






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__UpperCAmelCase								=						{
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1000,
    'block_out_channels': [32, 64],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__UpperCAmelCase								=						{
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1000,
    'block_out_channels': [192, 192 * 2, 192 * 3, 192 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__UpperCAmelCase								=						{
    'sample_size': 256,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__UpperCAmelCase								=						{
    'num_train_timesteps': 40,
    'sigma_min': 0.0_0_2,
    'sigma_max': 8_0.0,
}

__UpperCAmelCase								=						{
    'num_train_timesteps': 201,
    'sigma_min': 0.0_0_2,
    'sigma_max': 8_0.0,
}

__UpperCAmelCase								=						{
    'num_train_timesteps': 151,
    'sigma_min': 0.0_0_2,
    'sigma_max': 8_0.0,
}




def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					if isinstance(__snake_case     ,       __snake_case  ):
										return v
					if v.lower() in ("yes", "true", "t", "y", "1"):
										return True
					elif v.lower() in ("no", "false", "f", "n", "0"):
										return False
					else:
										raise argparse.ArgumentTypeError('boolean value expected'  )




def      lowercase__      (     __snake_case    :							Any     ,       __snake_case    :							Dict     ,       __snake_case    :							List[Any]     ,       __snake_case    :							str     ,       __snake_case    :							Dict=False  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						checkpoint[F"{old_prefix}.in_layers.0.weight"]
					UpperCAmelCase_    :      str            =						checkpoint[F"{old_prefix}.in_layers.0.bias"]
					UpperCAmelCase_    :      Dict            =						checkpoint[F"{old_prefix}.in_layers.2.weight"]
					UpperCAmelCase_    :      Optional[Any]            =						checkpoint[F"{old_prefix}.in_layers.2.bias"]
					UpperCAmelCase_    :      int            =						checkpoint[F"{old_prefix}.emb_layers.1.weight"]
					UpperCAmelCase_    :      Any            =						checkpoint[F"{old_prefix}.emb_layers.1.bias"]
					UpperCAmelCase_    :      List[str]            =						checkpoint[F"{old_prefix}.out_layers.0.weight"]
					UpperCAmelCase_    :      Tuple            =						checkpoint[F"{old_prefix}.out_layers.0.bias"]
					UpperCAmelCase_    :      Dict            =						checkpoint[F"{old_prefix}.out_layers.3.weight"]
					UpperCAmelCase_    :      Optional[int]            =						checkpoint[F"{old_prefix}.out_layers.3.bias"]

					if has_skip:
										UpperCAmelCase_    :      Tuple            =						checkpoint[F"{old_prefix}.skip_connection.weight"]
										UpperCAmelCase_    :      List[str]            =						checkpoint[F"{old_prefix}.skip_connection.bias"]

					return new_checkpoint




def      lowercase__      (     __snake_case    :							Dict     ,       __snake_case    :							List[Any]     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							int     ,       __snake_case    :							Dict=None  ):


					'''simple docstring'''


					UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						checkpoint[F"{old_prefix}.qkv.weight"].chunk(3     ,       dim=0  )
					UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						checkpoint[F"{old_prefix}.qkv.bias"].chunk(3     ,       dim=0  )

					UpperCAmelCase_    :      Dict            =						checkpoint[F"{old_prefix}.norm.weight"]
					UpperCAmelCase_    :      int            =						checkpoint[F"{old_prefix}.norm.bias"]

					UpperCAmelCase_    :      Tuple            =						weight_q.squeeze(-1  ).squeeze(-1  )
					UpperCAmelCase_    :      str            =						bias_q.squeeze(-1  ).squeeze(-1  )
					UpperCAmelCase_    :      Any            =						weight_k.squeeze(-1  ).squeeze(-1  )
					UpperCAmelCase_    :      List[Any]            =						bias_k.squeeze(-1  ).squeeze(-1  )
					UpperCAmelCase_    :      Dict            =						weight_v.squeeze(-1  ).squeeze(-1  )
					UpperCAmelCase_    :      Optional[Any]            =						bias_v.squeeze(-1  ).squeeze(-1  )

					UpperCAmelCase_    :      Any            =						(
					    checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1  ).squeeze(-1  )
					)
					UpperCAmelCase_    :      Optional[int]            =						checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1  ).squeeze(-1  )

					return new_checkpoint




def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							Any  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						torch.load(__snake_case     ,       map_location='cpu'  )
					UpperCAmelCase_    :      int            =						{}

					UpperCAmelCase_    :      Optional[int]            =						checkpoint['time_embed.0.weight']
					UpperCAmelCase_    :      str            =						checkpoint['time_embed.0.bias']
					UpperCAmelCase_    :      str            =						checkpoint['time_embed.2.weight']
					UpperCAmelCase_    :      str            =						checkpoint['time_embed.2.bias']

					if unet_config["num_class_embeds"] is not None:
										UpperCAmelCase_    :      Any            =						checkpoint['label_emb.weight']

					UpperCAmelCase_    :      Dict            =						checkpoint['input_blocks.0.0.weight']
					UpperCAmelCase_    :      List[str]            =						checkpoint['input_blocks.0.0.bias']

					UpperCAmelCase_    :      List[Any]            =						unet_config['down_block_types']
					UpperCAmelCase_    :      Any            =						unet_config['layers_per_block']
					UpperCAmelCase_    :      Optional[Any]            =						unet_config['attention_head_dim']
					UpperCAmelCase_    :      Union[str, Any]            =						unet_config['block_out_channels']
					UpperCAmelCase_    :      List[str]            =						1
					UpperCAmelCase_    :      str            =						channels_list[0]

					for i, layer_type in enumerate(__snake_case  ):
										UpperCAmelCase_    :      List[Any]            =						channels_list[i]
										UpperCAmelCase_    :      Union[str, Any]            =						current_channels != prev_channels
										if layer_type == "ResnetDownsampleBlock2D":
															for j in range(__snake_case  ):
																				UpperCAmelCase_    :      Tuple            =						F"down_blocks.{i}.resnets.{j}"
																				UpperCAmelCase_    :      Dict            =						F"input_blocks.{current_layer}.0"
																				UpperCAmelCase_    :      Union[str, Any]            =						True if j == 0 and downsample_block_has_skip else False
																				UpperCAmelCase_    :      Optional[int]            =						convert_resnet(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       has_skip=__snake_case  )
																				current_layer += 1

										elif layer_type == "AttnDownBlock2D":
															for j in range(__snake_case  ):
																				UpperCAmelCase_    :      Optional[int]            =						F"down_blocks.{i}.resnets.{j}"
																				UpperCAmelCase_    :      str            =						F"input_blocks.{current_layer}.0"
																				UpperCAmelCase_    :      Dict            =						True if j == 0 and downsample_block_has_skip else False
																				UpperCAmelCase_    :      int            =						convert_resnet(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       has_skip=__snake_case  )
																				UpperCAmelCase_    :      Dict            =						F"down_blocks.{i}.attentions.{j}"
																				UpperCAmelCase_    :      List[Any]            =						F"input_blocks.{current_layer}.1"
																				UpperCAmelCase_    :      List[str]            =						convert_attention(
																				    __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )
																				current_layer += 1

										if i != len(__snake_case  ) - 1:
															UpperCAmelCase_    :      Dict            =						F"down_blocks.{i}.downsamplers.0"
															UpperCAmelCase_    :      Optional[Any]            =						F"input_blocks.{current_layer}.0"
															UpperCAmelCase_    :      Any            =						convert_resnet(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )
															current_layer += 1

										UpperCAmelCase_    :      Optional[int]            =						current_channels

					# hardcoded the mid-block for now
					UpperCAmelCase_    :      int            =						'mid_block.resnets.0'
					UpperCAmelCase_    :      Optional[Any]            =						'middle_block.0'
					UpperCAmelCase_    :      Union[str, Any]            =						convert_resnet(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )
					UpperCAmelCase_    :      Optional[Any]            =						'mid_block.attentions.0'
					UpperCAmelCase_    :      Union[str, Any]            =						'middle_block.1'
					UpperCAmelCase_    :      List[Any]            =						convert_attention(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )
					UpperCAmelCase_    :      int            =						'mid_block.resnets.1'
					UpperCAmelCase_    :      Dict            =						'middle_block.2'
					UpperCAmelCase_    :      str            =						convert_resnet(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )

					UpperCAmelCase_    :      int            =						0
					UpperCAmelCase_    :      Union[str, Any]            =						unet_config['up_block_types']

					for i, layer_type in enumerate(__snake_case  ):
										if layer_type == "ResnetUpsampleBlock2D":
															for j in range(layers_per_block + 1  ):
																				UpperCAmelCase_    :      Dict            =						F"up_blocks.{i}.resnets.{j}"
																				UpperCAmelCase_    :      Dict            =						F"output_blocks.{current_layer}.0"
																				UpperCAmelCase_    :      Dict            =						convert_resnet(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       has_skip=__snake_case  )
																				current_layer += 1

															if i != len(__snake_case  ) - 1:
																				UpperCAmelCase_    :      Dict            =						F"up_blocks.{i}.upsamplers.0"
																				UpperCAmelCase_    :      Optional[Any]            =						F"output_blocks.{current_layer-1}.1"
																				UpperCAmelCase_    :      int            =						convert_resnet(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )
										elif layer_type == "AttnUpBlock2D":
															for j in range(layers_per_block + 1  ):
																				UpperCAmelCase_    :      Optional[int]            =						F"up_blocks.{i}.resnets.{j}"
																				UpperCAmelCase_    :      List[Any]            =						F"output_blocks.{current_layer}.0"
																				UpperCAmelCase_    :      int            =						convert_resnet(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       has_skip=__snake_case  )
																				UpperCAmelCase_    :      List[str]            =						F"up_blocks.{i}.attentions.{j}"
																				UpperCAmelCase_    :      Optional[Any]            =						F"output_blocks.{current_layer}.1"
																				UpperCAmelCase_    :      Optional[Any]            =						convert_attention(
																				    __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )
																				current_layer += 1

															if i != len(__snake_case  ) - 1:
																				UpperCAmelCase_    :      List[str]            =						F"up_blocks.{i}.upsamplers.0"
																				UpperCAmelCase_    :      List[str]            =						F"output_blocks.{current_layer-1}.2"
																				UpperCAmelCase_    :      str            =						convert_resnet(__snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )

					UpperCAmelCase_    :      Optional[Any]            =						checkpoint['out.0.weight']
					UpperCAmelCase_    :      str            =						checkpoint['out.0.bias']
					UpperCAmelCase_    :      List[str]            =						checkpoint['out.2.weight']
					UpperCAmelCase_    :      str            =						checkpoint['out.2.bias']

					return new_checkpoint


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

		parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
		parser.add_argument(
		    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
		)
		parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

		__UpperCAmelCase								=						parser.parse_args()
		__UpperCAmelCase								=						strabool(args.class_cond)

		__UpperCAmelCase								=						os.path.basename(args.unet_path)
		print(F'Checkpoint: {ckpt_name}')

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				__UpperCAmelCase								=						IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__UpperCAmelCase								=						LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				__UpperCAmelCase								=						TEST_UNET_CONFIG
		else:
				raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

		if not args.class_cond:
				__UpperCAmelCase								=						None

		__UpperCAmelCase								=						con_pt_to_diffuser(args.unet_path, unet_config)

		__UpperCAmelCase								=						UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				__UpperCAmelCase								=						CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__UpperCAmelCase								=						CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__UpperCAmelCase								=						CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

		__UpperCAmelCase								=						CMStochasticIterativeScheduler(**scheduler_config)

		__UpperCAmelCase								=						ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)














from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

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

__UpperCAmelCase								=						logging.get_logger(__name__)



@add_end_docstrings(_snake_case			)
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							int:
							super().__init__(*_UpperCamelCase				,		**_UpperCamelCase   )
							self.check_model_type(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						{}, {}
							if padding is not None:
												UpperCAmelCase_    :      List[str]            =						padding
							if truncation is not None:
												UpperCAmelCase_    :      Tuple            =						truncation
							if top_k is not None:
												UpperCAmelCase_    :      Dict            =						top_k
							return preprocess_params, {}, postprocess_params



		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							int:
							if isinstance(_UpperCamelCase				,		(Image.Image, str)   ) and isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Optional[Any]            =						{'image': image, 'question': question}
							else:
												UpperCAmelCase_    :      List[str]            =						image
							UpperCAmelCase_    :      Optional[Any]            =						super().__call__(_UpperCamelCase				,		**_UpperCamelCase   )
							return results



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=False				,		_UpperCamelCase=False   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[Any]            =						load_image(inputs['image']   )
							UpperCAmelCase_    :      Dict            =						self.tokenizer(
							    inputs['question']				,		return_tensors=self.framework				,		padding=_UpperCamelCase				,		truncation=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.image_processor(images=_UpperCamelCase				,		return_tensors=self.framework   )
							model_inputs.update(_UpperCamelCase   )
							return model_inputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[int]:
							UpperCAmelCase_    :      Any            =						self.model(**_UpperCamelCase   )
							return model_outputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=5   )	->							str:
							if top_k > self.model.config.num_labels:
												UpperCAmelCase_    :      Union[str, Any]            =						self.model.config.num_labels

							if self.framework == "pt":
												UpperCAmelCase_    :      List[str]            =						model_outputs.logits.sigmoid()[0]
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						probs.topk(_UpperCamelCase   )
							else:
												raise ValueError(f"Unsupported framework: {self.framework}"   )

							UpperCAmelCase_    :      Optional[Any]            =						scores.tolist()
							UpperCAmelCase_    :      Tuple            =						ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase				,		_UpperCamelCase   )]






