






'''simple docstring'''





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)



def        __snake_case						(__UpperCAmelCase       ,	__UpperCAmelCase    ):





	"""simple docstring"""
	lowerCamelCase_					:  Union[str, Any]						=    checkpoint

	lowerCamelCase_					:  List[str]						=    {}

	lowerCamelCase_					:  Union[str, Any]						=    vae_state_dict['''encoder.conv_in.weight''']
	lowerCamelCase_					:  Optional[Any]						=    vae_state_dict['''encoder.conv_in.bias''']
	lowerCamelCase_					:  List[str]						=    vae_state_dict['''encoder.conv_out.weight''']
	lowerCamelCase_					:  List[str]						=    vae_state_dict['''encoder.conv_out.bias''']
	lowerCamelCase_					:  List[str]						=    vae_state_dict['''encoder.norm_out.weight''']
	lowerCamelCase_					:  Any						=    vae_state_dict['''encoder.norm_out.bias''']

	lowerCamelCase_					:  List[Any]						=    vae_state_dict['''decoder.conv_in.weight''']
	lowerCamelCase_					:  Dict						=    vae_state_dict['''decoder.conv_in.bias''']
	lowerCamelCase_					:  str						=    vae_state_dict['''decoder.conv_out.weight''']
	lowerCamelCase_					:  int						=    vae_state_dict['''decoder.conv_out.bias''']
	lowerCamelCase_					:  int						=    vae_state_dict['''decoder.norm_out.weight''']
	lowerCamelCase_					:  List[Any]						=    vae_state_dict['''decoder.norm_out.bias''']

	lowerCamelCase_					:  Union[str, Any]						=    vae_state_dict['''quant_conv.weight''']
	lowerCamelCase_					:  Tuple						=    vae_state_dict['''quant_conv.bias''']
	lowerCamelCase_					:  Optional[int]						=    vae_state_dict['''post_quant_conv.weight''']
	lowerCamelCase_					:  Union[str, Any]						=    vae_state_dict['''post_quant_conv.bias''']

	# Retrieves the keys for the encoder down blocks only
	lowerCamelCase_					:  Union[str, Any]						=    len({'''.'''.join(layer.split('''.'''    )[:3]    ) for layer in vae_state_dict if '''encoder.down''' in layer}    )
	lowerCamelCase_					:  Any						=    {
	    layer_id: [key for key in vae_state_dict if F"""down.{layer_id}""" in key] for layer_id in range(_lowerCamelCase    )
	}

	# Retrieves the keys for the decoder up blocks only
	lowerCamelCase_					:  str						=    len({'''.'''.join(layer.split('''.'''    )[:3]    ) for layer in vae_state_dict if '''decoder.up''' in layer}    )
	lowerCamelCase_					:  Tuple						=    {
	    layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(_lowerCamelCase    )
	}

	for i in range(_lowerCamelCase    ):
		lowerCamelCase_					:  str						=    [key for key in down_blocks[i] if F"""down.{i}""" in key and F"""down.{i}.downsample""" not in key]

		if F"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
			lowerCamelCase_					:  Tuple						=    vae_state_dict.pop(
			    F"""encoder.down.{i}.downsample.conv.weight"""    )
			lowerCamelCase_					:  int						=    vae_state_dict.pop(
			    F"""encoder.down.{i}.downsample.conv.bias"""    )

		lowerCamelCase_					:  Optional[int]						=    renew_vae_resnet_paths(_lowerCamelCase    )
		lowerCamelCase_					:  str						=    {'''old''': F"""down.{i}.block""", '''new''': F"""down_blocks.{i}.resnets"""}
		assign_to_checkpoint(_lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase       ,	additional_replacements=[meta_path]       ,	config=_lowerCamelCase    )

	lowerCamelCase_					:  List[Any]						=    [key for key in vae_state_dict if '''encoder.mid.block''' in key]
	lowerCamelCase_					:  Optional[int]						=    2
	for i in range(1       ,	num_mid_res_blocks + 1    ):
		lowerCamelCase_					:  Optional[Any]						=    [key for key in mid_resnets if F"""encoder.mid.block_{i}""" in key]

		lowerCamelCase_					:  Optional[Any]						=    renew_vae_resnet_paths(_lowerCamelCase    )
		lowerCamelCase_					:  int						=    {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
		assign_to_checkpoint(_lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase       ,	additional_replacements=[meta_path]       ,	config=_lowerCamelCase    )

	lowerCamelCase_					:  Optional[Any]						=    [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
	lowerCamelCase_					:  int						=    renew_vae_attention_paths(_lowerCamelCase    )
	lowerCamelCase_					:  Optional[int]						=    {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
	assign_to_checkpoint(_lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase       ,	additional_replacements=[meta_path]       ,	config=_lowerCamelCase    )
	conv_attn_to_linear(_lowerCamelCase    )

	for i in range(_lowerCamelCase    ):
		lowerCamelCase_					:  Union[str, Any]						=    num_up_blocks - 1 - i
		lowerCamelCase_					:  Dict						=    [
		    key for key in up_blocks[block_id] if F"""up.{block_id}""" in key and F"""up.{block_id}.upsample""" not in key
		]

		if F"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
			lowerCamelCase_					:  List[Any]						=    vae_state_dict[
			    F"""decoder.up.{block_id}.upsample.conv.weight"""
			]
			lowerCamelCase_					:  Tuple						=    vae_state_dict[
			    F"""decoder.up.{block_id}.upsample.conv.bias"""
			]

		lowerCamelCase_					:  Optional[int]						=    renew_vae_resnet_paths(_lowerCamelCase    )
		lowerCamelCase_					:  Optional[Any]						=    {'''old''': F"""up.{block_id}.block""", '''new''': F"""up_blocks.{i}.resnets"""}
		assign_to_checkpoint(_lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase       ,	additional_replacements=[meta_path]       ,	config=_lowerCamelCase    )

	lowerCamelCase_					:  str						=    [key for key in vae_state_dict if '''decoder.mid.block''' in key]
	lowerCamelCase_					:  Tuple						=    2
	for i in range(1       ,	num_mid_res_blocks + 1    ):
		lowerCamelCase_					:  List[str]						=    [key for key in mid_resnets if F"""decoder.mid.block_{i}""" in key]

		lowerCamelCase_					:  Union[str, Any]						=    renew_vae_resnet_paths(_lowerCamelCase    )
		lowerCamelCase_					:  List[str]						=    {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
		assign_to_checkpoint(_lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase       ,	additional_replacements=[meta_path]       ,	config=_lowerCamelCase    )

	lowerCamelCase_					:  List[Any]						=    [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
	lowerCamelCase_					:  Tuple						=    renew_vae_attention_paths(_lowerCamelCase    )
	lowerCamelCase_					:  Any						=    {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
	assign_to_checkpoint(_lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase       ,	additional_replacements=[meta_path]       ,	config=_lowerCamelCase    )
	conv_attn_to_linear(_lowerCamelCase    )
	return new_checkpoint



def        __snake_case						(__UpperCAmelCase       ,	__UpperCAmelCase       ,	):





	"""simple docstring"""
	# Only support V1
	lowerCamelCase_					:  Any						=    requests.get(
	    ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'''    )
	lowerCamelCase_					:  Optional[int]						=    io.BytesIO(r.content    )

	lowerCamelCase_					:  Any						=    OmegaConf.load(_lowerCamelCase    )
	lowerCamelCase_					:  Optional[int]						=    512
	lowerCamelCase_					:  Dict						=    '''cuda''' if torch.cuda.is_available() else '''cpu'''
	if checkpoint_path.endswith('''safetensors'''    ):
		from safetensors import safe_open

		lowerCamelCase_					:  Union[str, Any]						=    {}
		with safe_open(_lowerCamelCase       ,	framework='''pt'''       ,	device='''cpu'''    ) as f:
			for key in f.keys():
				lowerCamelCase_					:  Dict						=    f.get_tensor(_lowerCamelCase    )
	else:
		lowerCamelCase_					:  Dict						=    torch.load(_lowerCamelCase       ,	map_location=_lowerCamelCase    )['''state_dict''']

	# Convert the VAE model.
	lowerCamelCase_					:  List[str]						=    create_vae_diffusers_config(_lowerCamelCase       ,	image_size=_lowerCamelCase    )
	lowerCamelCase_					:  int						=    custom_convert_ldm_vae_checkpoint(_lowerCamelCase       ,	_lowerCamelCase    )

	lowerCamelCase_					:  Tuple						=    AutoencoderKL(**_lowerCamelCase    )
	vae.load_state_dict(_lowerCamelCase    )
	vae.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
			__lowerCamelCase						:    int     				=  argparse.ArgumentParser()

			parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
			parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")

			__lowerCamelCase						:    List[str]     				=  parser.parse_args()

			vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)













'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase				:      Union[str, Any]						      ={"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MraForMaskedLM",
      "MraForMultipleChoice",
      "MraForQuestionAnswering",
      "MraForSequenceClassification",
      "MraForTokenClassification",
      "MraLayer",
      "MraModel",
      "MraPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mra import (
        MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraLayer,
        MraModel,
        MraPreTrainedModel,
    )
else:
  import sys

  __lowerCAmelCase				:      Dict						      =_LazyModule(__name__, globals()["__file__"], _import_structure)



