"""simple docstring"""







import uuid
from typing import Any, Dict, List, Optional, Union

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


if is_tf_available():
       import tensorflow as tf

if is_torch_available():
       import torch


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase=None     ,__UpperCamelCase=None			)	->      Union[str, Any]:


        '''simple docstring'''


        if not conversation_id:
             lowercase_	:				Dict													=	uuid.uuida()
        if past_user_inputs is None:
             lowercase_	:				Union[str, Any]													=	[]
        if generated_responses is None:
             lowercase_	:				Any													=	[]

        lowercase_	:				uuid.UUID													=	conversation_id
        lowercase_	:				List[str]													=	past_user_inputs
        lowercase_	:				List[str]													=	generated_responses
        lowercase_	:				Optional[str]													=	text
   def __eq__(			self     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        if not isinstance(__UpperCamelCase     ,__UpperCamelCase			):
             return False
        if self.uuid == other.uuid:
             return True
        return (
            self.new_user_input == other.new_user_input
            and self.past_user_inputs == other.past_user_inputs
            and self.generated_responses == other.generated_responses
        )
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = False			)	->      List[str]:


        '''simple docstring'''


        if self.new_user_input:
             if overwrite:
                  logger.warning(
                      f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
                      f'''with: "{text}".'''			)
                  lowercase_	:				Tuple													=	text
             else:
                  logger.warning(
                      f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
                      f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''			)
        else:
             lowercase_	:				List[Any]													=	text
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        if self.new_user_input:
             self.past_user_inputs.append(self.new_user_input			)
        lowercase_	:				Union[str, Any]													=	None
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        self.generated_responses.append(__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        for user_input, generated_response in zip(self.past_user_inputs     ,self.generated_responses			):
             yield True, user_input
             yield False, generated_response
        if self.new_user_input:
             yield True, self.new_user_input







   def __repr__(			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				str													=	f'''Conversation id: {self.uuid} \n'''
        for is_user, text in self.iter_texts():
             lowercase_	:				Union[str, Any]													=	'user' if is_user else 'bot'
             output += f'''{name} >> {text} \n'''
        return output







@add_end_docstrings(
    lowercase_						,      r'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '						,      )
class        UpperCamelCase		(			lowercase_  ):
   def __init__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      str:


        '''simple docstring'''


        super().__init__(*__UpperCamelCase     ,**__UpperCamelCase			)
        if self.tokenizer.pad_token_id is None:
             lowercase_	:				List[str]													=	self.tokenizer.eos_token
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	{}
        lowercase_	:				int													=	{}
        lowercase_	:				Optional[Any]													=	{}

        if min_length_for_response is not None:
             lowercase_	:				List[str]													=	min_length_for_response
        if minimum_tokens is not None:
             lowercase_	:				int													=	minimum_tokens

        if "max_length" in generate_kwargs:
             lowercase_	:				List[str]													=	generate_kwargs['max_length']
             # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
        if clean_up_tokenization_spaces is not None:
             lowercase_	:				Optional[Any]													=	clean_up_tokenization_spaces

        if generate_kwargs:
             forward_params.update(__UpperCamelCase			)
        return preprocess_params, forward_params, postprocess_params
   def __call__(			self     ,__UpperCamelCase     ,__UpperCamelCase=0     ,**__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				int													=	super().__call__(__UpperCamelCase     ,num_workers=__UpperCamelCase     ,**__UpperCamelCase			)
        if isinstance(__UpperCamelCase     ,__UpperCamelCase			) and len(__UpperCamelCase			) == 1:
             return outputs[0]
        return outputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=32			)	->      Dict[str, Any]:


        '''simple docstring'''


        if not isinstance(__UpperCamelCase     ,__UpperCamelCase			):
             raise ValueError('ConversationalPipeline, expects Conversation as inputs'			)
        if conversation.new_user_input is None:
             raise ValueError(
                 f'''Conversation with UUID {type(conversation.uuid			)} does not contain new user input to process. '''
                 'Add user inputs with the conversation\'s `add_user_input` method'			)
        if hasattr(self.tokenizer     ,'_build_conversation_input_ids'			):
             lowercase_	:				Tuple													=	self.tokenizer._build_conversation_input_ids(__UpperCamelCase			)
        else:
             # If the tokenizer cannot handle conversations, we default to only the old version
             lowercase_	:				Tuple													=	self._legacy_parse_and_tokenize(__UpperCamelCase			)

        if self.framework == "pt":
             lowercase_	:				Optional[Any]													=	torch.LongTensor([input_ids]			)
        elif self.framework == "tf":
             lowercase_	:				int													=	tf.constant([input_ids]			)
        return {"input_ids": input_ids, "conversation": conversation}
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=10     ,**__UpperCamelCase			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	generate_kwargs.get('max_length'     ,self.model.config.max_length			)

        lowercase_	:				Tuple													=	model_inputs['input_ids'].shape[1]
        if max_length - minimum_tokens < n:
             logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''			)
             lowercase_	:				Optional[Any]													=	max_length - minimum_tokens
             lowercase_	:				Any													=	model_inputs['input_ids'][:, -trim:]
             if "attention_mask" in model_inputs:
                  lowercase_	:				Any													=	model_inputs['attention_mask'][:, -trim:]
        lowercase_	:				Union[str, Any]													=	model_inputs.pop('conversation'			)
        lowercase_	:				List[str]													=	max_length
        lowercase_	:				List[Any]													=	self.model.generate(**__UpperCamelCase     ,**__UpperCamelCase			)
        if self.model.config.is_encoder_decoder:
             lowercase_	:				Union[str, Any]													=	1
        else:
             lowercase_	:				Dict													=	n
        return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=True			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				List[str]													=	model_outputs['output_ids']
        lowercase_	:				Optional[Any]													=	self.tokenizer.decode(
            output_ids[0]     ,skip_special_tokens=__UpperCamelCase     ,clean_up_tokenization_spaces=__UpperCamelCase     ,)
        lowercase_	:				Union[str, Any]													=	model_outputs['conversation']
        conversation.mark_processed()
        conversation.append_response(__UpperCamelCase			)
        return conversation







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				int													=	self.tokenizer.eos_token_id
        lowercase_	:				Tuple													=	[]
        for is_user, text in conversation.iter_texts():
             if eos_token_id is not None:
                  input_ids.extend(self.tokenizer.encode(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			) + [eos_token_id]			)
             else:
                  input_ids.extend(self.tokenizer.encode(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)			)

        if len(__UpperCamelCase			) > self.tokenizer.model_max_length:
             lowercase_	:				List[str]													=	input_ids[-self.tokenizer.model_max_length :]
        return input_ids


"""simple docstring"""







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__SCREAMING_SNAKE_CASE    	={
    "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",
}

__SCREAMING_SNAKE_CASE    	={
    "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",
}

__SCREAMING_SNAKE_CASE    	={
    "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",
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

__SCREAMING_SNAKE_CASE    	={
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     if isinstance(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_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__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     str=False      ):
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.0.weight''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.in_layers.0.bias''']
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.2.weight''']
     lowercase_	:				str													=	checkpoint[F'''{old_prefix}.in_layers.2.bias''']
     lowercase_	:				Optional[Any]													=	checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
     lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.out_layers.0.weight''']
     lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.out_layers.0.bias''']
     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.out_layers.3.weight''']
     lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.out_layers.3.bias''']

     if has_skip:
          lowercase_	:				Optional[Any]													=	checkpoint[F'''{old_prefix}.skip_connection.weight''']
          lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.skip_connection.bias''']

     return new_checkpoint



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     str=None      ):
     lowercase_							,  lowercase_							,  lowercase_	:				Optional[int]													=	checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3     ,			dim=0      )
     lowercase_							,  lowercase_							,  lowercase_	:				Dict													=	checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3     ,			dim=0      )

     lowercase_	:				Any													=	checkpoint[F'''{old_prefix}.norm.weight''']
     lowercase_	:				Tuple													=	checkpoint[F'''{old_prefix}.norm.bias''']

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

     lowercase_	:				List[str]													=	(
         checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1      ).squeeze(-1      )
     )
     lowercase_	:				int													=	checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1      ).squeeze(-1      )

     return new_checkpoint



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple      ):
     lowercase_	:				Any													=	torch.load(__SCREAMING_SNAKE_CASE     ,			map_location='cpu'      )
     lowercase_	:				Any													=	{}

     lowercase_	:				Dict													=	checkpoint['time_embed.0.weight']
     lowercase_	:				Optional[Any]													=	checkpoint['time_embed.0.bias']
     lowercase_	:				List[str]													=	checkpoint['time_embed.2.weight']
     lowercase_	:				Any													=	checkpoint['time_embed.2.bias']

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

     lowercase_	:				Optional[Any]													=	checkpoint['input_blocks.0.0.weight']
     lowercase_	:				Optional[int]													=	checkpoint['input_blocks.0.0.bias']

     lowercase_	:				Dict													=	unet_config['down_block_types']
     lowercase_	:				int													=	unet_config['layers_per_block']
     lowercase_	:				int													=	unet_config['attention_head_dim']
     lowercase_	:				Any													=	unet_config['block_out_channels']
     lowercase_	:				Optional[Any]													=	1
     lowercase_	:				Tuple													=	channels_list[0]

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

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

          if i != len(__SCREAMING_SNAKE_CASE      ) - 1:
               lowercase_	:				Tuple													=	F'''down_blocks.{i}.downsamplers.0'''
               lowercase_	:				str													=	F'''input_blocks.{current_layer}.0'''
               lowercase_	:				Tuple													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
               current_layer += 1

          lowercase_	:				int													=	current_channels

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

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

     for i, layer_type in enumerate(__SCREAMING_SNAKE_CASE      ):
          if layer_type == "ResnetUpsampleBlock2D":
               for j in range(layers_per_block + 1      ):
                    lowercase_	:				Any													=	F'''up_blocks.{i}.resnets.{j}'''
                    lowercase_	:				Any													=	F'''output_blocks.{current_layer}.0'''
                    lowercase_	:				Union[str, Any]													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			has_skip=__SCREAMING_SNAKE_CASE      )
                    current_layer += 1

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

               if i != len(__SCREAMING_SNAKE_CASE      ) - 1:
                    lowercase_	:				Any													=	F'''up_blocks.{i}.upsamplers.0'''
                    lowercase_	:				str													=	F'''output_blocks.{current_layer-1}.2'''
                    lowercase_	:				int													=	convert_resnet(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     lowercase_	:				List[str]													=	checkpoint['out.0.weight']
     lowercase_	:				Dict													=	checkpoint['out.0.bias']
     lowercase_	:				Optional[int]													=	checkpoint['out.2.weight']
     lowercase_	:				Optional[int]													=	checkpoint['out.2.bias']

     return new_checkpoint


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=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.")

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

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

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

       if not args.class_cond:
              __SCREAMING_SNAKE_CASE    	=None

       __SCREAMING_SNAKE_CASE    	=con_pt_to_diffuser(args.unet_path, unet_config)

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

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

       __SCREAMING_SNAKE_CASE    	=CMStochasticIterativeScheduler(**scheduler_config)

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

