






"""simple docstring"""



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Any		,					__snake_case				: Dict		,					__snake_case				: Optional[Any]		,					__snake_case				: Dict	):
		# Initialise PyTorch model.
		# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
		# TapasConfig to False.

		# initialize configuration from json file
		lowercase_ : Optional[Any]							       =   TapasConfig.from_json_file(__snake_case	)
		# set absolute/relative position embeddings parameter
		lowercase_ : Any							       =   reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowercase_ : List[Any]							       =   TapasForQuestionAnswering(config=__snake_case	)
		elif task == "WTQ":
				# run_task_main.py hparams
				lowercase_ : int							       =   4
				lowercase_ : str							       =   True
				# hparam_utils.py hparams
				lowercase_ : int							       =   0.664694
				lowercase_ : Any							       =   0.207951
				lowercase_ : List[Any]							       =   0.121194
				lowercase_ : Optional[int]							       =   True
				lowercase_ : List[Any]							       =   True
				lowercase_ : Optional[Any]							       =   False
				lowercase_ : Optional[int]							       =   0.0352513

				lowercase_ : str							       =   TapasForQuestionAnswering(config=__snake_case	)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowercase_ : Tuple							       =   4
				lowercase_ : Optional[int]							       =   False
				# hparam_utils.py hparams
				lowercase_ : Dict							       =   36.4519
				lowercase_ : Tuple							       =   0.903421
				lowercase_ : int							       =   222.088
				lowercase_ : int							       =   True
				lowercase_ : List[str]							       =   True
				lowercase_ : Optional[Any]							       =   True
				lowercase_ : List[str]							       =   0.763141

				lowercase_ : Any							       =   TapasForQuestionAnswering(config=__snake_case	)
		elif task == "TABFACT":
				lowercase_ : Dict							       =   TapasForSequenceClassification(config=__snake_case	)
		elif task == "MLM":
				lowercase_ : List[Any]							       =   TapasForMaskedLM(config=__snake_case	)
		elif task == "INTERMEDIATE_PRETRAINING":
				lowercase_ : Optional[int]							       =   TapasModel(config=__snake_case	)
		else:
				raise ValueError(F'''Task {task} not supported.'''	)

		print(F'''Building PyTorch model from configuration: {config}'''	)
		# Load weights from tf checkpoint
		load_tf_weights_in_tapas(__snake_case		,					__snake_case		,					__snake_case	)

		# Save pytorch-model (weights and configuration)
		print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
		model.save_pretrained(__snake_case	)

		# Save tokenizer files
		print(F'''Save tokenizer files to {pytorch_dump_path}'''	)
		lowercase_ : List[str]							       =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + '''vocab.txt'''		,					model_max_length=5_1_2	)
		tokenizer.save_pretrained(__snake_case	)

		print('''Used relative position embeddings:'''		,					model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
						__A		:       List[str]  				=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
						)
						parser.add_argument(
						    '''--reset_position_index_per_cell''',
						    default=False,
						    action='''store_true''',
						    help='''Whether to use relative position embeddings or not. Defaults to True.''',
						)
						parser.add_argument(
						    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--tapas_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained TAPAS model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						__A		:       List[str]  				=		parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.task,
						    args.reset_position_index_per_cell,
						    args.tf_checkpoint_path,
						    args.tapas_config_file,
						    args.pytorch_dump_path,
						)











"""simple docstring"""



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						__A		:       str  				=		argparse.ArgumentParser()

						parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

						parser.add_argument(
						    '''--txt2img_unclip''',
						    default='''kakaobrain/karlo-v1-alpha''',
						    type=str,
						    required=False,
						    help='''The pretrained txt2img unclip.''',
						)

						__A		:       str  				=		parser.parse_args()

						__A		:       List[Any]  				=		UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						__A		:       Dict  				=		CLIPImageProcessor()
						__A		:       Union[str, Any]  				=		CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

						__A		:       List[str]  				=		UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)



