






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__											=     {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class        a_		(    lowerCamelCase       ):
  lowercase  	=   """marian"""
  lowercase  	=   ["""past_key_values"""]
  lowercase  	=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=58101    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=12    ,	_SCREAMING_SNAKE_CASE=4096    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=12    ,	_SCREAMING_SNAKE_CASE=4096    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE="gelu"    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=58100    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=58100    ,	_SCREAMING_SNAKE_CASE=0    ,	_SCREAMING_SNAKE_CASE=0    ,	_SCREAMING_SNAKE_CASE=True    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      vocab_size
        UpperCamelCase       						=      decoder_vocab_size or vocab_size
        UpperCamelCase       						=      max_position_embeddings
        UpperCamelCase       						=      d_model
        UpperCamelCase       						=      encoder_ffn_dim
        UpperCamelCase       						=      encoder_layers
        UpperCamelCase       						=      encoder_attention_heads
        UpperCamelCase       						=      decoder_ffn_dim
        UpperCamelCase       						=      decoder_layers
        UpperCamelCase       						=      decoder_attention_heads
        UpperCamelCase       						=      dropout
        UpperCamelCase       						=      attention_dropout
        UpperCamelCase       						=      activation_dropout
        UpperCamelCase       						=      activation_function
        UpperCamelCase       						=      init_std
        UpperCamelCase       						=      encoder_layerdrop
        UpperCamelCase       						=      decoder_layerdrop
        UpperCamelCase       						=      use_cache
        UpperCamelCase       						=      encoder_layers
        UpperCamelCase       						=      scale_embedding  # scale factor will be sqrt(d_model) if True
        UpperCamelCase       						=      share_encoder_decoder_embeddings
        super().__init__(
            pad_token_id=_SCREAMING_SNAKE_CASE    ,	eos_token_id=_SCREAMING_SNAKE_CASE    ,	is_encoder_decoder=_SCREAMING_SNAKE_CASE    ,	decoder_start_token_id=_SCREAMING_SNAKE_CASE    ,	forced_eos_token_id=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)






class        a_		(    lowerCamelCase       ):

  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def 						A__		(   self   )						->   Mapping[str, Mapping[int, str]]:



        """simple docstring"""






        if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase       						=      OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]   )

              if self.use_past:
                    UpperCamelCase       						=      {0: """batch"""}
                    UpperCamelCase       						=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                    UpperCamelCase       						=      {0: """batch""", 1: """decoder_sequence"""}
                    UpperCamelCase       						=      {0: """batch""", 1: """decoder_sequence"""}

              if self.use_past:
                    self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE    ,	direction="""inputs"""   )
        elif self.task == "causal-lm":
              # TODO: figure this case out.
              UpperCamelCase       						=      OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]   )
              if self.use_past:
                    UpperCamelCase						,UpperCamelCase       						=      self.num_layers
                    for i in range(_SCREAMING_SNAKE_CASE   ):
                          UpperCamelCase       						=      {0: """batch""", 2: """past_sequence + sequence"""}
                          UpperCamelCase       						=      {0: """batch""", 2: """past_sequence + sequence"""}
        else:
              UpperCamelCase       						=      OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                      ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                  ]   )

        return common_inputs

  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
  def 						A__		(   self   )						->   Mapping[str, Mapping[int, str]]:



        """simple docstring"""






        if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase       						=      super().outputs
        else:
              UpperCamelCase       						=      super(_SCREAMING_SNAKE_CASE    ,	self   ).outputs
              if self.use_past:
                    UpperCamelCase						,UpperCamelCase       						=      self.num_layers
                    for i in range(_SCREAMING_SNAKE_CASE   ):
                          UpperCamelCase       						=      {0: """batch""", 2: """past_sequence + sequence"""}
                          UpperCamelCase       						=      {0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Mapping[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self._generate_dummy_inputs_for_encoder_and_decoder(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        # Generate decoder inputs
        UpperCamelCase       						=      seq_length if not self.use_past else 1
        UpperCamelCase       						=      self._generate_dummy_inputs_for_encoder_and_decoder(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
        UpperCamelCase       						=      dict(**_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   )

        if self.use_past:
              if not is_torch_available():
                    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
              else:
                    import torch
              UpperCamelCase						,UpperCamelCase       						=      common_inputs["""input_ids"""].shape
              UpperCamelCase       						=      common_inputs["""decoder_input_ids"""].shape[1]
              UpperCamelCase						,UpperCamelCase       						=      self.num_attention_heads
              UpperCamelCase       						=      (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCamelCase       						=      decoder_seq_length + 3
              UpperCamelCase       						=      (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              UpperCamelCase       						=      torch.cat(
                  [common_inputs["""decoder_attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )]    ,	dim=1   )

              UpperCamelCase       						=      []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCamelCase						,UpperCamelCase       						=      self.num_layers
              UpperCamelCase       						=      min(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      max(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ) - min_num_layers
              UpperCamelCase       						=      """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

              for _ in range(_SCREAMING_SNAKE_CASE   ):
                    common_inputs["past_key_values"].append(
                        (
                            torch.zeros(_SCREAMING_SNAKE_CASE   ),
                            torch.zeros(_SCREAMING_SNAKE_CASE   ),
                            torch.zeros(_SCREAMING_SNAKE_CASE   ),
                            torch.zeros(_SCREAMING_SNAKE_CASE   ),
                        )   )
              # TODO: test this.
              UpperCamelCase       						=      encoder_shape if remaining_side_name == """encoder""" else decoder_shape
              for _ in range(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                    common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE   ), torch.zeros(_SCREAMING_SNAKE_CASE   ))   )
        return common_inputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Mapping[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self._generate_dummy_inputs_for_encoder_and_decoder(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        if self.use_past:
              if not is_torch_available():
                    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
              else:
                    import torch
              UpperCamelCase						,UpperCamelCase       						=      common_inputs["""input_ids"""].shape
              # Not using the same length for past_key_values
              UpperCamelCase       						=      seqlen + 2
              UpperCamelCase						,UpperCamelCase       						=      self.num_layers
              UpperCamelCase						,UpperCamelCase       						=      self.num_attention_heads
              UpperCamelCase       						=      (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              UpperCamelCase       						=      common_inputs["""attention_mask"""].dtype
              UpperCamelCase       						=      torch.cat(
                  [common_inputs["""attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	dtype=_SCREAMING_SNAKE_CASE   )]    ,	dim=1   )
              UpperCamelCase       						=      [
                  (torch.zeros(_SCREAMING_SNAKE_CASE   ), torch.zeros(_SCREAMING_SNAKE_CASE   )) for _ in range(_SCREAMING_SNAKE_CASE   )
              ]
        return common_inputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Mapping[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      compute_effective_axis_dimension(
            _SCREAMING_SNAKE_CASE    ,	fixed_dimension=OnnxConfig.default_fixed_batch    ,	num_token_to_add=0   )

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        UpperCamelCase       						=      tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      compute_effective_axis_dimension(
            _SCREAMING_SNAKE_CASE    ,	fixed_dimension=OnnxConfig.default_fixed_sequence    ,	num_token_to_add=_SCREAMING_SNAKE_CASE   )

        # Generate dummy inputs according to compute batch and sequence
        UpperCamelCase       						=      [""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
        UpperCamelCase       						=      dict(tokenizer(_SCREAMING_SNAKE_CASE    ,	return_tensors=_SCREAMING_SNAKE_CASE   )   )
        return common_inputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Mapping[str, Any]:



        """simple docstring"""






        if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase       						=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  _SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	seq_length=_SCREAMING_SNAKE_CASE    ,	is_pair=_SCREAMING_SNAKE_CASE    ,	framework=_SCREAMING_SNAKE_CASE   )

        else:
              UpperCamelCase       						=      self._generate_dummy_inputs_for_causal_lm(
                  _SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	seq_length=_SCREAMING_SNAKE_CASE    ,	is_pair=_SCREAMING_SNAKE_CASE    ,	framework=_SCREAMING_SNAKE_CASE   )

        return common_inputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase       						=      super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
        else:
              UpperCamelCase       						=      super(_SCREAMING_SNAKE_CASE    ,	self   )._flatten_past_key_values_(
                  _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )


  @property
  def 						A__		(   self   )						->   float:



        """simple docstring"""






        return 1e-4








'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=1					)->				Tuple:
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split("""."""					)[n_shave_prefix_segments:]					)
      else:
            return ".".join(path.split("""."""					)[:n_shave_prefix_segments]					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				Dict:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item.replace("""in_layers.0"""	,		"""norm1"""					)
            UpperCamelCase       						=      new_item.replace("""in_layers.2"""	,		"""conv1"""					)

            UpperCamelCase       						=      new_item.replace("""out_layers.0"""	,		"""norm2"""					)
            UpperCamelCase       						=      new_item.replace("""out_layers.3"""	,		"""conv2"""					)

            UpperCamelCase       						=      new_item.replace("""emb_layers.1"""	,		"""time_emb_proj"""					)
            UpperCamelCase       						=      new_item.replace("""skip_connection"""	,		"""conv_shortcut"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

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

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				List[str]:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item

            UpperCamelCase       						=      new_item.replace("""norm.weight"""	,		"""group_norm.weight"""					)
            UpperCamelCase       						=      new_item.replace("""norm.bias"""	,		"""group_norm.bias"""					)

            UpperCamelCase       						=      new_item.replace("""proj_out.weight"""	,		"""proj_attn.weight"""					)
            UpperCamelCase       						=      new_item.replace("""proj_out.bias"""	,		"""proj_attn.bias"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

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

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				str:
      assert isinstance(__UpperCamelCase	,		__UpperCamelCase					), "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():
                  UpperCamelCase       						=      old_checkpoint[path]
                  UpperCamelCase       						=      old_tensor.shape[0] // 3

                  UpperCamelCase       						=      (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

                  UpperCamelCase       						=      old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

                  UpperCamelCase       						=      query.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      key.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      value.reshape(__UpperCamelCase					)

      for path in paths:
            UpperCamelCase       						=      path["""new"""]

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

            # Global renaming happens here
            UpperCamelCase       						=      new_path.replace("""middle_block.0"""	,		"""mid_block.resnets.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.1"""	,		"""mid_block.attentions.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.2"""	,		"""mid_block.resnets.1"""					)

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

        # proj_attn.weight has to be converted from conv 1D to linear
            if "proj_attn.weight" in new_path:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]][:, :, 0]
            else:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]]







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      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"""]

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

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

      # Retrieves the keys for the input blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """input_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the middle blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """middle_block""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the output blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """output_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      for i in range(1	,		__UpperCamelCase					):
            UpperCamelCase       						=      (i - 1) // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      (i - 1) % (config["""num_res_blocks"""] + 1)

            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

            if F"input_blocks.{i}.0.op.weight" in checkpoint:
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.weight"
                  ]
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.bias"
                  ]
                  continue

            UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
            UpperCamelCase       						=      {"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
            UpperCamelCase       						=      {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
            assign_to_checkpoint(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path, resnet_op]	,		config=__UpperCamelCase					)

            if len(__UpperCamelCase					):
                  UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      {
                      """old""": F"input_blocks.{i}.1",
                      """new""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
                  }
                  UpperCamelCase       						=      {
                      F"input_blocks.{i}.1.qkv.bias": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                      },
                      F"input_blocks.{i}.1.qkv.weight": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                      },
                  }
                  assign_to_checkpoint(
                      __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase	,		)

      UpperCamelCase       						=      middle_blocks[0]
      UpperCamelCase       						=      middle_blocks[1]
      UpperCamelCase       						=      middle_blocks[2]

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
      UpperCamelCase       						=      {
          """middle_block.1.qkv.bias""": {
              """key""": """mid_block.attentions.0.key.bias""",
              """query""": """mid_block.attentions.0.query.bias""",
              """value""": """mid_block.attentions.0.value.bias""",
          },
          """middle_block.1.qkv.weight""": {
              """key""": """mid_block.attentions.0.key.weight""",
              """query""": """mid_block.attentions.0.query.weight""",
              """value""": """mid_block.attentions.0.value.weight""",
          },
      }
      assign_to_checkpoint(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase					)

      for i in range(__UpperCamelCase					):
            UpperCamelCase       						=      i // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      i % (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      [shave_segments(__UpperCamelCase	,		2					) for name in output_blocks[i]]
            UpperCamelCase       						=      {}

            for layer in output_block_layers:
                  UpperCamelCase						,UpperCamelCase       						=      layer.split("""."""					)[0], shave_segments(__UpperCamelCase	,		1					)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(__UpperCamelCase					)
                  else:
                        UpperCamelCase       						=      [layer_name]

            if len(__UpperCamelCase					) > 1:
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)

                  UpperCamelCase       						=      {"""old""": F"output_blocks.{i}.0", """new""": F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
                  assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		config=__UpperCamelCase					)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        UpperCamelCase       						=      list(output_block_list.values()					).index(["""conv.weight""", """conv.bias"""]					)
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.weight"
                        ]
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.bias"
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(__UpperCamelCase					) == 2:
                              UpperCamelCase       						=      []

                  if len(__UpperCamelCase					):
                        UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                        UpperCamelCase       						=      {
                            """old""": F"output_blocks.{i}.1",
                            """new""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                        }
                        UpperCamelCase       						=      {
                            F"output_blocks.{i}.1.qkv.bias": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                            },
                            F"output_blocks.{i}.1.qkv.weight": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                            },
                        }
                        assign_to_checkpoint(
                            __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions					) else None	,		config=__UpperCamelCase	,		)
            else:
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase	,		n_shave_prefix_segments=1					)
                  for path in resnet_0_paths:
                        UpperCamelCase       						=      """.""".join(["""output_blocks""", str(__UpperCamelCase					), path["""old"""]]					)
                        UpperCamelCase       						=      """.""".join(["""up_blocks""", str(__UpperCamelCase					), """resnets""", str(__UpperCamelCase					), path["""new"""]]					)

                        UpperCamelCase       						=      checkpoint[old_path]

      return new_checkpoint


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

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )

     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The config json file corresponding to the architecture.',
     )

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          SCREAMING_SNAKE_CASE__											=     json.loads(f.read())

     SCREAMING_SNAKE_CASE__											=     convert_ldm_checkpoint(checkpoint, config)

     if "ldm" in config:
          del config["ldm"]

     SCREAMING_SNAKE_CASE__											=     UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          SCREAMING_SNAKE_CASE__											=     DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
          SCREAMING_SNAKE_CASE__											=     VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

          SCREAMING_SNAKE_CASE__											=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)
