






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
	lowerCamelCase       :       List[Any]        =				argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
	)
	# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
	parser.add_argument(
	    '''--original_config_file''',
	    default=None,
	    type=str,
	    help='''The YAML config file corresponding to the original architecture.''',
	)
	parser.add_argument(
	    '''--num_in_channels''',
	    default=None,
	    type=int,
	    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
	)
	parser.add_argument(
	    '''--scheduler_type''',
	    default='''pndm''',
	    type=str,
	    help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
	)
	parser.add_argument(
	    '''--pipeline_type''',
	    default=None,
	    type=str,
	    help=(
	        '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
	        '''. If `None` pipeline will be automatically inferred.'''
	    ),
	)
	parser.add_argument(
	    '''--image_size''',
	    default=None,
	    type=int,
	    help=(
	        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
	        ''' Base. Use 768 for Stable Diffusion v2.'''
	    ),
	)
	parser.add_argument(
	    '''--prediction_type''',
	    default=None,
	    type=str,
	    help=(
	        '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
	        ''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
	    ),
	)
	parser.add_argument(
	    '''--extract_ema''',
	    action='''store_true''',
	    help=(
	        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
	        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
	        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
	    ),
	)
	parser.add_argument(
	    '''--upcast_attention''',
	    action='''store_true''',
	    help=(
	        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
	        ''' diffusion 2.1.'''
	    ),
	)
	parser.add_argument(
	    '''--from_safetensors''',
	    action='''store_true''',
	    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
	)
	parser.add_argument(
	    '''--to_safetensors''',
	    action='''store_true''',
	    help='''Whether to store pipeline in safetensors format or not.''',
	)
	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
	parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
	parser.add_argument(
	    '''--stable_unclip''',
	    type=str,
	    default=None,
	    required=False,
	    help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
	)
	parser.add_argument(
	    '''--stable_unclip_prior''',
	    type=str,
	    default=None,
	    required=False,
	    help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
	)
	parser.add_argument(
	    '''--clip_stats_path''',
	    type=str,
	    help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
	    required=False,
	)
	parser.add_argument(
	    '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
	)
	parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
	parser.add_argument(
	    '''--vae_path''',
	    type=str,
	    default=None,
	    required=False,
	    help='''Set to a path, hub id to an already converted vae to not convert it again.''',
	)
	lowerCamelCase       :       Any        =				parser.parse_args()

	lowerCamelCase       :       Dict        =				download_from_original_stable_diffusion_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    prediction_type=args.prediction_type,
	    model_type=args.pipeline_type,
	    extract_ema=args.extract_ema,
	    scheduler_type=args.scheduler_type,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    stable_unclip=args.stable_unclip,
	    stable_unclip_prior=args.stable_unclip_prior,
	    clip_stats_path=args.clip_stats_path,
	    controlnet=args.controlnet,
	    vae_path=args.vae_path,
	)

	if args.half:
		pipe.to(torch_dtype=torch.floataa)

	if args.controlnet:
		# only save the controlnet model
		pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
	else:
		pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







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 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  int					):
							# 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[int]			=   TapasConfig.from_json_file(lowerCAmelCase_					)
							# set absolute/relative position embeddings parameter
							__lowercase					:							Optional[Any]			=   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowercase					:							Union[str, Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowercase					:							List[Any]			=   4
														__lowercase					:							Union[str, Any]			=   True
														# hparam_utils.py hparams
														__lowercase					:							Any			=   0.664_694
														__lowercase					:							Tuple			=   0.207_951
														__lowercase					:							Dict			=   0.121_194
														__lowercase					:							List[str]			=   True
														__lowercase					:							str			=   True
														__lowercase					:							Dict			=   False
														__lowercase					:							Tuple			=   0.0_352_513

														__lowercase					:							List[Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowercase					:							Optional[int]			=   4
														__lowercase					:							int			=   False
														# hparam_utils.py hparams
														__lowercase					:							Tuple			=   36.4_519
														__lowercase					:							str			=   0.903_421
														__lowercase					:							List[Any]			=   222.088
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Tuple			=   True
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Optional[Any]			=   0.763_141

														__lowercase					:							str			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "TABFACT":
														__lowercase					:							List[Any]			=   TapasForSequenceClassification(config=lowerCAmelCase_					)
							elif task == "MLM":
														__lowercase					:							Optional[int]			=   TapasForMaskedLM(config=lowerCAmelCase_					)
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowercase					:							Dict			=   TapasModel(config=lowerCAmelCase_					)
							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(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)

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

							# Save tokenizer files
							print(F"Save tokenizer files to {pytorch_dump_path}"					)
							__lowercase					:							Any			=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""				,				model_max_length=512					)
							tokenizer.save_pretrained(lowerCAmelCase_					)

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


if __name__ == "__main__":
	lowerCamelCase       :       List[Any]        =				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.'''
	)
	lowerCamelCase       :       int        =				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,
	)