




'''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 			SCREAMING_SNAKE_CASE_			(							__A    :			Any	,					__A    :			str  )			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				checkpoint

   _SCREAMING_SNAKE_CASE	=				{}

   _SCREAMING_SNAKE_CASE	=				vae_state_dict["encoder.conv_in.weight"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["encoder.conv_in.bias"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["encoder.conv_out.weight"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["encoder.conv_out.bias"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["encoder.norm_out.weight"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["encoder.norm_out.bias"]

   _SCREAMING_SNAKE_CASE	=				vae_state_dict["decoder.conv_in.weight"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["decoder.conv_in.bias"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["decoder.conv_out.weight"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["decoder.conv_out.bias"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["decoder.norm_out.weight"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["decoder.norm_out.bias"]

   _SCREAMING_SNAKE_CASE	=				vae_state_dict["quant_conv.weight"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["quant_conv.bias"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["post_quant_conv.weight"]
   _SCREAMING_SNAKE_CASE	=				vae_state_dict["post_quant_conv.bias"]

   # Retrieves the keys for the encoder down blocks only
   _SCREAMING_SNAKE_CASE	=				len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "encoder.down" in layer}  )
   _SCREAMING_SNAKE_CASE	=				{
       layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(lowercase_  )
   }

   # Retrieves the keys for the decoder up blocks only
   _SCREAMING_SNAKE_CASE	=				len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "decoder.up" in layer}  )
   _SCREAMING_SNAKE_CASE	=				{
       layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(lowercase_  )
   }

   for i in range(lowercase_  ):
      _SCREAMING_SNAKE_CASE	=				[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:
         _SCREAMING_SNAKE_CASE	=				vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.weight"""  )
         _SCREAMING_SNAKE_CASE	=				vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.bias"""  )

      _SCREAMING_SNAKE_CASE	=				renew_vae_resnet_paths(lowercase_  )
      _SCREAMING_SNAKE_CASE	=				{"old": f"""down.{i}.block""", "new": f"""down_blocks.{i}.resnets"""}
      assign_to_checkpoint(lowercase_	,					lowercase_	,					lowercase_	,					additional_replacements=[meta_path]	,					config=lowercase_  )

   _SCREAMING_SNAKE_CASE	=				[key for key in vae_state_dict if "encoder.mid.block" in key]
   _SCREAMING_SNAKE_CASE	=				2
   for i in range(1	,					num_mid_res_blocks + 1  ):
      _SCREAMING_SNAKE_CASE	=				[key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

      _SCREAMING_SNAKE_CASE	=				renew_vae_resnet_paths(lowercase_  )
      _SCREAMING_SNAKE_CASE	=				{"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(lowercase_	,					lowercase_	,					lowercase_	,					additional_replacements=[meta_path]	,					config=lowercase_  )

   _SCREAMING_SNAKE_CASE	=				[key for key in vae_state_dict if "encoder.mid.attn" in key]
   _SCREAMING_SNAKE_CASE	=				renew_vae_attention_paths(lowercase_  )
   _SCREAMING_SNAKE_CASE	=				{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(lowercase_	,					lowercase_	,					lowercase_	,					additional_replacements=[meta_path]	,					config=lowercase_  )
   conv_attn_to_linear(lowercase_  )

   for i in range(lowercase_  ):
      _SCREAMING_SNAKE_CASE	=				num_up_blocks - 1 - i
      _SCREAMING_SNAKE_CASE	=				[
          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:
         _SCREAMING_SNAKE_CASE	=				vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.weight"""
         ]
         _SCREAMING_SNAKE_CASE	=				vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.bias"""
         ]

      _SCREAMING_SNAKE_CASE	=				renew_vae_resnet_paths(lowercase_  )
      _SCREAMING_SNAKE_CASE	=				{"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
      assign_to_checkpoint(lowercase_	,					lowercase_	,					lowercase_	,					additional_replacements=[meta_path]	,					config=lowercase_  )

   _SCREAMING_SNAKE_CASE	=				[key for key in vae_state_dict if "decoder.mid.block" in key]
   _SCREAMING_SNAKE_CASE	=				2
   for i in range(1	,					num_mid_res_blocks + 1  ):
      _SCREAMING_SNAKE_CASE	=				[key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

      _SCREAMING_SNAKE_CASE	=				renew_vae_resnet_paths(lowercase_  )
      _SCREAMING_SNAKE_CASE	=				{"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(lowercase_	,					lowercase_	,					lowercase_	,					additional_replacements=[meta_path]	,					config=lowercase_  )

   _SCREAMING_SNAKE_CASE	=				[key for key in vae_state_dict if "decoder.mid.attn" in key]
   _SCREAMING_SNAKE_CASE	=				renew_vae_attention_paths(lowercase_  )
   _SCREAMING_SNAKE_CASE	=				{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(lowercase_	,					lowercase_	,					lowercase_	,					additional_replacements=[meta_path]	,					config=lowercase_  )
   conv_attn_to_linear(lowercase_  )
   return new_checkpoint
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str	,					)			->			str:
   # Only support V1
   _SCREAMING_SNAKE_CASE	=				requests.get(
       " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"  )
   _SCREAMING_SNAKE_CASE	=				io.BytesIO(r.content  )

   _SCREAMING_SNAKE_CASE	=				OmegaConf.load(lowercase_  )
   _SCREAMING_SNAKE_CASE	=				5_12
   _SCREAMING_SNAKE_CASE	=				"cuda" if torch.cuda.is_available() else "cpu"
   if checkpoint_path.endswith("safetensors"  ):
      from safetensors import safe_open

      _SCREAMING_SNAKE_CASE	=				{}
      with safe_open(lowercase_	,					framework="pt"	,					device="cpu"  ) as f:
         for key in f.keys():
            _SCREAMING_SNAKE_CASE	=				f.get_tensor(lowercase_  )
   else:
      _SCREAMING_SNAKE_CASE	=				torch.load(lowercase_	,					map_location=lowercase_  )["state_dict"]

   # Convert the VAE model.
   _SCREAMING_SNAKE_CASE	=				create_vae_diffusers_config(lowercase_	,					image_size=lowercase_  )
   _SCREAMING_SNAKE_CASE	=				custom_convert_ldm_vae_checkpoint(lowercase_	,					lowercase_  )

   _SCREAMING_SNAKE_CASE	=				AutoencoderKL(**lowercase_  )
   vae.load_state_dict(lowercase_  )
   vae.save_pretrained(lowercase_  )


if __name__ == "__main__":
 lowerCamelCase_			=						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_			=						parser.parse_args()

 vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCamelCase_			=						{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['LayoutLMv2TokenizerFast']

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['LayoutLMv2FeatureExtractor']
 lowerCamelCase_			=						['LayoutLMv2ImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LayoutLMv2ForQuestionAnswering',
     'LayoutLMv2ForSequenceClassification',
     'LayoutLMv2ForTokenClassification',
     'LayoutLMv2Layer',
     'LayoutLMv2Model',
     'LayoutLMv2PreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaLayer,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )
else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

