


'''simple docstring'''







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def       lowercase					(_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       lowercase					(_A ,    _A=0       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[int]       =							[]
       for old_item in old_list:
              _lowerCAmelCase     :				Any       =							old_item.replace('in_layers.0' ,    'norm1'       )
              _lowerCAmelCase     :				Union[str, Any]       =							new_item.replace('in_layers.2' ,    'conv1'       )

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

              _lowerCAmelCase     :				Any       =							new_item.replace('emb_layers.1' ,    'time_emb_proj'       )
              _lowerCAmelCase     :				Optional[Any]       =							new_item.replace('skip_connection' ,    'conv_shortcut'       )

              _lowerCAmelCase     :				Optional[int]       =							shave_segments(_A ,    n_shave_prefix_segments=_A       )

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

       return mapping






def       lowercase					(_A ,    _A=0       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							[]
       for old_item in old_list:
              _lowerCAmelCase     :				Optional[int]       =							old_item

              _lowerCAmelCase     :				List[str]       =							new_item.replace('norm.weight' ,    'group_norm.weight'       )
              _lowerCAmelCase     :				List[str]       =							new_item.replace('norm.bias' ,    'group_norm.bias'       )

              _lowerCAmelCase     :				List[Any]       =							new_item.replace('proj_out.weight' ,    'proj_attn.weight'       )
              _lowerCAmelCase     :				str       =							new_item.replace('proj_out.bias' ,    'proj_attn.bias'       )

              _lowerCAmelCase     :				Tuple       =							shave_segments(_A ,    n_shave_prefix_segments=_A       )

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

       return mapping






def       lowercase					(_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     :				int       =							old_checkpoint[path]
                     _lowerCAmelCase     :				str       =							old_tensor.shape[0] // 3

                     _lowerCAmelCase     :				Optional[int]       =							(-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

                     _lowerCAmelCase     :				int       =							old_tensor.shape[0] // config['num_head_channels'] // 3

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

                     _lowerCAmelCase     :				Any       =							query.reshape(_A       )
                     _lowerCAmelCase     :				Optional[Any]       =							key.reshape(_A       )
                     _lowerCAmelCase     :				Any       =							value.reshape(_A       )

       for path in paths:
              _lowerCAmelCase     :				Any       =							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
              _lowerCAmelCase     :				Optional[int]       =							new_path.replace('middle_block.0' ,    'mid_block.resnets.0'       )
              _lowerCAmelCase     :				Any       =							new_path.replace('middle_block.1' ,    'mid_block.attentions.0'       )
              _lowerCAmelCase     :				int       =							new_path.replace('middle_block.2' ,    'mid_block.resnets.1'       )

              if additional_replacements is not None:
                     for replacement in additional_replacements:
                            _lowerCAmelCase     :				Tuple       =							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:
                     _lowerCAmelCase     :				str       =							old_checkpoint[path['old']][:, :, 0]
              else:
                     _lowerCAmelCase     :				Dict       =							old_checkpoint[path['old']]






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							{}

       _lowerCAmelCase     :				List[str]       =							checkpoint['time_embed.0.weight']
       _lowerCAmelCase     :				Dict       =							checkpoint['time_embed.0.bias']
       _lowerCAmelCase     :				Tuple       =							checkpoint['time_embed.2.weight']
       _lowerCAmelCase     :				Union[str, Any]       =							checkpoint['time_embed.2.bias']

       _lowerCAmelCase     :				int       =							checkpoint['input_blocks.0.0.weight']
       _lowerCAmelCase     :				int       =							checkpoint['input_blocks.0.0.bias']

       _lowerCAmelCase     :				Optional[int]       =							checkpoint['out.0.weight']
       _lowerCAmelCase     :				int       =							checkpoint['out.0.bias']
       _lowerCAmelCase     :				List[Any]       =							checkpoint['out.2.weight']
       _lowerCAmelCase     :				Tuple       =							checkpoint['out.2.bias']

       # Retrieves the keys for the input blocks only
       _lowerCAmelCase     :				Union[str, Any]       =							len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'input_blocks' in layer}       )
       _lowerCAmelCase     :				Optional[int]       =							{
           layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
           for layer_id in range(_A       )
       }

       # Retrieves the keys for the middle blocks only
       _lowerCAmelCase     :				Optional[int]       =							len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'middle_block' in layer}       )
       _lowerCAmelCase     :				Tuple       =							{
           layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
           for layer_id in range(_A       )
       }

       # Retrieves the keys for the output blocks only
       _lowerCAmelCase     :				Optional[Any]       =							len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'output_blocks' in layer}       )
       _lowerCAmelCase     :				Dict       =							{
           layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
           for layer_id in range(_A       )
       }

       for i in range(1 ,    _A       ):
              _lowerCAmelCase     :				Tuple       =							(i - 1) // (config['num_res_blocks'] + 1)
              _lowerCAmelCase     :				Dict       =							(i - 1) % (config['num_res_blocks'] + 1)

              _lowerCAmelCase     :				Optional[Any]       =							[key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
              _lowerCAmelCase     :				Any       =							[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:
                     _lowerCAmelCase     :				Dict       =							checkpoint[
                         f'input_blocks.{i}.0.op.weight'
                     ]
                     _lowerCAmelCase     :				Union[str, Any]       =							checkpoint[
                         f'input_blocks.{i}.0.op.bias'
                     ]
                     continue

              _lowerCAmelCase     :				Any       =							renew_resnet_paths(_A       )
              _lowerCAmelCase     :				List[str]       =							{'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
              _lowerCAmelCase     :				int       =							{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
              assign_to_checkpoint(
                  _A ,    _A ,    _A ,    additional_replacements=[meta_path, resnet_op] ,    config=_A       )

              if len(_A       ):
                     _lowerCAmelCase     :				Union[str, Any]       =							renew_attention_paths(_A       )
                     _lowerCAmelCase     :				Optional[int]       =							{
                         'old': f'input_blocks.{i}.1',
                         'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
                     }
                     _lowerCAmelCase     :				int       =							{
                         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(
                         _A ,    _A ,    _A ,    additional_replacements=[meta_path] ,    attention_paths_to_split=_A ,    config=_A ,    )

       _lowerCAmelCase     :				List[str]       =							middle_blocks[0]
       _lowerCAmelCase     :				Optional[int]       =							middle_blocks[1]
       _lowerCAmelCase     :				Optional[Any]       =							middle_blocks[2]

       _lowerCAmelCase     :				Optional[Any]       =							renew_resnet_paths(_A       )
       assign_to_checkpoint(_A ,    _A ,    _A ,    config=_A       )

       _lowerCAmelCase     :				List[str]       =							renew_resnet_paths(_A       )
       assign_to_checkpoint(_A ,    _A ,    _A ,    config=_A       )

       _lowerCAmelCase     :				str       =							renew_attention_paths(_A       )
       _lowerCAmelCase     :				Union[str, Any]       =							{
           '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(
           _A ,    _A ,    _A ,    attention_paths_to_split=_A ,    config=_A       )

       for i in range(_A       ):
              _lowerCAmelCase     :				Union[str, Any]       =							i // (config['num_res_blocks'] + 1)
              _lowerCAmelCase     :				Optional[int]       =							i % (config['num_res_blocks'] + 1)
              _lowerCAmelCase     :				Optional[int]       =							[shave_segments(_A ,    2       ) for name in output_blocks[i]]
              _lowerCAmelCase     :				List[Any]       =							{}

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

              if len(_A       ) > 1:
                     _lowerCAmelCase     :				Optional[Any]       =							[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
                     _lowerCAmelCase     :				Optional[Any]       =							[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

                     _lowerCAmelCase     :				Union[str, Any]       =							renew_resnet_paths(_A       )
                     _lowerCAmelCase     :				Optional[int]       =							renew_resnet_paths(_A       )

                     _lowerCAmelCase     :				Dict       =							{'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       )

                     if ["conv.weight", "conv.bias"] in output_block_list.values():
                            _lowerCAmelCase     :				List[Any]       =							list(output_block_list.values()       ).index(['conv.weight', 'conv.bias']       )
                            _lowerCAmelCase     :				Optional[Any]       =							checkpoint[
                                f'output_blocks.{i}.{index}.conv.weight'
                            ]
                            _lowerCAmelCase     :				int       =							checkpoint[
                                f'output_blocks.{i}.{index}.conv.bias'
                            ]

                            # Clear attentions as they have been attributed above.
                            if len(_A       ) == 2:
                                   _lowerCAmelCase     :				Tuple       =							[]

                     if len(_A       ):
                            _lowerCAmelCase     :				Tuple       =							renew_attention_paths(_A       )
                            _lowerCAmelCase     :				Optional[Any]       =							{
                                'old': f'output_blocks.{i}.1',
                                'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                            }
                            _lowerCAmelCase     :				Union[str, Any]       =							{
                                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(
                                _A ,    _A ,    _A ,    additional_replacements=[meta_path] ,    attention_paths_to_split=to_split if any('qkv' in key for key in attentions       ) else None ,    config=_A ,    )
              else:
                     _lowerCAmelCase     :				List[str]       =							renew_resnet_paths(_A ,    n_shave_prefix_segments=1       )
                     for path in resnet_0_paths:
                            _lowerCAmelCase     :				List[str]       =							'.'.join(['output_blocks', str(_A       ), path['old']]       )
                            _lowerCAmelCase     :				List[str]       =							'.'.join(['up_blocks', str(_A       ), 'resnets', str(_A       ), path['new']]       )

                            _lowerCAmelCase     :				Dict       =							checkpoint[old_path]

       return new_checkpoint


if __name__ == "__main__":
       lowerCAmelCase     :  str											=    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.""")

       lowerCAmelCase     :  Any											=    parser.parse_args()

       lowerCAmelCase     :  List[Any]											=    torch.load(args.checkpoint_path)

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

       lowerCAmelCase     :  Any											=    convert_ldm_checkpoint(checkpoint, config)

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

       lowerCAmelCase     :  List[Any]											=    UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

       try:
              lowerCAmelCase     :  Optional[int]											=    DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
              lowerCAmelCase     :  Union[str, Any]											=    VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

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





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase     :  int											=    {
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Dict											=    ["""OwlViTFeatureExtractor"""]
       lowerCAmelCase     :  int											=    ["""OwlViTImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    [
           """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """OwlViTModel""",
           """OwlViTPreTrainedModel""",
           """OwlViTTextModel""",
           """OwlViTVisionModel""",
           """OwlViTForObjectDetection""",
       ]

if TYPE_CHECKING:
       from .configuration_owlvit import (
           OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           OwlViTConfig,
           OwlViTOnnxConfig,
           OwlViTTextConfig,
           OwlViTVisionConfig,
       )
       from .processing_owlvit import OwlViTProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_owlvit import OwlViTFeatureExtractor
              from .image_processing_owlvit import OwlViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_owlvit import (
                  OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OwlViTForObjectDetection,
                  OwlViTModel,
                  OwlViTPreTrainedModel,
                  OwlViTTextModel,
                  OwlViTVisionModel,
              )

else:
       import sys

       lowerCAmelCase     :  Union[str, Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

