'unet/diffusion_pytorch_model.bin',
											    'unet/diffusion_pytorch_model.safetensors',
											]
											A      						=    'fp16'
											self.assertTrue(is_safetensors_compatible(__UpperCamelCase						, variant=__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Any       )		->     List[str]:
											A      						=    [
											    'safety_checker/pytorch_model.fp16.bin',
											    'safety_checker/model.fp16.safetensors',
											    'vae/diffusion_pytorch_model.fp16.bin',
											    'vae/diffusion_pytorch_model.fp16.safetensors',
											    'text_encoder/pytorch_model.fp16.bin',
											    'text_encoder/model.fp16.safetensors',
											    'unet/diffusion_pytorch_model.fp16.bin',
											    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
											]
											A      						=    'fp16'
											self.assertFalse(is_safetensors_compatible(__UpperCamelCase						, variant=__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Optional[int]:
											A      						=    [
											    'text_encoder/pytorch_model.fp16.bin',
											    'text_encoder/model.fp16.safetensors',
											]
											A      						=    'fp16'
											self.assertTrue(is_safetensors_compatible(__UpperCamelCase						, variant=__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     int:
											# pass variant but use the non-variant filenames
											A      						=    [
											    'text_encoder/pytorch_model.bin',
											    'text_encoder/model.safetensors',
											]
											A      						=    'fp16'
											self.assertTrue(is_safetensors_compatible(__UpperCamelCase						, variant=__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Optional[Any]:
											A      						=    [
											    'safety_checker/pytorch_model.fp16.bin',
											    'safety_checker/model.fp16.safetensors',
											    'vae/diffusion_pytorch_model.fp16.bin',
											    'vae/diffusion_pytorch_model.fp16.safetensors',
											    'text_encoder/pytorch_model.fp16.bin',
											    # 'text_encoder/model.fp16.safetensors',
											    'unet/diffusion_pytorch_model.fp16.bin',
											    'unet/diffusion_pytorch_model.fp16.safetensors',
											]
											A      						=    'fp16'
											self.assertFalse(is_safetensors_compatible(__UpperCamelCase						, variant=__UpperCamelCase       )       )