

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]=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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		List[str]=0       ):





				"""simple docstring"""


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

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

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

								SCREAMING_SNAKE_CASE_									=						shave_segments(_SCREAMING_SNAKE_CASE ,   n_shave_prefix_segments=_SCREAMING_SNAKE_CASE       )

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

				return mapping


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Tuple=0       ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						[]
				for old_item in old_list:
								SCREAMING_SNAKE_CASE_									=						old_item

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

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

								SCREAMING_SNAKE_CASE_									=						shave_segments(_SCREAMING_SNAKE_CASE ,   n_shave_prefix_segments=_SCREAMING_SNAKE_CASE       )

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

				return mapping


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]=None ,   _SCREAMING_SNAKE_CASE					:		Any=None ,   _SCREAMING_SNAKE_CASE					:		Dict=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():
												SCREAMING_SNAKE_CASE_									=						old_checkpoint[path]
												SCREAMING_SNAKE_CASE_									=						old_tensor.shape[0] // 3

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

												SCREAMING_SNAKE_CASE_									=						old_tensor.shape[0] // config['num_head_channels'] // 3

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

												SCREAMING_SNAKE_CASE_									=						query.reshape(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE_									=						key.reshape(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE_									=						value.reshape(_SCREAMING_SNAKE_CASE       )

				for path in paths:
								SCREAMING_SNAKE_CASE_									=						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
								SCREAMING_SNAKE_CASE_									=						new_path.replace('middle_block.0' ,   'mid_block.resnets.0'       )
								SCREAMING_SNAKE_CASE_									=						new_path.replace('middle_block.1' ,   'mid_block.attentions.0'       )
								SCREAMING_SNAKE_CASE_									=						new_path.replace('middle_block.2' ,   'mid_block.resnets.1'       )

								if additional_replacements is not None:
												for replacement in additional_replacements:
																SCREAMING_SNAKE_CASE_									=						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:
												SCREAMING_SNAKE_CASE_									=						old_checkpoint[path['old']][:, :, 0]
								else:
												SCREAMING_SNAKE_CASE_									=						old_checkpoint[path['old']]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		int       ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_									=						{}

				SCREAMING_SNAKE_CASE_									=						checkpoint['time_embed.0.weight']
				SCREAMING_SNAKE_CASE_									=						checkpoint['time_embed.0.bias']
				SCREAMING_SNAKE_CASE_									=						checkpoint['time_embed.2.weight']
				SCREAMING_SNAKE_CASE_									=						checkpoint['time_embed.2.bias']

				SCREAMING_SNAKE_CASE_									=						checkpoint['input_blocks.0.0.weight']
				SCREAMING_SNAKE_CASE_									=						checkpoint['input_blocks.0.0.bias']

				SCREAMING_SNAKE_CASE_									=						checkpoint['out.0.weight']
				SCREAMING_SNAKE_CASE_									=						checkpoint['out.0.bias']
				SCREAMING_SNAKE_CASE_									=						checkpoint['out.2.weight']
				SCREAMING_SNAKE_CASE_									=						checkpoint['out.2.bias']

				# Retrieves the keys for the input blocks only
				SCREAMING_SNAKE_CASE_									=						len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'input_blocks' in layer}       )
				SCREAMING_SNAKE_CASE_									=						{
				    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
				SCREAMING_SNAKE_CASE_									=						len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'middle_block' in layer}       )
				SCREAMING_SNAKE_CASE_									=						{
				    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
				SCREAMING_SNAKE_CASE_									=						len({'.'.join(layer.split('.'       )[:2]       ) for layer in checkpoint if 'output_blocks' in layer}       )
				SCREAMING_SNAKE_CASE_									=						{
				    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       ):
								SCREAMING_SNAKE_CASE_									=						(i - 1) // (config['num_res_blocks'] + 1)
								SCREAMING_SNAKE_CASE_									=						(i - 1) % (config['num_res_blocks'] + 1)

								SCREAMING_SNAKE_CASE_									=						[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
								SCREAMING_SNAKE_CASE_									=						[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:
												SCREAMING_SNAKE_CASE_									=						checkpoint[
												    f"""input_blocks.{i}.0.op.weight"""
												]
												SCREAMING_SNAKE_CASE_									=						checkpoint[
												    f"""input_blocks.{i}.0.op.bias"""
												]
												continue

								SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE_									=						{'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
								SCREAMING_SNAKE_CASE_									=						{'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       ):
												SCREAMING_SNAKE_CASE_									=						renew_attention_paths(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE_									=						{
												    'old': f"""input_blocks.{i}.1""",
												    'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
												}
												SCREAMING_SNAKE_CASE_									=						{
												    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 ,   )

				SCREAMING_SNAKE_CASE_									=						middle_blocks[0]
				SCREAMING_SNAKE_CASE_									=						middle_blocks[1]
				SCREAMING_SNAKE_CASE_									=						middle_blocks[2]

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

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

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

								for layer in output_block_layers:
												SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						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:
																SCREAMING_SNAKE_CASE_									=						[layer_name]

								if len(_SCREAMING_SNAKE_CASE       ) > 1:
												SCREAMING_SNAKE_CASE_									=						[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
												SCREAMING_SNAKE_CASE_									=						[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

												SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE       )

												SCREAMING_SNAKE_CASE_									=						{'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():
																SCREAMING_SNAKE_CASE_									=						list(output_block_list.values()       ).index(['conv.weight', 'conv.bias']       )
																SCREAMING_SNAKE_CASE_									=						checkpoint[
																    f"""output_blocks.{i}.{index}.conv.weight"""
																]
																SCREAMING_SNAKE_CASE_									=						checkpoint[
																    f"""output_blocks.{i}.{index}.conv.bias"""
																]

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

												if len(_SCREAMING_SNAKE_CASE       ):
																SCREAMING_SNAKE_CASE_									=						renew_attention_paths(_SCREAMING_SNAKE_CASE       )
																SCREAMING_SNAKE_CASE_									=						{
																    'old': f"""output_blocks.{i}.1""",
																    'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																}
																SCREAMING_SNAKE_CASE_									=						{
																    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:
												SCREAMING_SNAKE_CASE_									=						renew_resnet_paths(_SCREAMING_SNAKE_CASE ,   n_shave_prefix_segments=1       )
												for path in resnet_0_paths:
																SCREAMING_SNAKE_CASE_									=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE       ), path['old']]       )
																SCREAMING_SNAKE_CASE_									=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE       ), 'resnets', str(_SCREAMING_SNAKE_CASE       ), path['new']]       )

																SCREAMING_SNAKE_CASE_									=						checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
							UpperCamelCase__  :      Union[str, 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.")

							UpperCamelCase__  :      List[Any]			=    parser.parse_args()

							UpperCamelCase__  :      Tuple			=    torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														UpperCamelCase__  :      Optional[Any]			=    json.loads(f.read())

							UpperCamelCase__  :      List[str]			=    convert_ldm_checkpoint(checkpoint, config)

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

							UpperCamelCase__  :      Tuple			=    UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

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

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__  :      str			=    logging.get_logger(__name__)

UpperCamelCase__  :      Optional[int]			=    {
    "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       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Optional[int]						 =  'dpr'

  def __init__(					self						,		_A=30522						,		_A=768						,		_A=12						,		_A=12						,		_A=3072						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=512						,		_A=2						,		_A=0.0_2						,		_A=1E-12						,		_A=0						,		_A="absolute"						,		_A = 0						,		**_A						,		):
      super().__init__(pad_token_id=_A						,		**_A)

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


