



"""simple docstring"""

import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					["image_processor", "tokenizer"]
						_lowerCAmelCase							=					"OwlViTImageProcessor"
						_lowerCAmelCase							=					("CLIPTokenizer", "CLIPTokenizerFast")



						def __init__(self						,     _lowercase=None						,     _lowercase=None						,     **_lowercase							):





													'''simple docstring'''

													__a		:		Dict									=       None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																				    """ instead."""						,     _lowercase						,     )
																				__a		:		str									=       kwargs.pop("""feature_extractor"""							)

													__a		:		Union[str, Any]									=       image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																				raise ValueError("""You need to specify an `image_processor`."""							)
													if tokenizer is None:
																				raise ValueError("""You need to specify a `tokenizer`."""							)

													super().__init__(_lowercase						,     _lowercase							)



						def __call__(self						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase="max_length"						,     _lowercase="np"						,     **_lowercase							):





													'''simple docstring'''

													if text is None and query_images is None and images is None:
																				raise ValueError(
																				    """You have to specify at least one text or query image or image. All three cannot be none."""							)

													if text is not None:
																				if isinstance(_lowercase						,     _lowercase							) or (isinstance(_lowercase						,     _lowercase							) and not isinstance(text[0]						,     _lowercase							)):
																											__a		:		Any									=       [self.tokenizer(_lowercase						,     padding=_lowercase						,     return_tensors=_lowercase						,     **_lowercase							)]

																				elif isinstance(_lowercase						,     _lowercase							) and isinstance(text[0]						,     _lowercase							):
																											__a		:		Optional[Any]									=       []

																											# Maximum number of queries across batch
																											__a		:		str									=       max([len(_lowercase							) for t in text]							)

																											# Pad all batch samples to max number of text queries
																											for t in text:
																																		if len(_lowercase							) != max_num_queries:
																																									__a		:		int									=       t + [""" """] * (max_num_queries - len(_lowercase							))

																																		__a		:		Dict									=       self.tokenizer(_lowercase						,     padding=_lowercase						,     return_tensors=_lowercase						,     **_lowercase							)
																																		encodings.append(_lowercase							)
																				else:
																											raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""							)

																				if return_tensors == "np":
																											__a		:		Union[str, Any]									=       np.concatenate([encoding["""input_ids"""] for encoding in encodings]						,     axis=0							)
																											__a		:		Union[str, Any]									=       np.concatenate([encoding["""attention_mask"""] for encoding in encodings]						,     axis=0							)

																				elif return_tensors == "jax" and is_flax_available():
																											import jax.numpy as jnp

																											__a		:		Union[str, Any]									=       jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]						,     axis=0							)
																											__a		:		Optional[int]									=       jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]						,     axis=0							)

																				elif return_tensors == "pt" and is_torch_available():
																											import torch

																											__a		:		List[str]									=       torch.cat([encoding["""input_ids"""] for encoding in encodings]						,     dim=0							)
																											__a		:		int									=       torch.cat([encoding["""attention_mask"""] for encoding in encodings]						,     dim=0							)

																				elif return_tensors == "tf" and is_tf_available():
																											import tensorflow as tf

																											__a		:		List[str]									=       tf.stack([encoding["""input_ids"""] for encoding in encodings]						,     axis=0							)
																											__a		:		Dict									=       tf.stack([encoding["""attention_mask"""] for encoding in encodings]						,     axis=0							)

																				else:
																											raise ValueError("""Target return tensor type could not be returned"""							)

																				__a		:		List[str]									=       BatchEncoding()
																				__a		:		Union[str, Any]									=       input_ids
																				__a		:		str									=       attention_mask

													if query_images is not None:
																				__a		:		int									=       BatchEncoding()
																				__a		:		str									=       self.image_processor(
																				    _lowercase						,     return_tensors=_lowercase						,     **_lowercase							).pixel_values
																				__a		:		Union[str, Any]									=       query_pixel_values

													if images is not None:
																				__a		:		List[Any]									=       self.image_processor(_lowercase						,     return_tensors=_lowercase						,     **_lowercase							)

													if text is not None and images is not None:
																				__a		:		List[Any]									=       image_features.pixel_values
																				return encoding
													elif query_images is not None and images is not None:
																				__a		:		int									=       image_features.pixel_values
																				return encoding
													elif text is not None or query_images is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**_lowercase							)						,     tensor_type=_lowercase							)



						def     lowerCAmelCase__(self						,     *_lowercase						,     **_lowercase							):





													'''simple docstring'''

													return self.image_processor.post_process(*_lowercase						,     **_lowercase							)



						def     lowerCAmelCase__(self						,     *_lowercase						,     **_lowercase							):





													'''simple docstring'''

													return self.image_processor.post_process_object_detection(*_lowercase						,     **_lowercase							)



						def     lowerCAmelCase__(self						,     *_lowercase						,     **_lowercase							):





													'''simple docstring'''

													return self.image_processor.post_process_image_guided_detection(*_lowercase						,     **_lowercase							)



						def     lowerCAmelCase__(self						,     *_lowercase						,     **_lowercase							):





													'''simple docstring'''

													return self.tokenizer.batch_decode(*_lowercase						,     **_lowercase							)



						def     lowerCAmelCase__(self						,     *_lowercase						,     **_lowercase							):





													'''simple docstring'''

													return self.tokenizer.decode(*_lowercase						,     **_lowercase							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													warnings.warn(
													    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,     _lowercase						,     )
													return self.image_processor_class





						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													warnings.warn(
													    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						,     _lowercase						,     )
													return self.image_processor






"""simple docstring"""

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	lowercase__			   =  argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)
	parser.add_argument(
	    "--original_config_file",
	    type=str,
	    required=True,
	    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(
	    "--image_size",
	    default=512,
	    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(
	    "--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.)")





	def       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(F'''could not parse string as bool {string}'''					)

	parser.add_argument(
	    "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
	)

	parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

	lowercase__			   =  parser.parse_args()

	lowercase__			   =  download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

