





'''simple docstring'''

from copy import deepcopy
from typing import Optional, Union

import numpy as np

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


if is_torch_available():
  import torch

if is_tf_available():
  import tensorflow as tf






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				["""image_processor"""]
 __lowercase   		=				"""SamImageProcessor"""
 def __init__(				self      :Dict						,       lowercase_      :Union[str, Any]							)->							Tuple:
     super().__init__(lowercase_							)
     A__				=			self.image_processor
     A__				=			-10
     A__				=			self.image_processor.size["longest_edge"]
 def __call__(				self      :List[Any]						,       lowercase_      :Tuple=None						,       lowercase_      :Union[str, Any]=None						,       lowercase_      :int=None						,       lowercase_      :Optional[Any]=None						,       lowercase_      :Optional[Union[str, TensorType]] = None						,       **lowercase_      :List[Any]						,       )->							BatchEncoding:
     A__				=			self.image_processor(
         lowercase_						,       return_tensors=lowercase_						,       **lowercase_						,       )

     # pop arguments that are not used in the foward but used nevertheless
     A__				=			encoding_image_processor["original_sizes"]

     if hasattr(lowercase_						,       "numpy"							):  # Checks if Torch or TF tensor
         A__				=			original_sizes.numpy()

     A__,	A__,	A__				=			self._check_and_preprocess_points(
         input_points=lowercase_						,       input_labels=lowercase_						,       input_boxes=lowercase_						,       )

     A__				=			self._normalize_and_convert(
         lowercase_						,       lowercase_						,       input_points=lowercase_						,       input_labels=lowercase_						,       input_boxes=lowercase_						,       return_tensors=lowercase_						,       )

     return encoding_image_processor
 def      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :List[Any]						,       lowercase_      :Optional[Any]						,       lowercase_      :List[str]=None						,       lowercase_      :int=None						,       lowercase_      :Dict=None						,       lowercase_      :Optional[int]="pt"						,       )->							List[Any]:
     if input_points is not None:
         if len(lowercase_							) != len(lowercase_							):
             A__				=			[
                 self._normalize_coordinates(self.target_size						,       lowercase_						,       original_sizes[0]							) for point in input_points
             ]
         else:
             A__				=			[
                 self._normalize_coordinates(self.target_size						,       lowercase_						,       lowercase_							)
                 for point, original_size in zip(lowercase_						,       lowercase_							)
             ]
         # check that all arrays have the same shape
         if not all(point.shape == input_points[0].shape for point in input_points							):
             if input_labels is not None:
                 A__,	A__				=			self._pad_points_and_labels(lowercase_						,       lowercase_							)

         A__				=			np.array(lowercase_							)

     if input_labels is not None:
         A__				=			np.array(lowercase_							)

     if input_boxes is not None:
         if len(lowercase_							) != len(lowercase_							):
             A__				=			[
                 self._normalize_coordinates(self.target_size						,       lowercase_						,       original_sizes[0]						,       is_bounding_box=lowercase_							)
                 for box in input_boxes
             ]
         else:
             A__				=			[
                 self._normalize_coordinates(self.target_size						,       lowercase_						,       lowercase_						,       is_bounding_box=lowercase_							)
                 for box, original_size in zip(lowercase_						,       lowercase_							)
             ]
         A__				=			np.array(lowercase_							)

     if input_boxes is not None:
         if return_tensors == "pt":
             A__				=			torch.from_numpy(lowercase_							)
             # boxes batch size of 1 by default
             A__				=			input_boxes.unsqueeze(1							) if len(input_boxes.shape							) != 3 else input_boxes
         elif return_tensors == "tf":
             A__				=			tf.convert_to_tensor(lowercase_							)
             # boxes batch size of 1 by default
             A__				=			tf.expand_dims(lowercase_						,       1							) if len(input_boxes.shape							) != 3 else input_boxes
         encoding_image_processor.update({"input_boxes": input_boxes}							)
     if input_points is not None:
         if return_tensors == "pt":
             A__				=			torch.from_numpy(lowercase_							)
             # point batch size of 1 by default
             A__				=			input_points.unsqueeze(1							) if len(input_points.shape							) != 4 else input_points
         elif return_tensors == "tf":
             A__				=			tf.convert_to_tensor(lowercase_							)
             # point batch size of 1 by default
             A__				=			tf.expand_dims(lowercase_						,       1							) if len(input_points.shape							) != 4 else input_points
         encoding_image_processor.update({"input_points": input_points}							)
     if input_labels is not None:
         if return_tensors == "pt":
             A__				=			torch.from_numpy(lowercase_							)
             # point batch size of 1 by default
             A__				=			input_labels.unsqueeze(1							) if len(input_labels.shape							) != 3 else input_labels
         elif return_tensors == "tf":
             A__				=			tf.convert_to_tensor(lowercase_							)
             # point batch size of 1 by default
             A__				=			tf.expand_dims(lowercase_						,       1							) if len(input_labels.shape							) != 3 else input_labels
         encoding_image_processor.update({"input_labels": input_labels}							)

     return encoding_image_processor
 def      UpperCAmelCase_							(				self      :Optional[Any]						,       lowercase_      :Optional[Any]						,       lowercase_      :List[Any]							)->							Optional[Any]:
     A__				=			max([point.shape[0] for point in input_points]							)
     A__				=			[]
     for i, point in enumerate(lowercase_							):
         if point.shape[0] != expected_nb_points:
             A__				=			np.concatenate(
                 [point, np.zeros((expected_nb_points - point.shape[0], 2)							) + self.point_pad_value]						,       axis=0							)
             A__				=			np.append(input_labels[i]						,       [self.point_pad_value]							)
         processed_input_points.append(lowercase_							)
     A__				=			processed_input_points
     return input_points, input_labels
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :int						,       lowercase_      :np.ndarray						,       lowercase_      :str						,       lowercase_      :List[str]=False							)->							np.ndarray:
     A__,	A__				=			original_size
     A__,	A__				=			self.image_processor._get_preprocess_shape(lowercase_						,       longest_edge=lowercase_							)
     A__				=			deepcopy(lowercase_							).astype(lowercase_							)

     if is_bounding_box:
         A__				=			coords.reshape(-1						,       2						,       2							)

     A__				=			coords[..., 0] * (new_w / old_w)
     A__				=			coords[..., 1] * (new_h / old_h)

     if is_bounding_box:
         A__				=			coords.reshape(-1						,       4							)

     return coords
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :int=None						,       lowercase_      :List[Any]=None						,       lowercase_      :str=None						,       )->							Dict:
     if input_points is not None:
         if hasattr(lowercase_						,       "numpy"							):  # Checks for TF or Torch tensor
             A__				=			input_points.numpy().tolist()

         if not isinstance(lowercase_						,       lowercase_							) or not isinstance(input_points[0]						,       lowercase_							):
             raise ValueError("Input points must be a list of list of floating points."							)
         A__				=			[np.array(lowercase_							) for input_point in input_points]
     else:
         A__				=			None

     if input_labels is not None:
         if hasattr(lowercase_						,       "numpy"							):
             A__				=			input_labels.numpy().tolist()

         if not isinstance(lowercase_						,       lowercase_							) or not isinstance(input_labels[0]						,       lowercase_							):
             raise ValueError("Input labels must be a list of list integers."							)
         A__				=			[np.array(lowercase_							) for label in input_labels]
     else:
         A__				=			None

     if input_boxes is not None:
         if hasattr(lowercase_						,       "numpy"							):
             A__				=			input_boxes.numpy().tolist()

         if (
             not isinstance(lowercase_						,       lowercase_							)
             or not isinstance(input_boxes[0]						,       lowercase_							)
             or not isinstance(input_boxes[0][0]						,       lowercase_							)
         ):
             raise ValueError("Input boxes must be a list of list of list of floating points."							)
         A__				=			[np.array(lowercase_							).astype(np.floataa							) for box in input_boxes]
     else:
         A__				=			None

     return input_points, input_labels, input_boxes
 @property
 def      UpperCAmelCase_							(				self      :str							)->							str:
     A__				=			self.image_processor.model_input_names
     return list(dict.fromkeys(lowercase_							)							)



 def      UpperCAmelCase_							(				self      :str						,       *lowercase_      :str						,       **lowercase_      :int							)->							Tuple:
     return self.image_processor.post_process_masks(*lowercase_						,       **lowercase_							)










'''simple docstring'''

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase							:	List[str]			={
    "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",
}

__lowerCAmelCase							:	Dict			={
    "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",
}

__lowerCAmelCase							:	Union[str, Any]			={
    "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",
}

__lowerCAmelCase							:	str			={
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Tuple			={
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__lowerCAmelCase							:	Dict			={
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}





def 						UpperCamelCase				(							_lowerCamelCase					:					Tuple       ):
    if isinstance(_lowerCamelCase						,       _lowerCamelCase       ):
        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 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Optional[int]=False       ):
    A__				=			checkpoint[F"{old_prefix}.in_layers.0.weight"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.0.bias"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.2.weight"]
    A__				=			checkpoint[F"{old_prefix}.in_layers.2.bias"]
    A__				=			checkpoint[F"{old_prefix}.emb_layers.1.weight"]
    A__				=			checkpoint[F"{old_prefix}.emb_layers.1.bias"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.0.weight"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.0.bias"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.3.weight"]
    A__				=			checkpoint[F"{old_prefix}.out_layers.3.bias"]

    if has_skip:
        A__				=			checkpoint[F"{old_prefix}.skip_connection.weight"]
        A__				=			checkpoint[F"{old_prefix}.skip_connection.bias"]

    return new_checkpoint





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					List[Any]=None       ):
    A__,	A__,	A__				=			checkpoint[F"{old_prefix}.qkv.weight"].chunk(3						,       dim=0       )
    A__,	A__,	A__				=			checkpoint[F"{old_prefix}.qkv.bias"].chunk(3						,       dim=0       )

    A__				=			checkpoint[F"{old_prefix}.norm.weight"]
    A__				=			checkpoint[F"{old_prefix}.norm.bias"]

    A__				=			weight_q.squeeze(-1       ).squeeze(-1       )
    A__				=			bias_q.squeeze(-1       ).squeeze(-1       )
    A__				=			weight_k.squeeze(-1       ).squeeze(-1       )
    A__				=			bias_k.squeeze(-1       ).squeeze(-1       )
    A__				=			weight_v.squeeze(-1       ).squeeze(-1       )
    A__				=			bias_v.squeeze(-1       ).squeeze(-1       )

    A__				=			(
        checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1       ).squeeze(-1       )
    )
    A__				=			checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1       ).squeeze(-1       )

    return new_checkpoint





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					List[str]       ):
    A__				=			torch.load(_lowerCamelCase						,       map_location="cpu"       )
    A__				=			{}

    A__				=			checkpoint["time_embed.0.weight"]
    A__				=			checkpoint["time_embed.0.bias"]
    A__				=			checkpoint["time_embed.2.weight"]
    A__				=			checkpoint["time_embed.2.bias"]

    if unet_config["num_class_embeds"] is not None:
        A__				=			checkpoint["label_emb.weight"]

    A__				=			checkpoint["input_blocks.0.0.weight"]
    A__				=			checkpoint["input_blocks.0.0.bias"]

    A__				=			unet_config["down_block_types"]
    A__				=			unet_config["layers_per_block"]
    A__				=			unet_config["attention_head_dim"]
    A__				=			unet_config["block_out_channels"]
    A__				=			1
    A__				=			channels_list[0]

    for i, layer_type in enumerate(_lowerCamelCase       ):
        A__				=			channels_list[i]
        A__				=			current_channels != prev_channels
        if layer_type == "ResnetDownsampleBlock2D":
            for j in range(_lowerCamelCase       ):
                A__				=			F"down_blocks.{i}.resnets.{j}"
                A__				=			F"input_blocks.{current_layer}.0"
                A__				=			True if j == 0 and downsample_block_has_skip else False
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                current_layer += 1

        elif layer_type == "AttnDownBlock2D":
            for j in range(_lowerCamelCase       ):
                A__				=			F"down_blocks.{i}.resnets.{j}"
                A__				=			F"input_blocks.{current_layer}.0"
                A__				=			True if j == 0 and downsample_block_has_skip else False
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                A__				=			F"down_blocks.{i}.attentions.{j}"
                A__				=			F"input_blocks.{current_layer}.1"
                A__				=			convert_attention(
                    _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
                current_layer += 1

        if i != len(_lowerCamelCase       ) - 1:
            A__				=			F"down_blocks.{i}.downsamplers.0"
            A__				=			F"input_blocks.{current_layer}.0"
            A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
            current_layer += 1

        A__				=			current_channels

    # hardcoded the mid-block for now
    A__				=			"mid_block.resnets.0"
    A__				=			"middle_block.0"
    A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
    A__				=			"mid_block.attentions.0"
    A__				=			"middle_block.1"
    A__				=			convert_attention(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
    A__				=			"mid_block.resnets.1"
    A__				=			"middle_block.2"
    A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

    A__				=			0
    A__				=			unet_config["up_block_types"]

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

            if i != len(_lowerCamelCase       ) - 1:
                A__				=			F"up_blocks.{i}.upsamplers.0"
                A__				=			F"output_blocks.{current_layer-1}.1"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
        elif layer_type == "AttnUpBlock2D":
            for j in range(layers_per_block + 1       ):
                A__				=			F"up_blocks.{i}.resnets.{j}"
                A__				=			F"output_blocks.{current_layer}.0"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       has_skip=_lowerCamelCase       )
                A__				=			F"up_blocks.{i}.attentions.{j}"
                A__				=			F"output_blocks.{current_layer}.1"
                A__				=			convert_attention(
                    _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
                current_layer += 1

            if i != len(_lowerCamelCase       ) - 1:
                A__				=			F"up_blocks.{i}.upsamplers.0"
                A__				=			F"output_blocks.{current_layer-1}.2"
                A__				=			convert_resnet(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )

    A__				=			checkpoint["out.0.weight"]
    A__				=			checkpoint["out.0.bias"]
    A__				=			checkpoint["out.2.weight"]
    A__				=			checkpoint["out.2.bias"]

    return new_checkpoint


if __name__ == "__main__":
  __lowerCAmelCase							:	List[Any]			=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.")

  __lowerCAmelCase							:	Optional[Any]			=parser.parse_args()
  __lowerCAmelCase							:	List[Any]			=strabool(args.class_cond)

  __lowerCAmelCase							:	List[str]			=os.path.basename(args.unet_path)
  print(f"""Checkpoint: {ckpt_name}""")

  # Get U-Net config
  if "imagenet64" in ckpt_name:
    __lowerCAmelCase							:	List[str]			=IMAGENET_64_UNET_CONFIG
  elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    __lowerCAmelCase							:	List[str]			=LSUN_256_UNET_CONFIG
  elif "test" in ckpt_name:
    __lowerCAmelCase							:	Any			=TEST_UNET_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  if not args.class_cond:
    __lowerCAmelCase							:	Dict			=None

  __lowerCAmelCase							:	Optional[int]			=con_pt_to_diffuser(args.unet_path, unet_config)

  __lowerCAmelCase							:	Dict			=UNetaDModel(**unet_config)
  image_unet.load_state_dict(converted_unet_ckpt)

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    __lowerCAmelCase							:	List[str]			=CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    __lowerCAmelCase							:	Dict			=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)):
    __lowerCAmelCase							:	Dict			=CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

  __lowerCAmelCase							:	Dict			=CMStochasticIterativeScheduler(**scheduler_config)

  __lowerCAmelCase							:	str			=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)



