

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = 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.',
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()

					SCREAMING_SNAKE_CASE_            = 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 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 			a					(		UpperCAmelCase			):
						_lowercase        =     ["image_processor", "tokenizer"]
						_lowercase        =     "OwlViTImageProcessor"
						_lowercase        =     ("CLIPTokenizer", "CLIPTokenizerFast")


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







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,   A_  ,   )
														_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("feature_extractor"		)

										_UpperCAmelCase    :						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__(A_  ,   A_		)


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







										'''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(A_  ,   A_		) or (isinstance(A_  ,   A_		) and not isinstance(text[0]  ,   A_		)):
																		_UpperCAmelCase    :						Optional[int]							=      [self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)]

														elif isinstance(A_  ,   A_		) and isinstance(text[0]  ,   A_		):
																		_UpperCAmelCase    :						Optional[int]							=      []

																		# Maximum number of queries across batch
																		_UpperCAmelCase    :						Optional[Any]							=      max([len(A_		) for t in text]		)

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

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

														if return_tensors == "np":
																		_UpperCAmelCase    :						List[str]							=      np.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Tuple							=      np.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

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

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

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

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

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

																		_UpperCAmelCase    :						Union[str, Any]							=      tf.stack([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Optional[int]							=      tf.stack([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

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

														_UpperCAmelCase    :						Optional[int]							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      input_ids
														_UpperCAmelCase    :						Optional[Any]							=      attention_mask

										if query_images is not None:
														_UpperCAmelCase    :						int							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      self.image_processor(
														    A_  ,   return_tensors=A_  ,   **A_		).pixel_values
														_UpperCAmelCase    :						Optional[Any]							=      query_pixel_values

										if images is not None:
														_UpperCAmelCase    :						int							=      self.image_processor(A_  ,   return_tensors=A_  ,   **A_		)

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


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


