
'''simple docstring'''


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


snake_case_ :  Tuple     =					{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "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",
}

snake_case_ :  Optional[Any]     =					{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "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",
}

snake_case_ :  Optional[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",
}

snake_case_ :  Optional[Any]     =					{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

snake_case_ :  List[str]     =					{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

snake_case_ :  str     =					{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}



def  __snake_case     (    _UpperCAmelCase					:       int):


   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  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any=False):
   UpperCamelCase       =  checkpoint[f'{old_prefix}.in_layers.0.weight']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.in_layers.0.bias']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.in_layers.2.weight']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.in_layers.2.bias']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.emb_layers.1.weight']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.emb_layers.1.bias']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.out_layers.0.weight']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.out_layers.0.bias']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.out_layers.3.weight']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.out_layers.3.bias']

   if has_skip:
      UpperCamelCase       =  checkpoint[f'{old_prefix}.skip_connection.weight']
      UpperCamelCase       =  checkpoint[f'{old_prefix}.skip_connection.bias']

   return new_checkpoint



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Optional[int]=None):
   UpperCamelCase       =  checkpoint[f'{old_prefix}.qkv.weight'].chunk(3,      dim=0)
   UpperCamelCase       =  checkpoint[f'{old_prefix}.qkv.bias'].chunk(3,      dim=0)

   UpperCamelCase       =  checkpoint[f'{old_prefix}.norm.weight']
   UpperCamelCase       =  checkpoint[f'{old_prefix}.norm.bias']

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

   UpperCamelCase       =  (
       checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1).squeeze(-1)
   )
   UpperCamelCase       =  checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1).squeeze(-1)

   return new_checkpoint



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  torch.load(lowerCamelCase__,      map_location='''cpu''')
   UpperCamelCase       =  {}

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

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

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

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

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

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

      if i != len(lowerCamelCase__) - 1:
         UpperCamelCase       =  f'down_blocks.{i}.downsamplers.0'
         UpperCamelCase       =  f'input_blocks.{current_layer}.0'
         UpperCamelCase       =  convert_resnet(lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__)
         current_layer += 1

      UpperCamelCase       =  current_channels

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

   UpperCamelCase       =  0
   UpperCamelCase       =  unet_config["up_block_types"]

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

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

         if i != len(lowerCamelCase__) - 1:
            UpperCamelCase       =  f'up_blocks.{i}.upsamplers.0'
            UpperCamelCase       =  f'output_blocks.{current_layer-1}.2'
            UpperCamelCase       =  convert_resnet(lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__)

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

   return new_checkpoint


if __name__ == "__main__":
   snake_case_ :  Optional[int]     =					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.')

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

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

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

   if not args.class_cond:
      snake_case_ :  List[str]     =					None

   snake_case_ :  str     =					con_pt_to_diffuser(args.unet_path, unet_config)

   snake_case_ :  str     =					UNetaDModel(**unet_config)
   image_unet.load_state_dict(converted_unet_ckpt)

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

   snake_case_ :  Optional[int]     =					CMStochasticIterativeScheduler(**scheduler_config)

   snake_case_ :  Optional[Any]     =					ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
   consistency_model.save_pretrained(args.dump_path)







'''simple docstring'''


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
   from ..models.auto import AutoTokenizer


class 						lowercase__	:






   '''simple docstring'''







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      raise NotImplementedError()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      raise NotImplementedError()




class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  tokenizer
      UpperCamelCase       =  skip_prompt
      UpperCamelCase       =  decode_kwargs

      # variables used in the streaming process
      UpperCamelCase       =  []
      UpperCamelCase       =  0
      UpperCamelCase       =  True







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if len(value.shape   ) > 1 and value.shape[0] > 1:
         raise ValueError('''TextStreamer only supports batch size 1'''   )
      elif len(value.shape   ) > 1:
         UpperCamelCase       =  value[0]

      if self.skip_prompt and self.next_tokens_are_prompt:
         UpperCamelCase       =  False
         return

      # Add the new token to the cache and decodes the entire thing.
      self.token_cache.extend(value.tolist()   )
      UpperCamelCase       =  self.tokenizer.decode(self.token_cache ,   **self.decode_kwargs   )

      # After the symbol for a new line, we flush the cache.
      if text.endswith('''\n'''   ):
         UpperCamelCase       =  text[self.print_len :]
         UpperCamelCase       =  []
         UpperCamelCase       =  0
      # If the last token is a CJK character, we print the characters.
      elif len(lowerCamelCase__   ) > 0 and self._is_chinese_char(ord(text[-1]   )   ):
         UpperCamelCase       =  text[self.print_len :]
         self.print_len += len(lowerCamelCase__   )
      # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
      # which may change with the subsequent token -- there are probably smarter ways to do this!)
      else:
         UpperCamelCase       =  text[self.print_len : text.rfind(''' '''   ) + 1]
         self.print_len += len(lowerCamelCase__   )

      self.on_finalized_text(lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if len(self.token_cache   ) > 0:
         UpperCamelCase       =  self.tokenizer.decode(self.token_cache ,   **self.decode_kwargs   )
         UpperCamelCase       =  text[self.print_len :]
         UpperCamelCase       =  []
         UpperCamelCase       =  0
      else:
         UpperCamelCase       =  ''''''

      UpperCamelCase       =  True
      self.on_finalized_text(lowerCamelCase__ ,   stream_end=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      print(lowerCamelCase__ ,   flush=lowerCamelCase__ ,   end='''''' if not stream_end else None   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if (
          (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
          or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
          or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
          or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
          or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
          or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
          or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
          or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
      ):  #
         return True

      return False




class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False ,   lowerCamelCase__ = None ,   **lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__ ,   **lowerCamelCase__   )
      UpperCamelCase       =  Queue()
      UpperCamelCase       =  None
      UpperCamelCase       =  timeout







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      self.text_queue.put(lowerCamelCase__ ,   timeout=self.timeout   )
      if stream_end:
         self.text_queue.put(self.stop_signal ,   timeout=self.timeout   )







   def __iter__(			self   ):

      '''simple docstring'''







      return self







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.text_queue.get(timeout=self.timeout   )
      if value == self.stop_signal:
         raise StopIteration()
      else:
         return value





