import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


lowerCamelCase_				:		int       						=       logging.get_logger(__name__)

lowerCamelCase_				:		Optional[int]       						=       [
    """yjernite/retribert-base-uncased""",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]


class 						a__							(   UpperCAmelCase_		):
			A__				:   Dict								=			RetriBertConfig
			A__				:   List[str]								=			None
			A__				:   int								=			"""retribert"""



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:





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






lowerCamelCase_				:		List[Any]       						=       r"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

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

    Parameters:
        config ([`RetriBertConfig`]): 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.
"""


@add_start_docstrings(
    'Bert Based model to embed queries or document for document retrieval.'      ,						UpperCAmelCase_      ,						)
class 						a__							(   UpperCAmelCase_		):



			def __init__(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								super().__init__(UpperCAmelCase    )
								__a							=   config.projection_dim

								__a							=   BertModel(UpperCAmelCase    )
								__a							=   None if config.share_encoders else BertModel(UpperCAmelCase    )
								__a							=   nn.Dropout(config.hidden_dropout_prob    )
								__a							=   nn.Linear(config.hidden_size      ,						config.projection_dim      ,						bias=UpperCAmelCase    )
								__a							=   nn.Linear(config.hidden_size      ,						config.projection_dim      ,						bias=UpperCAmelCase    )

								__a							=   nn.CrossEntropyLoss(reduction='mean'    )

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



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=-1      ,						) ->    Optional[int]:





								if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
													return sent_encoder(UpperCAmelCase      ,						attention_mask=UpperCAmelCase    )[1]
								else:
													# prepare implicit variables
													__a							=   input_ids.device
													__a							=   input_ids.size()
													__a							=   torch.zeros(UpperCAmelCase      ,						dtype=torch.long      ,						device=UpperCAmelCase    )
													__a							=   [None] * sent_encoder.config.num_hidden_layers
													__a							=   sent_encoder.get_extended_attention_mask(
													    UpperCAmelCase      ,						UpperCAmelCase    )

													# define function for checkpointing
													def partial_encode(*UpperCAmelCase    ):
																		__a							=   sent_encoder.encoder(
																		    inputs[0]      ,						attention_mask=inputs[1]      ,						head_mask=UpperCAmelCase      ,						)
																		__a							=   encoder_outputs[0]
																		__a							=   sent_encoder.pooler(UpperCAmelCase    )
																		return pooled_output

													# run embedding layer on everything at once
													__a							=   sent_encoder.embeddings(
													    input_ids=UpperCAmelCase      ,						position_ids=UpperCAmelCase      ,						token_type_ids=UpperCAmelCase      ,						inputs_embeds=UpperCAmelCase    )
													# run encoding and pooling on one mini-batch at a time
													__a							=   []
													for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size    )    ):
																		__a							=   embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																		__a							=   extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																		__a							=   checkpoint.checkpoint(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
																		pooled_output_list.append(UpperCAmelCase    )
													return torch.cat(UpperCAmelCase      ,						dim=0    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=None      ,						UpperCAmelCase=-1      ,						) ->    List[Any]:
								__a							=   self.embed_sentences_checkpointed(
								    UpperCAmelCase      ,						UpperCAmelCase      ,						self.bert_query      ,						UpperCAmelCase      ,						)
								return self.project_query(UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=None      ,						UpperCAmelCase=-1      ,						) ->    int:
								__a							=   self.embed_sentences_checkpointed(
								    UpperCAmelCase      ,						UpperCAmelCase      ,						self.bert_query if self.bert_doc is None else self.bert_doc      ,						UpperCAmelCase      ,						)
								return self.project_doc(UpperCAmelCase    )







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase = -1      ,						) ->    List[Any]:
								__a							=   input_ids_query.device
								__a							=   self.embed_questions(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
								__a							=   self.embed_answers(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
								__a							=   torch.mm(UpperCAmelCase      ,						a_reps.t()    )
								__a							=   self.ce_loss(UpperCAmelCase      ,						torch.arange(compare_scores.shape[1]    ).to(UpperCAmelCase    )    )
								__a							=   self.ce_loss(compare_scores.t()      ,						torch.arange(compare_scores.shape[0]    ).to(UpperCAmelCase    )    )
								__a							=   (loss_qa + loss_aq) / 2
								return loss











import argparse

import torch

from diffusers import UNetaDConditionModel


def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		List[str]			,							lowerCAmelCase:		str			,							lowerCAmelCase:		Optional[Any]=None			,							lowerCAmelCase:		List[Any]=None			,							lowerCAmelCase:		Optional[int]=None )->						int:
  assert isinstance(lowerCAmelCase			,							lowerCAmelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      _snake_case      : Any														=		old_checkpoint[path]
      _snake_case      : Dict														=		old_tensor.shape[0] // 3

      _snake_case      : Dict														=		(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

      _snake_case      : Union[str, Any]														=		old_tensor.shape[0] // config['num_head_channels'] // 3

      _snake_case      : Dict														=		old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
      _snake_case							,    _snake_case							,    _snake_case      : Any														=		old_tensor.split(channels // num_heads			,							dim=1 )

      _snake_case      : List[Any]														=		query.reshape(lowerCAmelCase )
      _snake_case      : int														=		key.reshape(lowerCAmelCase )
      _snake_case      : Union[str, Any]														=		value.reshape(lowerCAmelCase )

  for path in paths:
    _snake_case      : str														=		path['new']

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    if additional_replacements is not None:
      for replacement in additional_replacements:
        _snake_case      : Dict														=		new_path.replace(replacement['old']			,							replacement['new'] )

        # proj_attn.weight has to be converted from conv 1D to linear
    _snake_case      : str														=		old_checkpoint[path['old']]
    _snake_case      : List[str]														=		['proj_attn.weight']
    _snake_case      : Dict														=		['proj_out.weight', 'proj_in.weight']
    if any(k in new_path for k in names ):
      _snake_case      : int														=		weight[:, :, 0]
    elif any(k in new_path for k in names_a ) and len(weight.shape ) > 2 and ".attentions." not in new_path:
      _snake_case      : List[Any]														=		weight[:, :, 0]
    else:
      _snake_case      : Dict														=		weight


def 			lowerCamelCase_		(					lowerCAmelCase:		int			,							lowerCAmelCase:		List[Any]=0 )->						Optional[int]:
  _snake_case      : Any														=		[]
  for old_item in old_list:
    _snake_case      : str														=		old_item

    #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
    #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

    #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
    #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

    #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

    mapping.append({'old': old_item, 'new': new_item} )

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		Any			,							lowerCAmelCase:		Tuple=1 )->						Union[str, Any]:
  if n_shave_prefix_segments >= 0:
    return ".".join(path.split('.' )[n_shave_prefix_segments:] )
  else:
    return ".".join(path.split('.' )[:n_shave_prefix_segments] )


def 			lowerCamelCase_		(					lowerCAmelCase:		Any			,							lowerCAmelCase:		Optional[Any]=0 )->						List[Any]:
  _snake_case      : Optional[Any]														=		[]
  for old_item in old_list:
    mapping.append({'old': old_item, 'new': old_item} )

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		str=0 )->						str:
  _snake_case      : int														=		[]
  for old_item in old_list:
    _snake_case      : List[Any]														=		old_item.replace('in_layers.0'			,							'norm1' )
    _snake_case      : Any														=		new_item.replace('in_layers.2'			,							'conv1' )

    _snake_case      : List[Any]														=		new_item.replace('out_layers.0'			,							'norm2' )
    _snake_case      : List[str]														=		new_item.replace('out_layers.3'			,							'conv2' )

    _snake_case      : int														=		new_item.replace('emb_layers.1'			,							'time_emb_proj' )
    _snake_case      : Dict														=		new_item.replace('skip_connection'			,							'conv_shortcut' )

    _snake_case      : int														=		shave_segments(lowerCAmelCase			,							n_shave_prefix_segments=lowerCAmelCase )

    if "temopral_conv" not in old_item:
      mapping.append({'old': old_item, 'new': new_item} )

  return mapping


def 			lowerCamelCase_		(					lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Optional[int]			,							lowerCAmelCase:		Optional[int]=None			,							lowerCAmelCase:		Dict=False )->						Optional[Any]:
  _snake_case      : Union[str, Any]														=		{}
  _snake_case      : Union[str, Any]														=		list(checkpoint.keys() )

  _snake_case      : Optional[int]														=		'model.diffusion_model.'

  # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
  if sum(k.startswith('model_ema' ) for k in keys ) > 1_00 and extract_ema:
    print(F"""Checkpoint {path} has both EMA and non-EMA weights.""" )
    print(
        'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
        ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.' )
    for key in keys:
      if key.startswith('model.diffusion_model' ):
        _snake_case      : Optional[int]														=		'model_ema.' + ''.join(key.split('.' )[1:] )
        _snake_case      : Any														=		checkpoint.pop(lowerCAmelCase )
  else:
    if sum(k.startswith('model_ema' ) for k in keys ) > 1_00:
      print(
          'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
          ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.' )

    for key in keys:
      _snake_case      : str														=		checkpoint.pop(lowerCAmelCase )

  _snake_case      : Union[str, Any]														=		{}

  _snake_case      : Any														=		unet_state_dict['time_embed.0.weight']
  _snake_case      : Optional[Any]														=		unet_state_dict['time_embed.0.bias']
  _snake_case      : str														=		unet_state_dict['time_embed.2.weight']
  _snake_case      : Dict														=		unet_state_dict['time_embed.2.bias']

  if config["class_embed_type"] is None:
    # No parameters to port
    ...
  elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
    _snake_case      : str														=		unet_state_dict['label_emb.0.0.weight']
    _snake_case      : Tuple														=		unet_state_dict['label_emb.0.0.bias']
    _snake_case      : str														=		unet_state_dict['label_emb.0.2.weight']
    _snake_case      : Optional[Any]														=		unet_state_dict['label_emb.0.2.bias']
  else:
    raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}""" )

  _snake_case      : List[str]														=		unet_state_dict['input_blocks.0.0.weight']
  _snake_case      : Tuple														=		unet_state_dict['input_blocks.0.0.bias']

  _snake_case      : List[Any]														=		[v for v in unet_state_dict if v.startswith('input_blocks.0.1' )]
  _snake_case      : Optional[int]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : str														=		{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  assign_to_checkpoint(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Optional[int]														=		unet_state_dict['out.0.weight']
  _snake_case      : Union[str, Any]														=		unet_state_dict['out.0.bias']
  _snake_case      : Any														=		unet_state_dict['out.2.weight']
  _snake_case      : int														=		unet_state_dict['out.2.bias']

  # Retrieves the keys for the input blocks only
  _snake_case      : Union[str, Any]														=		len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'input_blocks' in layer} )
  _snake_case      : List[str]														=		{
      layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase )
  }

  # Retrieves the keys for the middle blocks only
  _snake_case      : Any														=		len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'middle_block' in layer} )
  _snake_case      : Dict														=		{
      layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase )
  }

  # Retrieves the keys for the output blocks only
  _snake_case      : Optional[int]														=		len({'.'.join(layer.split('.' )[:2] ) for layer in unet_state_dict if 'output_blocks' in layer} )
  _snake_case      : Tuple														=		{
      layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
      for layer_id in range(lowerCAmelCase )
  }

  for i in range(1			,							lowerCAmelCase ):
    _snake_case      : Dict														=		(i - 1) // (config['layers_per_block'] + 1)
    _snake_case      : Union[str, Any]														=		(i - 1) % (config['layers_per_block'] + 1)

    _snake_case      : str														=		[
        key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
    ]
    _snake_case      : Optional[int]														=		[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
    _snake_case      : Union[str, Any]														=		[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]

    if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
      _snake_case      : str														=		unet_state_dict.pop(
          F"""input_blocks.{i}.op.weight""" )
      _snake_case      : Optional[int]														=		unet_state_dict.pop(
          F"""input_blocks.{i}.op.bias""" )

    _snake_case      : Optional[Any]														=		renew_resnet_paths(lowerCAmelCase )
    _snake_case      : Union[str, Any]														=		{'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
    assign_to_checkpoint(
        lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

    _snake_case      : List[Any]														=		[key for key in resnets if 'temopral_conv' in key]
    _snake_case      : Union[str, Any]														=		renew_temp_conv_paths(lowerCAmelCase )
    _snake_case      : List[str]														=		{
        'old': F"""input_blocks.{i}.0.temopral_conv""",
        'new': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
    }
    assign_to_checkpoint(
        lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

    if len(lowerCAmelCase ):
      _snake_case      : List[Any]														=		renew_attention_paths(lowerCAmelCase )
      _snake_case      : str														=		{'old': F"""input_blocks.{i}.1""", 'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

    if len(lowerCAmelCase ):
      _snake_case      : Tuple														=		renew_attention_paths(lowerCAmelCase )
      _snake_case      : Optional[int]														=		{
          'old': F"""input_blocks.{i}.2""",
          'new': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Tuple														=		middle_blocks[0]
  _snake_case      : Union[str, Any]														=		[key for key in resnet_a if 'temopral_conv' in key]
  _snake_case      : Optional[Any]														=		middle_blocks[1]
  _snake_case      : List[str]														=		middle_blocks[2]
  _snake_case      : List[str]														=		middle_blocks[3]
  _snake_case      : Tuple														=		[key for key in resnet_a if 'temopral_conv' in key]

  _snake_case      : str														=		renew_resnet_paths(lowerCAmelCase )
  _snake_case      : Dict														=		{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : Tuple														=		renew_temp_conv_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : List[Any]														=		renew_resnet_paths(lowerCAmelCase )
  _snake_case      : List[str]														=		{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : Any														=		renew_temp_conv_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							config=lowerCAmelCase			,							additional_replacements=[meta_path] )

  _snake_case      : List[Any]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : int														=		{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  _snake_case      : Optional[int]														=		renew_attention_paths(lowerCAmelCase )
  _snake_case      : List[Any]														=		{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  assign_to_checkpoint(
      lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

  for i in range(lowerCAmelCase ):
    _snake_case      : Union[str, Any]														=		i // (config['layers_per_block'] + 1)
    _snake_case      : Optional[int]														=		i % (config['layers_per_block'] + 1)
    _snake_case      : Dict														=		[shave_segments(lowerCAmelCase			,							2 ) for name in output_blocks[i]]
    _snake_case      : Optional[Any]														=		{}

    for layer in output_block_layers:
      _snake_case							,    _snake_case      : str														=		layer.split('.' )[0], shave_segments(lowerCAmelCase			,							1 )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(lowerCAmelCase )
      else:
        _snake_case      : Union[str, Any]														=		[layer_name]

    if len(lowerCAmelCase ) > 1:
      _snake_case      : Union[str, Any]														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
      _snake_case      : Optional[Any]														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
      _snake_case      : int														=		[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

      _snake_case      : Tuple														=		renew_resnet_paths(lowerCAmelCase )
      _snake_case      : int														=		renew_resnet_paths(lowerCAmelCase )

      _snake_case      : Optional[int]														=		{'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

      _snake_case      : Union[str, Any]														=		[key for key in resnets if 'temopral_conv' in key]
      _snake_case      : Dict														=		renew_temp_conv_paths(lowerCAmelCase )
      _snake_case      : int														=		{
          'old': F"""output_blocks.{i}.0.temopral_conv""",
          'new': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
      }
      assign_to_checkpoint(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

      _snake_case      : Dict														=		{k: sorted(lowerCAmelCase ) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        _snake_case      : List[Any]														=		list(output_block_list.values() ).index(['conv.bias', 'conv.weight'] )
        _snake_case      : Union[str, Any]														=		unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.weight"""
        ]
        _snake_case      : Dict														=		unet_state_dict[
            F"""output_blocks.{i}.{index}.conv.bias"""
        ]

        # Clear attentions as they have been attributed above.
        if len(lowerCAmelCase ) == 2:
          _snake_case      : Optional[int]														=		[]

      if len(lowerCAmelCase ):
        _snake_case      : Tuple														=		renew_attention_paths(lowerCAmelCase )
        _snake_case      : Tuple														=		{
            'old': F"""output_blocks.{i}.1""",
            'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
        }
        assign_to_checkpoint(
            lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )

      if len(lowerCAmelCase ):
        _snake_case      : List[str]														=		renew_attention_paths(lowerCAmelCase )
        _snake_case      : str														=		{
            'old': F"""output_blocks.{i}.2""",
            'new': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
        }
        assign_to_checkpoint(
            lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							additional_replacements=[meta_path]			,							config=lowerCAmelCase )
    else:
      _snake_case      : List[str]														=		renew_resnet_paths(lowerCAmelCase			,							n_shave_prefix_segments=1 )
      for path in resnet_0_paths:
        _snake_case      : Any														=		'.'.join(['output_blocks', str(lowerCAmelCase ), path['old']] )
        _snake_case      : Optional[int]														=		'.'.join(['up_blocks', str(lowerCAmelCase ), 'resnets', str(lowerCAmelCase ), path['new']] )
        _snake_case      : Any														=		unet_state_dict[old_path]

      _snake_case      : Optional[int]														=		[l for l in output_block_layers if 'temopral_conv' in l]
      for path in temopral_conv_paths:
        _snake_case      : Optional[Any]														=		path.split('temopral_conv.' )[-1]
        _snake_case      : str														=		'.'.join(['output_blocks', str(lowerCAmelCase ), str(lowerCAmelCase ), 'temopral_conv', pruned_path] )
        _snake_case      : Optional[int]														=		'.'.join(['up_blocks', str(lowerCAmelCase ), 'temp_convs', str(lowerCAmelCase ), pruned_path] )
        _snake_case      : Tuple														=		unet_state_dict[old_path]

  return new_checkpoint


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

      parser.add_argument(
          """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
      )
      parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      lowerCAmelCase_        =			parser.parse_args()

      lowerCAmelCase_        =			torch.load(args.checkpoint_path, map_location="""cpu""")
      lowerCAmelCase_        =			UNetaDConditionModel()

      lowerCAmelCase_        =			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

      lowerCAmelCase_        =			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
      lowerCAmelCase_        =			set(converted_ckpt.keys()) - set(unet.state_dict().keys())

      assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

      # load state_dict
      unet.load_state_dict(converted_ckpt)

      unet.save_pretrained(args.dump_path)

      # -- finish converting the unet --




