
import argparse

import torch

from diffusers import UNetaDConditionModel


def 		_lowerCamelCase       (   _a			,   _a			,   _a			,   _a=None			,   _a=None			,   _a=None  ):
  """simple docstring"""




  assert isinstance(_a			,   _a  ), "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():
      _lowerCamelCase      =						old_checkpoint[path]
      _lowerCamelCase      =						old_tensor.shape[0] // 3

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

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

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

      _lowerCamelCase      =						query.reshape(_a  )
      _lowerCamelCase      =						key.reshape(_a  )
      _lowerCamelCase      =						value.reshape(_a  )

  for path in paths:
    _lowerCamelCase      =						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:
        _lowerCamelCase      =						new_path.replace(replacement['''old''']			,   replacement['''new''']  )

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


def 		_lowerCamelCase       (   _a			,   _a=0  ):
  """simple docstring"""




  _lowerCamelCase      =						[]
  for old_item in old_list:
    _lowerCamelCase      =						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       (   _a			,   _a=1  ):
  """simple docstring"""




  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       (   _a			,   _a=0  ):
  """simple docstring"""




  _lowerCamelCase      =						[]
  for old_item in old_list:
    mapping.append({'''old''': old_item, '''new''': old_item}  )

  return mapping


def 		_lowerCamelCase       (   _a			,   _a=0  ):
  """simple docstring"""




  _lowerCamelCase      =						[]
  for old_item in old_list:
    _lowerCamelCase      =						old_item.replace('''in_layers.0'''			,   '''norm1'''  )
    _lowerCamelCase      =						new_item.replace('''in_layers.2'''			,   '''conv1'''  )

    _lowerCamelCase      =						new_item.replace('''out_layers.0'''			,   '''norm2'''  )
    _lowerCamelCase      =						new_item.replace('''out_layers.3'''			,   '''conv2'''  )

    _lowerCamelCase      =						new_item.replace('''emb_layers.1'''			,   '''time_emb_proj'''  )
    _lowerCamelCase      =						new_item.replace('''skip_connection'''			,   '''conv_shortcut'''  )

    _lowerCamelCase      =						shave_segments(_a			,   n_shave_prefix_segments=_a  )

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

  return mapping


def 		_lowerCamelCase       (   _a			,   _a			,   _a=None			,   _a=False  ):
  """simple docstring"""




  _lowerCamelCase      =						{}
  _lowerCamelCase      =						list(checkpoint.keys()  )

  _lowerCamelCase      =						"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_0_0 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'''  ):
        _lowerCamelCase      =						"model_ema." + "".join(key.split('''.'''  )[1:]  )
        _lowerCamelCase      =						checkpoint.pop(_a  )
  else:
    if sum(k.startswith('''model_ema'''  ) for k in keys  ) > 1_0_0:
      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:
      _lowerCamelCase      =						checkpoint.pop(_a  )

  _lowerCamelCase      =						{}

  _lowerCamelCase      =						unet_state_dict["time_embed.0.weight"]
  _lowerCamelCase      =						unet_state_dict["time_embed.0.bias"]
  _lowerCamelCase      =						unet_state_dict["time_embed.2.weight"]
  _lowerCamelCase      =						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":
    _lowerCamelCase      =						unet_state_dict["label_emb.0.0.weight"]
    _lowerCamelCase      =						unet_state_dict["label_emb.0.0.bias"]
    _lowerCamelCase      =						unet_state_dict["label_emb.0.2.weight"]
    _lowerCamelCase      =						unet_state_dict["label_emb.0.2.bias"]
  else:
    raise NotImplementedError(F'''Not implemented `class_embed_type`: {config['class_embed_type']}'''  )

  _lowerCamelCase      =						unet_state_dict["input_blocks.0.0.weight"]
  _lowerCamelCase      =						unet_state_dict["input_blocks.0.0.bias"]

  _lowerCamelCase      =						[v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''  )]
  _lowerCamelCase      =						renew_attention_paths(_a  )
  _lowerCamelCase      =						{"old": "input_blocks.0.1", "new": "transformer_in"}
  assign_to_checkpoint(_a			,   _a			,   _a			,   additional_replacements=[meta_path]			,   config=_a  )

  _lowerCamelCase      =						unet_state_dict["out.0.weight"]
  _lowerCamelCase      =						unet_state_dict["out.0.bias"]
  _lowerCamelCase      =						unet_state_dict["out.2.weight"]
  _lowerCamelCase      =						unet_state_dict["out.2.bias"]

  # Retrieves the keys for the input blocks only
  _lowerCamelCase      =						len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in unet_state_dict if '''input_blocks''' in layer}  )
  _lowerCamelCase      =						{
      layer_id: [key for key in unet_state_dict if F'''input_blocks.{layer_id}''' in key]
      for layer_id in range(_a  )
  }

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

  # Retrieves the keys for the output blocks only
  _lowerCamelCase      =						len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in unet_state_dict if '''output_blocks''' in layer}  )
  _lowerCamelCase      =						{
      layer_id: [key for key in unet_state_dict if F'''output_blocks.{layer_id}''' in key]
      for layer_id in range(_a  )
  }

  for i in range(1			,   _a  ):
    _lowerCamelCase      =						(i - 1) // (config["layers_per_block"] + 1)
    _lowerCamelCase      =						(i - 1) % (config["layers_per_block"] + 1)

    _lowerCamelCase      =						[
        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
    ]
    _lowerCamelCase      =						[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
    _lowerCamelCase      =						[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:
      _lowerCamelCase      =						unet_state_dict.pop(
          F'''input_blocks.{i}.op.weight'''  )
      _lowerCamelCase      =						unet_state_dict.pop(
          F'''input_blocks.{i}.op.bias'''  )

    _lowerCamelCase      =						renew_resnet_paths(_a  )
    _lowerCamelCase      =						{"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
    assign_to_checkpoint(
        _a			,   _a			,   _a			,   additional_replacements=[meta_path]			,   config=_a  )

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

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

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

  _lowerCamelCase      =						middle_blocks[0]
  _lowerCamelCase      =						[key for key in resnet_a if "temopral_conv" in key]
  _lowerCamelCase      =						middle_blocks[1]
  _lowerCamelCase      =						middle_blocks[2]
  _lowerCamelCase      =						middle_blocks[3]
  _lowerCamelCase      =						[key for key in resnet_a if "temopral_conv" in key]

  _lowerCamelCase      =						renew_resnet_paths(_a  )
  _lowerCamelCase      =						{"old": "middle_block.0", "new": "mid_block.resnets.0"}
  assign_to_checkpoint(
      _a			,   _a			,   _a			,   config=_a			,   additional_replacements=[meta_path]  )

  _lowerCamelCase      =						renew_temp_conv_paths(_a  )
  _lowerCamelCase      =						{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
  assign_to_checkpoint(
      _a			,   _a			,   _a			,   config=_a			,   additional_replacements=[meta_path]  )

  _lowerCamelCase      =						renew_resnet_paths(_a  )
  _lowerCamelCase      =						{"old": "middle_block.3", "new": "mid_block.resnets.1"}
  assign_to_checkpoint(
      _a			,   _a			,   _a			,   config=_a			,   additional_replacements=[meta_path]  )

  _lowerCamelCase      =						renew_temp_conv_paths(_a  )
  _lowerCamelCase      =						{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
  assign_to_checkpoint(
      _a			,   _a			,   _a			,   config=_a			,   additional_replacements=[meta_path]  )

  _lowerCamelCase      =						renew_attention_paths(_a  )
  _lowerCamelCase      =						{"old": "middle_block.1", "new": "mid_block.attentions.0"}
  assign_to_checkpoint(
      _a			,   _a			,   _a			,   additional_replacements=[meta_path]			,   config=_a  )

  _lowerCamelCase      =						renew_attention_paths(_a  )
  _lowerCamelCase      =						{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
  assign_to_checkpoint(
      _a			,   _a			,   _a			,   additional_replacements=[meta_path]			,   config=_a  )

  for i in range(_a  ):
    _lowerCamelCase      =						i // (config["layers_per_block"] + 1)
    _lowerCamelCase      =						i % (config["layers_per_block"] + 1)
    _lowerCamelCase      =						[shave_segments(_a			,   2  ) for name in output_blocks[i]]
    _lowerCamelCase      =						{}

    for layer in output_block_layers:
      _lowerCamelCase      =						layer.split('''.'''  )[0], shave_segments(_a			,   1  )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(_a  )
      else:
        _lowerCamelCase      =						[layer_name]

    if len(_a  ) > 1:
      _lowerCamelCase      =						[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
      _lowerCamelCase      =						[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
      _lowerCamelCase      =						[key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

      _lowerCamelCase      =						renew_resnet_paths(_a  )
      _lowerCamelCase      =						renew_resnet_paths(_a  )

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

      _lowerCamelCase      =						[key for key in resnets if "temopral_conv" in key]
      _lowerCamelCase      =						renew_temp_conv_paths(_a  )
      _lowerCamelCase      =						{
          "old": F'''output_blocks.{i}.0.temopral_conv''',
          "new": F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
      }
      assign_to_checkpoint(
          _a			,   _a			,   _a			,   additional_replacements=[meta_path]			,   config=_a  )

      _lowerCamelCase      =						{k: sorted(_a  ) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        _lowerCamelCase      =						list(output_block_list.values()  ).index(['''conv.bias''', '''conv.weight''']  )
        _lowerCamelCase      =						unet_state_dict[
            F'''output_blocks.{i}.{index}.conv.weight'''
        ]
        _lowerCamelCase      =						unet_state_dict[
            F'''output_blocks.{i}.{index}.conv.bias'''
        ]

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

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

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

      _lowerCamelCase      =						[l for l in output_block_layers if "temopral_conv" in l]
      for path in temopral_conv_paths:
        _lowerCamelCase      =						path.split('''temopral_conv.'''  )[-1]
        _lowerCamelCase      =						".".join(['''output_blocks''', str(_a  ), str(_a  ), '''temopral_conv''', pruned_path]  )
        _lowerCamelCase      =						".".join(['''up_blocks''', str(_a  ), '''temp_convs''', str(_a  ), pruned_path]  )
        _lowerCamelCase      =						unet_state_dict[old_path]

  return new_checkpoint


if __name__ == "__main__":
  _UpperCAmelCase    			=					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.")
  _UpperCAmelCase    			=					parser.parse_args()

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

  _UpperCAmelCase    			=					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__   :						Optional[Any] 							=     {"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						List[Any] 							=     ["""ViltFeatureExtractor"""]
    A__   :						Any 							=     ["""ViltImageProcessor"""]
    A__   :						int 							=     ["""ViltProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__   :						str 							=     [
        """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViltForImageAndTextRetrieval""",
        """ViltForImagesAndTextClassification""",
        """ViltForTokenClassification""",
        """ViltForMaskedLM""",
        """ViltForQuestionAnswering""",
        """ViltLayer""",
        """ViltModel""",
        """ViltPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_vilt import ViltFeatureExtractor
        from .image_processing_vilt import ViltImageProcessor
        from .processing_vilt import ViltProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vilt import (
            VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViltForImageAndTextRetrieval,
            ViltForImagesAndTextClassification,
            ViltForMaskedLM,
            ViltForQuestionAnswering,
            ViltForTokenClassification,
            ViltLayer,
            ViltModel,
            ViltPreTrainedModel,
        )


else:
    import sys

    A__   :						Any 							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure)



