




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							:  Dict    =		checkpoint

      _A							:  Optional[Any]    =		{}

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

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

      _A							:  Any    =		vae_state_dict["""quant_conv.weight"""]
      _A							:  str    =		vae_state_dict["""quant_conv.bias"""]
      _A							:  List[Any]    =		vae_state_dict["""post_quant_conv.weight"""]
      _A							:  Any    =		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							:  Union[str, 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							:  List[Any]    =		len({""".""".join(layer.split("""."""  )[:3]  ) for layer in vae_state_dict if """decoder.up""" in layer}  )
      _A							:  Tuple    =		{
          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							:  Optional[int]    =		[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							:  Dict    =		vae_state_dict.pop(
                      f'''encoder.down.{i}.downsample.conv.weight'''  )
                  _A							:  int    =		vae_state_dict.pop(
                      f'''encoder.down.{i}.downsample.conv.bias'''  )

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

            _A							:  Any    =		renew_vae_resnet_paths(snake_case_  )
            _A							:  Tuple    =		{"""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							:  List[Any]    =		[key for key in vae_state_dict if """encoder.mid.attn""" in key]
      _A							:  List[str]    =		renew_vae_attention_paths(snake_case_  )
      _A							:  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							:  Dict    =		num_up_blocks - 1 - i
            _A							:  Any    =		[
                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							:  Tuple    =		vae_state_dict[
                      f'''decoder.up.{block_id}.upsample.conv.weight'''
                  ]
                  _A							:  Union[str, Any]    =		vae_state_dict[
                      f'''decoder.up.{block_id}.upsample.conv.bias'''
                  ]

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

            _A							:  List[Any]    =		renew_vae_resnet_paths(snake_case_  )
            _A							:  Any    =		{"""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 """decoder.mid.attn""" in key]
      _A							:  List[str]    =		renew_vae_attention_paths(snake_case_  )
      _A							:  Union[str, Any]    =		{"""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							:  str    =		requests.get(
          """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""  )
      _A							:  Any    =		io.BytesIO(r.content  )

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

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

      # Convert the VAE model.
      _A							:  int    =		create_vae_diffusers_config(snake_case_,image_size=snake_case_  )
      _A							:  Optional[int]    =		custom_convert_ldm_vae_checkpoint(snake_case_,snake_case_  )

      _A							:  List[str]    =		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)







import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
  from transformers.models.rag.configuration_rag import RagConfig
  from transformers.models.rag.tokenization_rag import RagTokenizer







@require_faiss
@require_torch
class 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							int:
           _A							:  int    =		tempfile.mkdtemp()
           _A							:  Union[str, Any]    =		8

           # DPR tok
           _A							:  List[str]    =		[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           _A							:  List[str]    =		os.path.join(self.tmpdirname	,  """dpr_tokenizer"""       )
           os.makedirs(_a	,  exist_ok=_a       )
           _A							:  str    =		os.path.join(_a	,  DPR_VOCAB_FILES_NAMES["""vocab_file"""]       )
           with open(self.vocab_file	,  """w"""	,  encoding="""utf-8"""       ) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )

           # BART tok
           _A							:  Dict    =		[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           _A							:  Optional[Any]    =		dict(zip(_a	,  range(len(_a       )       )       )       )
           _A							:  Tuple    =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           _A							:  Dict    =		{"""unk_token""": """<unk>"""}

           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )
           os.makedirs(_a	,  exist_ok=_a       )
           _A							:  str    =		os.path.join(_a	,  BART_VOCAB_FILES_NAMES["""vocab_file"""]       )
           _A							:  List[Any]    =		os.path.join(_a	,  BART_VOCAB_FILES_NAMES["""merges_file"""]       )
           with open(self.vocab_file	,  """w"""	,  encoding="""utf-8"""       ) as fp:
                 fp.write(json.dumps(_a       ) + """\n"""       )
           with open(self.merges_file	,  """w"""	,  encoding="""utf-8"""       ) as fp:
                 fp.write("""\n""".join(_a       )       )

     def       a__			(						self       )		->							DPRQuestionEncoderTokenizer:
           return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """dpr_tokenizer"""       )       )

     def       a__			(						self       )		->							BartTokenizer:
           return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )       )

     def       a__			(						self       )		->							Optional[Any]:
           shutil.rmtree(self.tmpdirname       )

     @require_tokenizers
     def       a__			(						self       )		->							str:
           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """rag_tokenizer"""       )
           _A							:  int    =		RagConfig(question_encoder=DPRConfig().to_dict()	,  generator=BartConfig().to_dict()       )
           _A							:  Any    =		RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,  generator=self.get_bart_tokenizer()       )
           rag_config.save_pretrained(_a       )
           rag_tokenizer.save_pretrained(_a       )
           _A							:  Optional[Any]    =		RagTokenizer.from_pretrained(_a	,  config=_a       )
           self.assertIsInstance(new_rag_tokenizer.question_encoder	,  _a       )
           self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()	,  rag_tokenizer.question_encoder.get_vocab()       )
           self.assertIsInstance(new_rag_tokenizer.generator	,  _a       )
           self.assertEqual(new_rag_tokenizer.generator.get_vocab()	,  rag_tokenizer.generator.get_vocab()       )

     @slow
     def       a__			(						self       )		->							str:
           _A							:  Union[str, Any]    =		RagTokenizer.from_pretrained("""facebook/rag-token-nq"""       )
           _A							:  Tuple    =		[
               """who got the first nobel prize in physics""",
               """when is the next deadpool movie being released""",
               """which mode is used for short wave broadcast service""",
               """who is the owner of reading football club""",
               """when is the next scandal episode coming out""",
               """when is the last time the philadelphia won the superbowl""",
               """what is the most current adobe flash player version""",
               """how many episodes are there in dragon ball z""",
               """what is the first step in the evolution of the eye""",
               """where is gall bladder situated in human body""",
               """what is the main mineral in lithium batteries""",
               """who is the president of usa right now""",
               """where do the greasers live in the outsiders""",
               """panda is a national animal of which country""",
               """what is the name of manchester united stadium""",
           ]
           _A							:  Tuple    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )



     @slow
     def       a__			(						self       )		->							Dict:
           _A							:  Dict    =		RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""       )
           _A							:  str    =		[
               """who got the first nobel prize in physics""",
               """when is the next deadpool movie being released""",
               """which mode is used for short wave broadcast service""",
               """who is the owner of reading football club""",
               """when is the next scandal episode coming out""",
               """when is the last time the philadelphia won the superbowl""",
               """what is the most current adobe flash player version""",
               """how many episodes are there in dragon ball z""",
               """what is the first step in the evolution of the eye""",
               """where is gall bladder situated in human body""",
               """what is the main mineral in lithium batteries""",
               """who is the president of usa right now""",
               """where do the greasers live in the outsiders""",
               """panda is a national animal of which country""",
               """what is the name of manchester united stadium""",
           ]
           _A							:  Optional[Any]    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )

