'''simple docstring'''





import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						List[Any]          =  GPTSanJapaneseTokenizer
    a__       :						Optional[Any]          =  False
    a__       :						List[str]          =  {"""do_clean_text""": False, """add_prefix_space""": False}



    def 					_lowercase						(self    :    Union[str, Any] ):
         super().setUp()

         # fmt: off
         UpperCAmelCase_           =   ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
         # fmt: on
         UpperCAmelCase_           =   {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
         UpperCAmelCase_           =   {"unk_token": "<unk>"}

         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			VOCAB_FILES_NAMES["vocab_file"] )
         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			VOCAB_FILES_NAMES["emoji_file"] )
         with open(self.vocab_file   ,			"w"   ,			encoding="utf-8" ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
         with open(self.emoji_file   ,			"w" ) as emoji_writer:
              emoji_writer.write(json.dumps(__a ) )



    def 					_lowercase						(self    :    List[Any]   ,			**__a    :    str ):
         kwargs.update(self.special_tokens_map )
         return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname   ,			**__a )



    def 					_lowercase						(self    :    str   ,			__a    :    Tuple ):
         UpperCAmelCase_           =   "こんにちは、世界。 \nこんばんは、㔺界。😀"
         UpperCAmelCase_           =   "こんにちは、世界。 \nこんばんは、世界。😀"
         return input_text, output_text



    def 					_lowercase						(self    :    str   ,			__a    :    List[Any] ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.get_input_output_texts(__a )
         UpperCAmelCase_           =   tokenizer.encode(__a   ,			add_special_tokens=__a )
         UpperCAmelCase_           =   tokenizer.decode(__a   ,			clean_up_tokenization_spaces=__a )
         return text, ids



    def 					_lowercase						(self    :    str ):
         pass  # TODO add if relevant



    def 					_lowercase						(self    :    str ):
         pass  # TODO add if relevant



    def 					_lowercase						(self    :    List[Any] ):
         pass  # TODO add if relevant



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self.get_tokenizer()

         # Testing tokenization
         UpperCAmelCase_           =   "こんにちは、世界。　こんばんは、㔺界。"
         UpperCAmelCase_           =   ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
         UpperCAmelCase_           =   tokenizer.tokenize(__a )
         self.assertListEqual(__a   ,			__a )

         # Testing conversion to ids without special tokens
         UpperCAmelCase_           =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
         UpperCAmelCase_           =   tokenizer.convert_tokens_to_ids(__a )
         self.assertListEqual(__a   ,			__a )

         # Testing conversion to ids with special tokens
         UpperCAmelCase_           =   tokens + [tokenizer.unk_token]
         UpperCAmelCase_           =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
         UpperCAmelCase_           =   tokenizer.convert_tokens_to_ids(__a )
         self.assertListEqual(__a   ,			__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   self.get_tokenizer()

         # Testing tokenization
         UpperCAmelCase_           =   "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
         UpperCAmelCase_           =   "こんにちは、、、、世界。こんばんは、、、、世界。"
         UpperCAmelCase_           =   tokenizer.encode(__a )
         UpperCAmelCase_           =   tokenizer.decode(__a )
         self.assertEqual(__a   ,			__a )



    @slow
    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )

         # Testing tokenization
         UpperCAmelCase_           =   "こんにちは、世界。"
         UpperCAmelCase_           =   "こんばんは、㔺界。😀"
         UpperCAmelCase_           =   "こんにちは、世界。こんばんは、世界。😀"
         UpperCAmelCase_           =   tokenizer.encode(prefix_text + input_text )
         UpperCAmelCase_           =   tokenizer.encode(""   ,			prefix_text=prefix_text + input_text )
         UpperCAmelCase_           =   tokenizer.encode(__a   ,			prefix_text=__a )
         UpperCAmelCase_           =   tokenizer.decode(__a )
         UpperCAmelCase_           =   tokenizer.decode(__a )
         UpperCAmelCase_           =   tokenizer.decode(__a )
         self.assertEqual(__a   ,			__a )
         self.assertEqual(__a   ,			__a )
         self.assertEqual(__a   ,			__a )



    @slow
    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )

         # Testing tokenization
         UpperCAmelCase_           =   "こんにちは、世界。"
         UpperCAmelCase_           =   "こんばんは、㔺界。😀"

         UpperCAmelCase_           =   len(tokenizer.encode(__a ) ) - 2
         UpperCAmelCase_           =   len(tokenizer.encode(__a ) ) - 2

         UpperCAmelCase_           =   [1] + [0] * (len_prefix + len_text + 1)
         UpperCAmelCase_           =   [1] * (len_prefix + len_text + 1) + [0]
         UpperCAmelCase_           =   [1] + [1] * (len_prefix) + [0] * (len_text + 1)

         UpperCAmelCase_           =   tokenizer(prefix_text + input_text ).token_type_ids
         UpperCAmelCase_           =   tokenizer(""   ,			prefix_text=prefix_text + input_text ).token_type_ids
         UpperCAmelCase_           =   tokenizer(__a   ,			prefix_text=__a ).token_type_ids
         self.assertListEqual(__a   ,			__a )
         self.assertListEqual(__a   ,			__a )
         self.assertListEqual(__a   ,			__a )



    @slow
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )

         UpperCAmelCase_           =   tokenizer.encode("あンいワ" )
         UpperCAmelCase_           =   tokenizer.encode(""   ,			prefix_text="あンいワ" )
         UpperCAmelCase_           =   tokenizer.encode("いワ"   ,			prefix_text="あン" )

         self.assertEqual(tokenizer.decode(__a )   ,			tokenizer.decode(__a ) )
         self.assertEqual(tokenizer.decode(__a )   ,			tokenizer.decode(__a ) )
         self.assertNotEqual(__a   ,			__a )
         self.assertNotEqual(__a   ,			__a )
         self.assertEqual(x_token_a[1]   ,			x_token_a[-1] )  # SEG token
         self.assertEqual(x_token_a[1]   ,			x_token_a[3] )  # SEG token



    @slow
    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese" )

         UpperCAmelCase_           =   [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
         UpperCAmelCase_           =   tokenizer(__a   ,			padding=__a )
         UpperCAmelCase_           =   tokenizer.batch_encode_plus(__a   ,			padding=__a )

         # fmt: off
         UpperCAmelCase_           =   [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
         UpperCAmelCase_           =   [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
         UpperCAmelCase_           =   [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
         # fmt: on
         self.assertListEqual(x_token.input_ids   ,			__a )
         self.assertListEqual(x_token.token_type_ids   ,			__a )
         self.assertListEqual(x_token.attention_mask   ,			__a )
         self.assertListEqual(x_token_a.input_ids   ,			__a )
         self.assertListEqual(x_token_a.token_type_ids   ,			__a )
         self.assertListEqual(x_token_a.attention_mask   ,			__a )



    def 					_lowercase						(self    :    List[Any] ):
         # Intentionally convert some words to accommodate character fluctuations unique to Japanese
         pass






    def 					_lowercase						(self    :    List[str] ):
         # tokenizer has no padding token
         pass






"""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 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					str			     =    checkpoint

   UpperCAmelCase_					:					int			     =    {}

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

   UpperCAmelCase_					:					Any			     =    vae_state_dict["decoder.conv_in.weight"]
   UpperCAmelCase_					:					int			     =    vae_state_dict["decoder.conv_in.bias"]
   UpperCAmelCase_					:					Any			     =    vae_state_dict["decoder.conv_out.weight"]
   UpperCAmelCase_					:					Tuple			     =    vae_state_dict["decoder.conv_out.bias"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["decoder.norm_out.weight"]
   UpperCAmelCase_					:					Tuple			     =    vae_state_dict["decoder.norm_out.bias"]

   UpperCAmelCase_					:					str			     =    vae_state_dict["quant_conv.weight"]
   UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict["quant_conv.bias"]
   UpperCAmelCase_					:					List[str]			     =    vae_state_dict["post_quant_conv.weight"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["post_quant_conv.bias"]

   # Retrieves the keys for the encoder down blocks only
   UpperCAmelCase_					:					Optional[Any]			     =    len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "encoder.down" in layer}  )
   UpperCAmelCase_					:					Optional[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
   UpperCAmelCase_					:					Dict			     =    len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "decoder.up" in layer}  )
   UpperCAmelCase_					:					Optional[int]			     =    {
       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  ):
      UpperCAmelCase_					:					Any			     =    [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:
         UpperCAmelCase_					:					Dict			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.weight"""  )
         UpperCAmelCase_					:					Dict			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.bias"""  )

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

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

      UpperCAmelCase_					:					List[Any]			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Optional[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  )

   UpperCAmelCase_					:					Optional[int]			     =    [key for key in vae_state_dict if "encoder.mid.attn" in key]
   UpperCAmelCase_					:					Union[str, Any]			     =    renew_vae_attention_paths(__lowerCamelCase  )
   UpperCAmelCase_					:					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  ):
      UpperCAmelCase_					:					Optional[Any]			     =    num_up_blocks - 1 - i
      UpperCAmelCase_					:					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:
         UpperCAmelCase_					:					str			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.weight"""
         ]
         UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.bias"""
         ]

      UpperCAmelCase_					:					Dict			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					List[str]			     =    {"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

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

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

   UpperCAmelCase_					:					Optional[int]			     =    [key for key in vae_state_dict if "decoder.mid.attn" in key]
   UpperCAmelCase_					:					Any			     =    renew_vae_attention_paths(__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, 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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		):
   # Only support V1
   UpperCAmelCase_					:					List[str]			     =    requests.get(
       " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"  )
   UpperCAmelCase_					:					List[Any]			     =    io.BytesIO(r.content  )

   UpperCAmelCase_					:					Any			     =    OmegaConf.load(__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    512
   UpperCAmelCase_					:					Optional[Any]			     =    "cuda" if torch.cuda.is_available() else "cpu"
   if checkpoint_path.endswith("safetensors"  ):
      from safetensors import safe_open

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

   # Convert the VAE model.
   UpperCAmelCase_					:					Dict			     =    create_vae_diffusers_config(__lowerCamelCase,		image_size=__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    custom_convert_ldm_vae_checkpoint(__lowerCamelCase,		__lowerCamelCase  )

   UpperCAmelCase_					:					int			     =    AutoencoderKL(**__lowerCamelCase  )
   vae.load_state_dict(__lowerCamelCase  )
   vae.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							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.')

    _a		       =							parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


