


'''simple docstring'''


import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


__magic_name__								=   logging.get_logger(__name__)

# General docstring
__magic_name__								=   '''PoolFormerConfig'''

# Base docstring
__magic_name__								=   '''sail/poolformer_s12'''
__magic_name__								=   [1, 512, 7, 7]

# Image classification docstring
__magic_name__								=   '''sail/poolformer_s12'''
__magic_name__								=   '''tabby, tabby cat'''

__magic_name__								=   [
    '''sail/poolformer_s12''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]




def   lowerCamelCase       (   lowerCamelCase	:   List[Any]				,    lowerCamelCase	:   float = 0.0				,    lowerCamelCase	:   bool = False):





      if drop_prob == 0.0 or not training:
            return input
      A_    :	List[Any]							 =					1 - drop_prob
      A_    :	Optional[Any]							 =					(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
      A_    :	List[Any]							 =					keep_prob + torch.rand(a__				,    dtype=input.dtype				,    device=input.device)
      random_tensor.floor_()  # binarize
      A_    :	str							 =					input.div(a__) * random_tensor
      return output



class 						__lowerCAmelCase		(				nn.Module     ):


  '''simple docstring'''







  def __init__(				self			:							Optional[int]						,_a			:							Any = None			):

        '''simple docstring'''




        super().__init__()
        A_    :	Dict							 =					drop_prob






  def 							_a				(				self			:							Dict						,_a			:							Union[str, Any]			):

        '''simple docstring'''




        return drop_path(_SCREAMING_SNAKE_CASE						,self.drop_prob						,self.training			)






  def 							_a				(				self			:							List[str]			):

        '''simple docstring'''




        return "p={}".format(self.drop_prob			)







class 						__lowerCAmelCase		(				nn.Module     ):


  '''simple docstring'''







  def __init__(				self			:							Optional[int]						,_a			:							str						,_a			:							Any						,_a			:							Any						,_a			:							Optional[int]						,_a			:							Optional[Any]						,_a			:							Dict=None			):

        '''simple docstring'''




        super().__init__()
        A_    :	Union[str, Any]							 =					patch_size if isinstance(_SCREAMING_SNAKE_CASE						,collections.abc.Iterable			) else (patch_size, patch_size)
        A_    :	Optional[Any]							 =					stride if isinstance(_SCREAMING_SNAKE_CASE						,collections.abc.Iterable			) else (stride, stride)
        A_    :	Tuple							 =					padding if isinstance(_SCREAMING_SNAKE_CASE						,collections.abc.Iterable			) else (padding, padding)

        A_    :	Optional[int]							 =					nn.Convad(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,kernel_size=_SCREAMING_SNAKE_CASE						,stride=_SCREAMING_SNAKE_CASE						,padding=_SCREAMING_SNAKE_CASE			)
        A_    :	int							 =					norm_layer(_SCREAMING_SNAKE_CASE			) if norm_layer else nn.Identity()






  def 							_a				(				self			:							Tuple						,_a			:							int			):

        '''simple docstring'''




        A_    :	Optional[Any]							 =					self.projection(_SCREAMING_SNAKE_CASE			)
        A_    :	int							 =					self.norm(_SCREAMING_SNAKE_CASE			)
        return embeddings







class 						__lowerCAmelCase		(				nn.GroupNorm     ):


  '''simple docstring'''







  def __init__(				self			:							Any						,_a			:							Optional[int]						,**_a			:							Optional[int]			):

        '''simple docstring'''




        super().__init__(1						,_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE			)







class 						__lowerCAmelCase		(				nn.Module     ):


  '''simple docstring'''







  def __init__(				self			:							Optional[int]						,_a			:							Union[str, Any]			):

        '''simple docstring'''




        super().__init__()
        A_    :	Optional[int]							 =					nn.AvgPoolad(_SCREAMING_SNAKE_CASE						,stride=1						,padding=pool_size // 2						,count_include_pad=_SCREAMING_SNAKE_CASE			)






  def 							_a				(				self			:							Optional[Any]						,_a			:							Optional[int]			):

        '''simple docstring'''




        return self.pool(_SCREAMING_SNAKE_CASE			) - hidden_states







class 						__lowerCAmelCase		(				nn.Module     ):


  '''simple docstring'''







  def __init__(				self			:							Any						,_a			:							Tuple						,_a			:							List[str]						,_a			:							Any						,_a			:							Union[str, Any]			):

        '''simple docstring'''




        super().__init__()
        A_    :	Any							 =					nn.Convad(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,1			)
        A_    :	str							 =					nn.Convad(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,1			)
        A_    :	List[Any]							 =					PoolFormerDropPath(_SCREAMING_SNAKE_CASE			)
        if isinstance(config.hidden_act						,_SCREAMING_SNAKE_CASE			):
              A_    :	Any							 =					ACTaFN[config.hidden_act]
        else:
              A_    :	List[str]							 =					config.hidden_act






  def 							_a				(				self			:							Union[str, Any]						,_a			:							Dict			):

        '''simple docstring'''




        A_    :	str							 =					self.conva(_SCREAMING_SNAKE_CASE			)
        A_    :	Tuple							 =					self.act_fn(_SCREAMING_SNAKE_CASE			)
        A_    :	List[str]							 =					self.drop(_SCREAMING_SNAKE_CASE			)
        A_    :	int							 =					self.conva(_SCREAMING_SNAKE_CASE			)
        A_    :	int							 =					self.drop(_SCREAMING_SNAKE_CASE			)

        return hidden_states







class 						__lowerCAmelCase		(				nn.Module     ):


  '''simple docstring'''







  def __init__(				self			:							Tuple						,_a			:							Union[str, Any]						,_a			:							str						,_a			:							Optional[Any]						,_a			:							Union[str, Any]						,_a			:							int						,_a			:							List[Any]			):

        '''simple docstring'''




        super().__init__()
        A_    :	Optional[Any]							 =					PoolFormerPooling(_SCREAMING_SNAKE_CASE			)
        A_    :	int							 =					PoolFormerOutput(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE			)
        A_    :	Any							 =					PoolFormerGroupNorm(_SCREAMING_SNAKE_CASE			)
        A_    :	Dict							 =					PoolFormerGroupNorm(_SCREAMING_SNAKE_CASE			)

        # Useful for training neural nets
        A_    :	List[Any]							 =					PoolFormerDropPath(_SCREAMING_SNAKE_CASE			) if drop_path > 0.0 else nn.Identity()
        A_    :	Any							 =					config.use_layer_scale
        if config.use_layer_scale:
              A_    :	Union[str, Any]							 =					nn.Parameter(
                  config.layer_scale_init_value * torch.ones((_SCREAMING_SNAKE_CASE)			)						,requires_grad=_SCREAMING_SNAKE_CASE			)
              A_    :	List[Any]							 =					nn.Parameter(
                  config.layer_scale_init_value * torch.ones((_SCREAMING_SNAKE_CASE)			)						,requires_grad=_SCREAMING_SNAKE_CASE			)






  def 							_a				(				self			:							List[Any]						,_a			:							Tuple			):

        '''simple docstring'''




        if self.use_layer_scale:
              A_    :	Optional[int]							 =					self.pooling(self.before_norm(_SCREAMING_SNAKE_CASE			)			)
              A_    :	List[Any]							 =					self.layer_scale_a.unsqueeze(-1			).unsqueeze(-1			) * pooling_output
              # First residual connection
              A_    :	Optional[int]							 =					hidden_states + self.drop_path(_SCREAMING_SNAKE_CASE			)
              A_    :	Tuple							 =					()

              A_    :	Union[str, Any]							 =					self.output(self.after_norm(_SCREAMING_SNAKE_CASE			)			)
              A_    :	List[str]							 =					self.layer_scale_a.unsqueeze(-1			).unsqueeze(-1			) * layer_output
              # Second residual connection
              A_    :	Tuple							 =					hidden_states + self.drop_path(_SCREAMING_SNAKE_CASE			)

              A_    :	str							 =					(output,) + outputs
              return outputs

        else:
              A_    :	Tuple							 =					self.drop_path(self.pooling(self.before_norm(_SCREAMING_SNAKE_CASE			)			)			)
              # First residual connection
              A_    :	List[Any]							 =					pooling_output + hidden_states
              A_    :	Optional[int]							 =					()

              # Second residual connection inside the PoolFormerOutput block
              A_    :	int							 =					self.drop_path(self.output(self.after_norm(_SCREAMING_SNAKE_CASE			)			)			)
              A_    :	Optional[int]							 =					hidden_states + layer_output

              A_    :	List[str]							 =					(output,) + outputs
              return outputs







class 						__lowerCAmelCase		(				nn.Module     ):


  '''simple docstring'''







  def __init__(				self			:							Union[str, Any]						,_a			:							List[Any]			):

        '''simple docstring'''




        super().__init__()
        A_    :	str							 =					config
        # stochastic depth decay rule
        A_    :	Tuple							 =					[x.item() for x in torch.linspace(0						,config.drop_path_rate						,sum(config.depths			)			)]

        # patch embeddings
        A_    :	int							 =					[]
        for i in range(config.num_encoder_blocks			):
              embeddings.append(
                  PoolFormerEmbeddings(
                      patch_size=config.patch_sizes[i]						,stride=config.strides[i]						,padding=config.padding[i]						,num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]						,hidden_size=config.hidden_sizes[i]						,)			)
        A_    :	Dict							 =					nn.ModuleList(_SCREAMING_SNAKE_CASE			)

        # Transformer blocks
        A_    :	Union[str, Any]							 =					[]
        A_    :	int							 =					0
        for i in range(config.num_encoder_blocks			):
              # each block consists of layers
              A_    :	Tuple							 =					[]
              if i != 0:
                    cur += config.depths[i - 1]
              for j in range(config.depths[i]			):
                    layers.append(
                        PoolFormerLayer(
                            _SCREAMING_SNAKE_CASE						,num_channels=config.hidden_sizes[i]						,pool_size=config.pool_size						,hidden_size=config.hidden_sizes[i]						,intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio			)						,drop_path=dpr[cur + j]						,)			)
              blocks.append(nn.ModuleList(_SCREAMING_SNAKE_CASE			)			)

        A_    :	Optional[Any]							 =					nn.ModuleList(_SCREAMING_SNAKE_CASE			)






  def 							_a				(				self			:							List[str]						,_a			:							Union[str, Any]						,_a			:							int=False						,_a			:							str=True			):

        '''simple docstring'''




        A_    :	str							 =					() if output_hidden_states else None

        A_    :	Optional[Any]							 =					pixel_values
        for idx, layers in enumerate(zip(self.patch_embeddings						,self.block			)			):
              A_ ,       A_    :	int							 =					layers
              # Get patch embeddings from hidden_states
              A_    :	Dict							 =					embedding_layer(_SCREAMING_SNAKE_CASE			)
              # Send the embeddings through the blocks
              for _, blk in enumerate(_SCREAMING_SNAKE_CASE			):
                    A_    :	int							 =					blk(_SCREAMING_SNAKE_CASE			)
                    A_    :	Union[str, Any]							 =					layer_outputs[0]

              if output_hidden_states:
                    A_    :	List[str]							 =					all_hidden_states + (hidden_states,)

        if not return_dict:
              return tuple(v for v in [hidden_states, all_hidden_states] if v is not None			)

        return BaseModelOutputWithNoAttention(last_hidden_state=_SCREAMING_SNAKE_CASE						,hidden_states=_SCREAMING_SNAKE_CASE			)







class 						__lowerCAmelCase		(				__SCREAMING_SNAKE_CASE     ):


  '''simple docstring'''







  a_	       =				PoolFormerConfig
  a_	       =				'poolformer'
  a_	       =				'pixel_values'
  a_	       =				True






  def 							_a				(				self			:							Tuple						,_a			:							List[str]			):

        '''simple docstring'''




        if isinstance(_SCREAMING_SNAKE_CASE						,(nn.Linear, nn.Convad)			):
              module.weight.data.normal_(mean=0.0						,std=self.config.initializer_range			)
              if module.bias is not None:
                    module.bias.data.zero_()
        elif isinstance(_SCREAMING_SNAKE_CASE						,nn.LayerNorm			):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0			)






  def 							_a				(				self			:							Union[str, Any]						,_a			:							List[Any]						,_a			:							Optional[int]=False			):

        '''simple docstring'''




        if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE			):
              A_    :	List[Any]							 =					value







__magic_name__								=   R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__magic_name__								=   R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
'''



@add_start_docstrings(
    """The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."""	,			__SCREAMING_SNAKE_CASE	,			)
class 						__lowerCAmelCase		(				__SCREAMING_SNAKE_CASE     ):


  '''simple docstring'''







  def __init__(				self			:							str						,_a			:							Tuple			):

        '''simple docstring'''




        super().__init__(_SCREAMING_SNAKE_CASE			)
        A_    :	List[str]							 =					config

        A_    :	str							 =					PoolFormerEncoder(_SCREAMING_SNAKE_CASE			)

        # Initialize weights and apply final processing
        self.post_init()






  def 							_a				(				self			:							List[Any]			):

        '''simple docstring'''




        return self.embeddings.patch_embeddings






  @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE			)
  @add_code_sample_docstrings(
      checkpoint=_CHECKPOINT_FOR_DOC						,output_type=_SCREAMING_SNAKE_CASE						,config_class=_CONFIG_FOR_DOC						,modality="""vision"""						,expected_output=_EXPECTED_OUTPUT_SHAPE						,)
  def 							_a				(				self			:							Dict						,_a			:							Optional[Any] = None						,_a			:							Dict = None						,_a			:							List[str] = None						,):

        '''simple docstring'''




        A_    :	Tuple							 =					(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        A_    :	Any							 =					return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None:
              raise ValueError("""You have to specify pixel_values"""			)

        A_    :	int							 =					self.encoder(
            _SCREAMING_SNAKE_CASE						,output_hidden_states=_SCREAMING_SNAKE_CASE						,return_dict=_SCREAMING_SNAKE_CASE						,)
        A_    :	Optional[Any]							 =					encoder_outputs[0]

        if not return_dict:
              return (sequence_output, None) + encoder_outputs[1:]

        return BaseModelOutputWithNoAttention(
            last_hidden_state=_SCREAMING_SNAKE_CASE						,hidden_states=encoder_outputs.hidden_states						,)







class 						__lowerCAmelCase		(				nn.Module     ):


  '''simple docstring'''







  def __init__(				self			:							Optional[Any]						,_a			:							List[Any]			):

        '''simple docstring'''




        super().__init__()
        A_    :	int							 =					nn.Linear(config.hidden_size						,config.hidden_size			)






  def 							_a				(				self			:							Union[str, Any]						,_a			:							List[Any]			):

        '''simple docstring'''




        A_    :	str							 =					self.dense(_SCREAMING_SNAKE_CASE			)
        return output







@add_start_docstrings(
    """\n    PoolFormer Model transformer with an image classification head on top\n    """	,			__SCREAMING_SNAKE_CASE	,			)
class 						__lowerCAmelCase		(				__SCREAMING_SNAKE_CASE     ):


  '''simple docstring'''







  def __init__(				self			:							Any						,_a			:							Dict			):

        '''simple docstring'''




        super().__init__(_SCREAMING_SNAKE_CASE			)
        A_    :	List[Any]							 =					config.num_labels
        A_    :	Tuple							 =					PoolFormerModel(_SCREAMING_SNAKE_CASE			)

        # Final norm
        A_    :	Any							 =					PoolFormerGroupNorm(config.hidden_sizes[-1]			)
        # Classifier head
        A_    :	Union[str, Any]							 =					(
            nn.Linear(config.hidden_sizes[-1]						,config.num_labels			) if config.num_labels > 0 else nn.Identity()
        )

        # Initialize weights and apply final processing
        self.post_init()






  @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE			)
  @add_code_sample_docstrings(
      checkpoint=_IMAGE_CLASS_CHECKPOINT						,output_type=_SCREAMING_SNAKE_CASE						,config_class=_CONFIG_FOR_DOC						,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,)
  def 							_a				(				self			:							Optional[Any]						,_a			:							List[Any] = None						,_a			:							Optional[int] = None						,_a			:							List[Any] = None						,_a			:							List[str] = None						,):

        '''simple docstring'''




        A_    :	List[str]							 =					return_dict if return_dict is not None else self.config.use_return_dict

        A_    :	Dict							 =					self.poolformer(
            _SCREAMING_SNAKE_CASE						,output_hidden_states=_SCREAMING_SNAKE_CASE						,return_dict=_SCREAMING_SNAKE_CASE						,)

        A_    :	str							 =					outputs[0]

        A_    :	Optional[int]							 =					self.classifier(self.norm(_SCREAMING_SNAKE_CASE			).mean([-2, -1]			)			)

        A_    :	Union[str, Any]							 =					None
        if labels is not None:
              if self.config.problem_type is None:
                    if self.num_labels == 1:
                          A_    :	List[str]							 =					"""regression"""
                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                          A_    :	Optional[int]							 =					"""single_label_classification"""
                    else:
                          A_    :	str							 =					"""multi_label_classification"""

              if self.config.problem_type == "regression":
                    A_    :	Tuple							 =					MSELoss()
                    if self.num_labels == 1:
                          A_    :	Dict							 =					loss_fct(logits.squeeze()						,labels.squeeze()			)
                    else:
                          A_    :	List[Any]							 =					loss_fct(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE			)
              elif self.config.problem_type == "single_label_classification":
                    A_    :	Optional[int]							 =					CrossEntropyLoss()
                    A_    :	Union[str, Any]							 =					loss_fct(logits.view(-1						,self.num_labels			)						,labels.view(-1			)			)
              elif self.config.problem_type == "multi_label_classification":
                    A_    :	Optional[int]							 =					BCEWithLogitsLoss()
                    A_    :	Dict							 =					loss_fct(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE			)

        if not return_dict:
              A_    :	List[Any]							 =					(logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutputWithNoAttention(loss=_SCREAMING_SNAKE_CASE						,logits=_SCREAMING_SNAKE_CASE						,hidden_states=outputs.hidden_states			)




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


lowerCAmelCase__    :List[Any]       =				{
    '''sample_size''': 3_2,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [3_2, 6_4],
    '''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__    :List[str]       =				{
    '''sample_size''': 6_4,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    '''attention_head_dim''': 6_4,
    '''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__    :Any       =				{
    '''sample_size''': 2_5_6,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    '''attention_head_dim''': 6_4,
    '''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__    :Optional[int]       =				{
    '''num_train_timesteps''': 4_0,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowerCAmelCase__    :Optional[int]       =				{
    '''num_train_timesteps''': 2_0_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

lowerCAmelCase__    :Any       =				{
    '''num_train_timesteps''': 1_5_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

def 			lowerCAmelCase__					(     a__:						Optional[int]			)    ->						int:


							'''simple docstring'''





							if isinstance(a__     ,    a__			):
														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 			lowerCAmelCase__					(     a__:						Dict     ,    a__:						int     ,    a__:						Union[str, Any]     ,    a__:						Dict     ,    a__:						Optional[int]=False			)    ->						Dict:


							'''simple docstring'''





							_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 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Any     ,    a__:						List[str]     ,    a__:						List[Any]     ,    a__:						List[str]=None			)    ->						Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3     ,    dim=0			)
							_UpperCAmelCase						,			_UpperCAmelCase						,			_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 			lowerCAmelCase__					(     a__:						str     ,    a__:						Any			)    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     torch.load(a__     ,    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(a__			):
														_UpperCAmelCase											=     channels_list[i]
														_UpperCAmelCase											=     current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(a__			):
																												_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(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(a__			):
																												_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(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												_UpperCAmelCase											=     F'''down_blocks.{i}.attentions.{j}'''
																												_UpperCAmelCase											=     F'''input_blocks.{current_layer}.1'''
																												_UpperCAmelCase											=     convert_attention(
																												    a__     ,    a__     ,    a__     ,    a__     ,    a__			)
																												current_layer += 1

														if i != len(a__			) - 1:
																					_UpperCAmelCase											=     F'''down_blocks.{i}.downsamplers.0'''
																					_UpperCAmelCase											=     F'''input_blocks.{current_layer}.0'''
																					_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
																					current_layer += 1

														_UpperCAmelCase											=     current_channels

							# hardcoded the mid-block for now
							_UpperCAmelCase											=     'mid_block.resnets.0'
							_UpperCAmelCase											=     'middle_block.0'
							_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
							_UpperCAmelCase											=     'mid_block.attentions.0'
							_UpperCAmelCase											=     'middle_block.1'
							_UpperCAmelCase											=     convert_attention(a__     ,    a__     ,    a__     ,    a__     ,    a__			)
							_UpperCAmelCase											=     'mid_block.resnets.1'
							_UpperCAmelCase											=     'middle_block.2'
							_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)

							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     unet_config['up_block_types']

							for i, layer_type in enumerate(a__			):
														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(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												current_layer += 1

																					if i != len(a__			) - 1:
																												_UpperCAmelCase											=     F'''up_blocks.{i}.upsamplers.0'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer-1}.1'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)
														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(a__     ,    a__     ,    a__     ,    a__     ,    has_skip=a__			)
																												_UpperCAmelCase											=     F'''up_blocks.{i}.attentions.{j}'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer}.1'''
																												_UpperCAmelCase											=     convert_attention(
																												    a__     ,    a__     ,    a__     ,    a__     ,    a__			)
																												current_layer += 1

																					if i != len(a__			) - 1:
																												_UpperCAmelCase											=     F'''up_blocks.{i}.upsamplers.0'''
																												_UpperCAmelCase											=     F'''output_blocks.{current_layer-1}.2'''
																												_UpperCAmelCase											=     convert_resnet(a__     ,    a__     ,    a__     ,    a__			)

							_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__":
		lowerCAmelCase__    :Union[str, 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__    :Dict       =				parser.parse_args()
		lowerCAmelCase__    :List[str]       =				strabool(args.class_cond)

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

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				lowerCAmelCase__    :Union[str, Any]       =				IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				lowerCAmelCase__    :Tuple       =				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__    :str       =				None

		lowerCAmelCase__    :Tuple       =				con_pt_to_diffuser(args.unet_path, unet_config)

		lowerCAmelCase__    :Optional[int]       =				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__    :int       =				CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				lowerCAmelCase__    :Union[str, 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)):
				lowerCAmelCase__    :Tuple       =				CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		lowerCAmelCase__    :Any       =				CMStochasticIterativeScheduler(**scheduler_config)

		lowerCAmelCase__    :Union[str, Any]       =				ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)
