




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 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     List[Any]			=						checkpoint

      _A							:     Optional[Any]			=						{}

      _A							:     str			=						vae_state_dict["""encoder.conv_in.weight"""]
      _A							:     int			=						vae_state_dict["""encoder.conv_in.bias"""]
      _A							:     Dict			=						vae_state_dict["""encoder.conv_out.weight"""]
      _A							:     Tuple			=						vae_state_dict["""encoder.conv_out.bias"""]
      _A							:     List[Any]			=						vae_state_dict["""encoder.norm_out.weight"""]
      _A							:     Tuple			=						vae_state_dict["""encoder.norm_out.bias"""]

      _A							:     Optional[int]			=						vae_state_dict["""decoder.conv_in.weight"""]
      _A							:     Tuple			=						vae_state_dict["""decoder.conv_in.bias"""]
      _A							:     Union[str, Any]			=						vae_state_dict["""decoder.conv_out.weight"""]
      _A							:     Tuple			=						vae_state_dict["""decoder.conv_out.bias"""]
      _A							:     Tuple			=						vae_state_dict["""decoder.norm_out.weight"""]
      _A							:     List[str]			=						vae_state_dict["""decoder.norm_out.bias"""]

      _A							:     Optional[int]			=						vae_state_dict["""quant_conv.weight"""]
      _A							:     List[Any]			=						vae_state_dict["""quant_conv.bias"""]
      _A							:     Optional[Any]			=						vae_state_dict["""post_quant_conv.weight"""]
      _A							:     int			=						vae_state_dict["""post_quant_conv.bias"""]

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

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

      for i in range(snake_case_     ):
            _A							:     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:
                  _A							:     Any			=						vae_state_dict.pop(
                      f'''encoder.down.{i}.downsample.conv.weight'''     )
                  _A							:     List[str]			=						vae_state_dict.pop(
                      f'''encoder.down.{i}.downsample.conv.bias'''     )

            _A							:     Optional[Any]			=						renew_vae_resnet_paths(snake_case_     )
            _A							:     str			=						{"""old""": f'''down.{i}.block''', """new""": f'''down_blocks.{i}.resnets'''}
            assign_to_checkpoint(snake_case_,snake_case_,snake_case_,additional_replacements=[meta_path],config=snake_case_     )

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

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

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

      for i in range(snake_case_     ):
            _A							:     List[str]			=						num_up_blocks - 1 - i
            _A							:     List[str]			=						[
                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:
                  _A							:     Union[str, Any]			=						vae_state_dict[
                      f'''decoder.up.{block_id}.upsample.conv.weight'''
                  ]
                  _A							:     List[str]			=						vae_state_dict[
                      f'''decoder.up.{block_id}.upsample.conv.bias'''
                  ]

            _A							:     str			=						renew_vae_resnet_paths(snake_case_     )
            _A							:     int			=						{"""old""": f'''up.{block_id}.block''', """new""": f'''up_blocks.{i}.resnets'''}
            assign_to_checkpoint(snake_case_,snake_case_,snake_case_,additional_replacements=[meta_path],config=snake_case_     )

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

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

      _A							:     Union[str, Any]			=						[key for key in vae_state_dict if """decoder.mid.attn""" in key]
      _A							:     Any			=						renew_vae_attention_paths(snake_case_     )
      _A							:     Optional[int]			=						{"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
      assign_to_checkpoint(snake_case_,snake_case_,snake_case_,additional_replacements=[meta_path],config=snake_case_     )
      conv_attn_to_linear(snake_case_     )
      return new_checkpoint







def 				lowerCAmelCase_	(     snake_case_,snake_case_,):
      # Only support V1
      _A							:     Tuple			=						requests.get(
          """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""     )
      _A							:     Tuple			=						io.BytesIO(r.content     )

      _A							:     List[Any]			=						OmegaConf.load(snake_case_     )
      _A							:     Optional[int]			=						512
      _A							:     Dict			=						"""cuda""" if torch.cuda.is_available() else """cpu"""
      if checkpoint_path.endswith("""safetensors"""     ):
            from safetensors import safe_open

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

      # Convert the VAE model.
      _A							:     List[str]			=						create_vae_diffusers_config(snake_case_,image_size=snake_case_     )
      _A							:     Any			=						custom_convert_ldm_vae_checkpoint(snake_case_,snake_case_     )

      _A							:     List[Any]			=						AutoencoderKL(**snake_case_     )
      vae.load_state_dict(snake_case_     )
      vae.save_pretrained(snake_case_     )


if __name__ == "__main__":
  _snake_case										=       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.")

  _snake_case										=       parser.parse_args()

  vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)







from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy







@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							Optional[int]:
           super().__init__(_a				)

           _A							:     Union[str, Any]			=						RobertaEmbeddings(_a				)
           self.init_weights()







@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							str:
           super().__init__(_a				)
           _A							:     Any			=						config.num_labels
           _A							:     Dict			=						config.num_hidden_layers

           _A							:     List[str]			=						DeeRobertaModel(_a				)
           _A							:     int			=						nn.Dropout(config.hidden_dropout_prob				)
           _A							:     int			=						nn.Linear(config.hidden_size		,     self.config.num_labels				)



     @add_start_docstrings_to_model_forward(_a				)
     def 					a__	(  self		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=-1		,     _a=False		,     )						->							Any:
           _A							:     Optional[int]			=						self.num_layers
           try:
                 _A							:     List[str]			=						self.roberta(
                     _a		,     attention_mask=_a		,     token_type_ids=_a		,     position_ids=_a		,     head_mask=_a		,     inputs_embeds=_a		,     )

                 _A							:     List[str]			=						outputs[1]

                 _A							:     List[str]			=						self.dropout(_a				)
                 _A							:     Optional[Any]			=						self.classifier(_a				)
                 _A							:     List[Any]			=						(logits,) + outputs[2:]  # add hidden states and attention if they are here
           except HighwayException as e:
                 _A							:     List[Any]			=						e.message
                 _A							:     Optional[int]			=						e.exit_layer
                 _A							:     Optional[int]			=						outputs[0]

           if not self.training:
                 _A							:     int			=						entropy(_a				)
                 _A							:     int			=						[]
                 _A							:     int			=						[]
           if labels is not None:
                 if self.num_labels == 1:
                       #  We are doing regression
                       _A							:     Union[str, Any]			=						MSELoss()
                       _A							:     Tuple			=						loss_fct(logits.view(-1				)		,     labels.view(-1				)				)
                 else:
                       _A							:     List[Any]			=						CrossEntropyLoss()
                       _A							:     Dict			=						loss_fct(logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)

                 # work with highway exits
                 _A							:     Optional[Any]			=						[]
                 for highway_exit in outputs[-1]:
                       _A							:     Tuple			=						highway_exit[0]
                       if not self.training:
                             highway_logits_all.append(_a				)
                             highway_entropy.append(highway_exit[2]				)
                       if self.num_labels == 1:
                             #  We are doing regression
                             _A							:     List[str]			=						MSELoss()
                             _A							:     Optional[int]			=						loss_fct(highway_logits.view(-1				)		,     labels.view(-1				)				)
                       else:
                             _A							:     List[Any]			=						CrossEntropyLoss()
                             _A							:     Tuple			=						loss_fct(highway_logits.view(-1		,     self.num_labels				)		,     labels.view(-1				)				)
                       highway_losses.append(_a				)

                 if train_highway:
                       _A							:     Dict			=						(sum(highway_losses[:-1]				),) + outputs
                       # exclude the final highway, of course
                 else:
                       _A							:     int			=						(loss,) + outputs
           if not self.training:
                 _A							:     Optional[Any]			=						outputs + ((original_entropy, highway_entropy), exit_layer)
                 if output_layer >= 0:
                       _A							:     Union[str, Any]			=						(
                           (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                       )  # use the highway of the last layer

           return outputs  # (loss), logits, (hidden_states), (attentions), entropy

