'''simple docstring'''
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def 			__lowerCamelCase							(   _UpperCamelCase	:	int				,     _UpperCamelCase	:	int=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							(   _UpperCamelCase	:	Dict				,     _UpperCamelCase	:	List[str]=0       ):


      '''simple docstring'''


      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(SCREAMING_SNAKE_CASE_				,     n_shave_prefix_segments=SCREAMING_SNAKE_CASE_       )

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

      return mapping



def 			__lowerCamelCase							(   _UpperCamelCase	:	Union[str, Any]				,     _UpperCamelCase	:	Union[str, Any]=0       ):


      '''simple docstring'''


      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(SCREAMING_SNAKE_CASE_				,     n_shave_prefix_segments=SCREAMING_SNAKE_CASE_       )

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

      return mapping



def 			__lowerCamelCase							(   _UpperCamelCase	:	Optional[Any]				,     _UpperCamelCase	:	Optional[int]				,     _UpperCamelCase	:	Union[str, Any]				,     _UpperCamelCase	:	int=None				,     _UpperCamelCase	:	str=None				,     _UpperCamelCase	:	int=None       ):


      '''simple docstring'''


      assert isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_       ), "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(SCREAMING_SNAKE_CASE_       )
                  UpperCAmelCase_      =	key.reshape(SCREAMING_SNAKE_CASE_       )
                  UpperCAmelCase_      =	value.reshape(SCREAMING_SNAKE_CASE_       )

      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 			__lowerCamelCase							(   _UpperCamelCase	:	str				,     _UpperCamelCase	:	Tuple       ):


      '''simple docstring'''


      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(SCREAMING_SNAKE_CASE_       )
      }

      # 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(SCREAMING_SNAKE_CASE_       )
      }

      # 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(SCREAMING_SNAKE_CASE_       )
      }

      for i in range(1				,     SCREAMING_SNAKE_CASE_       ):
            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(SCREAMING_SNAKE_CASE_       )
            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(
                SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     additional_replacements=[meta_path, resnet_op]				,     config=SCREAMING_SNAKE_CASE_       )

            if len(SCREAMING_SNAKE_CASE_       ):
                  UpperCAmelCase_      =	renew_attention_paths(SCREAMING_SNAKE_CASE_       )
                  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(
                      SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     additional_replacements=[meta_path]				,     attention_paths_to_split=SCREAMING_SNAKE_CASE_				,     config=SCREAMING_SNAKE_CASE_				,     )

      UpperCAmelCase_      =	middle_blocks[0]
      UpperCAmelCase_      =	middle_blocks[1]
      UpperCAmelCase_      =	middle_blocks[2]

      UpperCAmelCase_      =	renew_resnet_paths(SCREAMING_SNAKE_CASE_       )
      assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     config=SCREAMING_SNAKE_CASE_       )

      UpperCAmelCase_      =	renew_resnet_paths(SCREAMING_SNAKE_CASE_       )
      assign_to_checkpoint(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     config=SCREAMING_SNAKE_CASE_       )

      UpperCAmelCase_      =	renew_attention_paths(SCREAMING_SNAKE_CASE_       )
      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(
          SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     attention_paths_to_split=SCREAMING_SNAKE_CASE_				,     config=SCREAMING_SNAKE_CASE_       )

      for i in range(SCREAMING_SNAKE_CASE_       ):
            UpperCAmelCase_      =	i // (config['''num_res_blocks'''] + 1)
            UpperCAmelCase_      =	i % (config['''num_res_blocks'''] + 1)
            UpperCAmelCase_      =	[shave_segments(SCREAMING_SNAKE_CASE_				,     2       ) for name in output_blocks[i]]
            UpperCAmelCase_      =	{}

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

            if len(SCREAMING_SNAKE_CASE_       ) > 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(SCREAMING_SNAKE_CASE_       )
                  UpperCAmelCase_      =	renew_resnet_paths(SCREAMING_SNAKE_CASE_       )

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

                  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(SCREAMING_SNAKE_CASE_       ) == 2:
                              UpperCAmelCase_      =	[]

                  if len(SCREAMING_SNAKE_CASE_       ):
                        UpperCAmelCase_      =	renew_attention_paths(SCREAMING_SNAKE_CASE_       )
                        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(
                            SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     additional_replacements=[meta_path]				,     attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions       ) else None				,     config=SCREAMING_SNAKE_CASE_				,     )
            else:
                  UpperCAmelCase_      =	renew_resnet_paths(SCREAMING_SNAKE_CASE_				,     n_shave_prefix_segments=1       )
                  for path in resnet_0_paths:
                        UpperCAmelCase_      =	'''.'''.join(['''output_blocks''', str(SCREAMING_SNAKE_CASE_       ), path['''old''']]       )
                        UpperCAmelCase_      =	'''.'''.join(['''up_blocks''', str(SCREAMING_SNAKE_CASE_       ), '''resnets''', str(SCREAMING_SNAKE_CASE_       ), path['''new''']]       )

                        UpperCAmelCase_      =	checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
      lowercase__	:   List[Any]										=       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.")

      lowercase__	:   Union[str, Any]										=       parser.parse_args()

      lowercase__	:   int										=       torch.load(args.checkpoint_path)

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

      lowercase__	:   Tuple										=       convert_ldm_checkpoint(checkpoint, config)

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

      lowercase__	:   Union[str, Any]										=       UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            lowercase__	:   List[str]										=       DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
            lowercase__	:   Dict										=       VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

            lowercase__	:   Union[str, 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""dpr"""



					def __init__(	self     :						Optional[Any]      ,       _lowercase     :						Dict=30_522      ,       _lowercase     :						Tuple=768      ,       _lowercase     :						int=12      ,       _lowercase     :						List[str]=12      ,       _lowercase     :						Union[str, Any]=3_072      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Dict=0.1      ,       _lowercase     :						Any=0.1      ,       _lowercase     :						Union[str, Any]=512      ,       _lowercase     :						Optional[int]=2      ,       _lowercase     :						Union[str, Any]=0.02      ,       _lowercase     :						List[Any]=1E-12      ,       _lowercase     :						List[str]=0      ,       _lowercase     :						Dict="absolute"      ,       _lowercase     :						int = 0      ,       **_lowercase     :						int      ,       )    ->							int:
								super().__init__(pad_token_id=_lowercase      ,       **_lowercase)

								A_									= vocab_size
								A_									= hidden_size
								A_									= num_hidden_layers
								A_									= num_attention_heads
								A_									= hidden_act
								A_									= intermediate_size
								A_									= hidden_dropout_prob
								A_									= attention_probs_dropout_prob
								A_									= max_position_embeddings
								A_									= type_vocab_size
								A_									= initializer_range
								A_									= layer_norm_eps
								A_									= projection_dim
								A_									= position_embedding_type




