


import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Dict										=       OmegaConf.load(__lowerCamelCase     )
							__snake_case		:      Tuple										=       torch.load(__lowerCamelCase							,     map_location="cpu"     )["model"]
							__snake_case		:      List[Any]										=       list(state_dict.keys()     )

							# extract state_dict for VQVAE
							__snake_case		:      List[Any]										=       {}
							__snake_case		:      int										=       "first_stage_model."
							for key in keys:
														if key.startswith(__lowerCamelCase     ):
																					__snake_case		:      Dict										=       state_dict[key]

    # extract state_dict for UNetLDM
							__snake_case		:      Tuple										=       {}
							__snake_case		:      List[str]										=       "model.diffusion_model."
							for key in keys:
														if key.startswith(__lowerCamelCase     ):
																					__snake_case		:      Any										=       state_dict[key]

							__snake_case		:      Optional[Any]										=       config.model.params.first_stage_config.params
							__snake_case		:      Optional[Any]										=       config.model.params.unet_config.params

							__snake_case		:      Optional[int]										=       VQModel(**__lowerCamelCase     ).eval()
							vqvae.load_state_dict(__lowerCamelCase     )

							__snake_case		:      List[str]										=       UNetLDMModel(**__lowerCamelCase     ).eval()
							unet.load_state_dict(__lowerCamelCase     )

							__snake_case		:      Union[str, Any]										=       DDIMScheduler(
							    timesteps=config.model.params.timesteps							,     beta_schedule="scaled_linear"							,     beta_start=config.model.params.linear_start							,     beta_end=config.model.params.linear_end							,     clip_sample=__lowerCamelCase							,     )

							__snake_case		:      List[Any]										=       LDMPipeline(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							pipeline.save_pretrained(__lowerCamelCase     )


if __name__ == "__main__":
			_snake_case					:		str      =    argparse.ArgumentParser()
			parser.add_argument("--checkpoint_path", type=str, required=True)
			parser.add_argument("--config_path", type=str, required=True)
			parser.add_argument("--output_path", type=str, required=True)
			_snake_case					:		Optional[Any]      =    parser.parse_args()

			convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

_snake_case					:		Optional[int]      =    logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase				)
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  Tuple    )     ->   int:
														super().__init__(*lowerCamelCase   ,     **lowerCamelCase    )
														requires_backends(self   ,     "vision"    )
														self.check_model_type(
														    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
														    if self.framework == "tf"
														    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING    )

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[Any]=None    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       {}
														if top_k is not None:
																					__snake_case		:      List[Any]										=       top_k
														return {}, {}, postprocess_params

							def __call__(							self	:  List[Any]   ,     lowerCamelCase	:  Union[str, List[str], "Image.Image", List["Image.Image"]]   ,     **lowerCamelCase	:  Dict    )     ->   Optional[int]:
														return super().__call__(lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  List[Any]    )     ->   int:
														__snake_case		:      Any										=       load_image(lowerCamelCase    )
														__snake_case		:      str										=       self.image_processor(images=lowerCamelCase   ,     return_tensors=self.framework    )
														return model_inputs

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  List[str]    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.model(**lowerCamelCase    )
														return model_outputs






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]=5    )     ->   List[str]:
														if top_k > self.model.config.num_labels:
																					__snake_case		:      int										=       self.model.config.num_labels

														if self.framework == "pt":
																					__snake_case		:      Optional[Any]										=       model_outputs.logits.softmax(-1    )[0]
																					__snake_case			,      __snake_case		:      List[str]										=       probs.topk(lowerCamelCase    )
														elif self.framework == "tf":
																					__snake_case		:      Tuple										=       stable_softmax(model_outputs.logits   ,     axis=-1    )[0]
																					__snake_case		:      Optional[Any]										=       tf.math.top_k(lowerCamelCase   ,     k=lowerCamelCase    )
																					__snake_case			,      __snake_case		:      List[Any]										=       topk.values.numpy(), topk.indices.numpy()
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'    )

														__snake_case		:      Any										=       scores.tolist()
														__snake_case		:      Optional[int]										=       ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase   ,     lowerCamelCase    )]

