





import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
 from test_module.custom_tokenization_fast import CustomTokenizerFast


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_       :					Dict        =      mock.Mock()
        A_       :					str        =      500
        A_       :					str        =      {}
        A_       :					List[Any]        =      HTTPError
        A_       :					Dict        =      {}

        # Download this model to make sure it's in the cache.
        A_       :					str        =      BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"	)

        # Under the mock environment we get a 500 error when trying to reach the tokenizer.
        with mock.patch("requests.Session.request"      ,   return_value=snake_case	) as mock_head:
           A_       :					Any        =      BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"	)
           # This check we did call the fake head request
           mock_head.assert_called()



     @require_tokenizers
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					Dict        =      mock.Mock()
        A_       :					str        =      500
        A_       :					int        =      {}
        A_       :					int        =      HTTPError
        A_       :					Tuple        =      {}

        # Download this model to make sure it's in the cache.
        A_       :					List[Any]        =      GPTaTokenizerFast.from_pretrained("gpt2"	)

        # Under the mock environment we get a 500 error when trying to reach the tokenizer.
        with mock.patch("requests.Session.request"      ,   return_value=snake_case	) as mock_head:
           A_       :					Dict        =      GPTaTokenizerFast.from_pretrained("gpt2"	)
           # This check we did call the fake head request
           mock_head.assert_called()



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        try:
           A_       :					List[str]        =      tempfile.mktemp()
           with open(snake_case      ,   "wb"	) as f:
              http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"      ,   snake_case	)

           A_       :					List[str]        =      AlbertTokenizer.from_pretrained(snake_case	)
        finally:
           os.remove(snake_case	)

        # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
        # the current folder and have the right name.
        if os.path.isfile("tokenizer.json"	):
           # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
           return
        try:
           with open("tokenizer.json"      ,   "wb"	) as f:
              http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"      ,   snake_case	)
           A_       :					List[Any]        =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
           # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
           self.assertEqual(tokenizer.vocab_size      ,   1_000	)
           # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

        finally:
           os.remove("tokenizer.json"	)





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					Dict        =      AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"	)




@is_staging_test
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']



     @classmethod
     def     SCREAMING_SNAKE_CASE		(				cls							:Optional[int]	):





        '''simple docstring'''




        A_       :					List[Any]        =      TOKEN
        HfFolder.save_token(snake_case	)



     @classmethod
     def     SCREAMING_SNAKE_CASE		(				cls							:List[str]	):





        '''simple docstring'''




        try:
           delete_repo(token=cls._token      ,   repo_id="test-tokenizer"	)
        except HTTPError:
           pass

        try:
           delete_repo(token=cls._token      ,   repo_id="valid_org/test-tokenizer-org"	)
        except HTTPError:
           pass

        try:
           delete_repo(token=cls._token      ,   repo_id="test-dynamic-tokenizer"	)
        except HTTPError:
           pass



     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmp_dir:
           A_       :					Any        =      os.path.join(snake_case      ,   "vocab.txt"	)
           with open(snake_case      ,   "w"      ,   encoding="utf-8"	) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]	)	)
           A_       :					Optional[int]        =      BertTokenizer(snake_case	)

        tokenizer.push_to_hub("test-tokenizer"      ,   use_auth_token=self._token	)
        A_       :					Dict        =      BertTokenizer.from_pretrained(f"{USER}/test-tokenizer"	)
        self.assertDictEqual(new_tokenizer.vocab      ,   tokenizer.vocab	)

        # Reset repo
        delete_repo(token=self._token      ,   repo_id="test-tokenizer"	)

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer.save_pretrained(snake_case      ,   repo_id="test-tokenizer"      ,   push_to_hub=snake_case      ,   use_auth_token=self._token	)

        A_       :					List[str]        =      BertTokenizer.from_pretrained(f"{USER}/test-tokenizer"	)
        self.assertDictEqual(new_tokenizer.vocab      ,   tokenizer.vocab	)



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmp_dir:
           A_       :					Tuple        =      os.path.join(snake_case      ,   "vocab.txt"	)
           with open(snake_case      ,   "w"      ,   encoding="utf-8"	) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]	)	)
           A_       :					Optional[int]        =      BertTokenizer(snake_case	)

        tokenizer.push_to_hub("valid_org/test-tokenizer-org"      ,   use_auth_token=self._token	)
        A_       :					Any        =      BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"	)
        self.assertDictEqual(new_tokenizer.vocab      ,   tokenizer.vocab	)

        # Reset repo
        delete_repo(token=self._token      ,   repo_id="valid_org/test-tokenizer-org"	)

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer.save_pretrained(
               snake_case      ,   repo_id="valid_org/test-tokenizer-org"      ,   push_to_hub=snake_case      ,   use_auth_token=self._token	)

        A_       :					List[Any]        =      BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"	)
        self.assertDictEqual(new_tokenizer.vocab      ,   tokenizer.vocab	)





     @require_tokenizers
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        CustomTokenizer.register_for_auto_class()
        with tempfile.TemporaryDirectory() as tmp_dir:
           A_       :					Any        =      os.path.join(snake_case      ,   "vocab.txt"	)
           with open(snake_case      ,   "w"      ,   encoding="utf-8"	) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]	)	)
           A_       :					List[str]        =      CustomTokenizer(snake_case	)

        # No fast custom tokenizer
        tokenizer.push_to_hub("test-dynamic-tokenizer"      ,   use_auth_token=self._token	)

        A_       :					Union[str, Any]        =      AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer"      ,   trust_remote_code=snake_case	)
        # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
        self.assertEqual(tokenizer.__class__.__name__      ,   "CustomTokenizer"	)

        # Fast and slow custom tokenizer
        CustomTokenizerFast.register_for_auto_class()
        with tempfile.TemporaryDirectory() as tmp_dir:
           A_       :					Optional[int]        =      os.path.join(snake_case      ,   "vocab.txt"	)
           with open(snake_case      ,   "w"      ,   encoding="utf-8"	) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]	)	)

           A_       :					List[Any]        =      BertTokenizerFast.from_pretrained(snake_case	)
           bert_tokenizer.save_pretrained(snake_case	)
           A_       :					str        =      CustomTokenizerFast.from_pretrained(snake_case	)

        tokenizer.push_to_hub("test-dynamic-tokenizer"      ,   use_auth_token=self._token	)

        A_       :					List[Any]        =      AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer"      ,   trust_remote_code=snake_case	)
        # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
        self.assertEqual(tokenizer.__class__.__name__      ,   "CustomTokenizerFast"	)
        A_       :					List[str]        =      AutoTokenizer.from_pretrained(
            f"{USER}/test-dynamic-tokenizer"      ,   use_fast=snake_case      ,   trust_remote_code=snake_case	)
        # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
        self.assertEqual(tokenizer.__class__.__name__      ,   "CustomTokenizer"	)




class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					Dict        =      Trie()
        trie.add("Hello 友達"	)
        self.assertEqual(trie.data      ,   {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}	)
        trie.add("Hello"	)
        trie.data
        self.assertEqual(trie.data      ,   {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        A_       :					Any        =      Trie()
        self.assertEqual(trie.split("[CLS] This is a extra_id_100"	)      ,   ["[CLS] This is a extra_id_100"]	)
        trie.add("[CLS]"	)
        trie.add("extra_id_1"	)
        trie.add("extra_id_100"	)
        self.assertEqual(trie.split("[CLS] This is a extra_id_100"	)      ,   ["[CLS]", " This is a ", "extra_id_100"]	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      Trie()
        trie.add("A"	)
        self.assertEqual(trie.split("ABC"	)      ,   ["A", "BC"]	)
        self.assertEqual(trie.split("BCA"	)      ,   ["BC", "A"]	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					List[str]        =      Trie()
        trie.add("TOKEN]"	)
        trie.add("[SPECIAL_TOKEN]"	)
        self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"	)      ,   ["This is something ", "[SPECIAL_TOKEN]"]	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      Trie()
        trie.add("A"	)
        trie.add("P"	)
        trie.add("[SPECIAL_TOKEN]"	)
        self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"	)      ,   ["This is something ", "[SPECIAL_TOKEN]"]	)



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					int        =      Trie()
        trie.add("AB"	)
        trie.add("B"	)
        trie.add("C"	)
        self.assertEqual(trie.split("ABC"	)      ,   ["AB", "C"]	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      Trie()
        trie.add("ABC"	)
        trie.add("B"	)
        trie.add("CD"	)
        self.assertEqual(trie.split("ABCD"	)      ,   ["ABC", "D"]	)





     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					int        =      Trie()
        A_       :					Union[str, Any]        =      trie.cut_text("ABC"      ,   [0, 0, 2, 1, 2, 3]	)
        self.assertEqual(snake_case      ,   ["AB", "C"]	)







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Union[str, Any]=1						)			->    Any:
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split("."						)[n_shave_prefix_segments:]						)
   else:
      return ".".join(path.split("."						)[:n_shave_prefix_segments]						)

def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			List[Any]=0						)			->    int:
   A_       :					Union[str, Any]        =      []
   for old_item in old_list:
      A_       :					Tuple        =      old_item.replace("in_layers.0"					,					"norm1"						)
      A_       :					str        =      new_item.replace("in_layers.2"					,					"conv1"						)

      A_       :					List[str]        =      new_item.replace("out_layers.0"					,					"norm2"						)
      A_       :					Optional[int]        =      new_item.replace("out_layers.3"					,					"conv2"						)

      A_       :					Union[str, Any]        =      new_item.replace("emb_layers.1"					,					"time_emb_proj"						)
      A_       :					Tuple        =      new_item.replace("skip_connection"					,					"conv_shortcut"						)

      A_       :					Any        =      shave_segments(_lowerCAmelCase					,					n_shave_prefix_segments=_lowerCAmelCase						)

      mapping.append({"old": old_item, "new": new_item}						)

   return mapping

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]					,					_lowerCAmelCase			:			List[str]=0						)			->    Tuple:
   A_       :					Any        =      []
   for old_item in old_list:
      A_       :					int        =      old_item

      A_       :					Dict        =      new_item.replace("norm.weight"					,					"group_norm.weight"						)
      A_       :					Any        =      new_item.replace("norm.bias"					,					"group_norm.bias"						)

      A_       :					Any        =      new_item.replace("proj_out.weight"					,					"proj_attn.weight"						)
      A_       :					List[str]        =      new_item.replace("proj_out.bias"					,					"proj_attn.bias"						)

      A_       :					List[Any]        =      shave_segments(_lowerCAmelCase					,					n_shave_prefix_segments=_lowerCAmelCase						)

      mapping.append({"old": old_item, "new": new_item}						)

   return mapping

def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			str					,					_lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			Dict=None					,					_lowerCAmelCase			:			Union[str, Any]=None					,					_lowerCAmelCase			:			List[str]=None						)			->    Optional[Any]:
   assert isinstance(_lowerCAmelCase					,					_lowerCAmelCase						), "Paths should be a list of dicts containing 'old' and 'new' keys."

   # Splits the attention layers into three variables.
   if attention_paths_to_split is not None:
      for path, path_map in attention_paths_to_split.items():
         A_       :					Any        =      old_checkpoint[path]
         A_       :					Tuple        =      old_tensor.shape[0] // 3

         A_       :					Union[str, Any]        =      (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

         A_       :					Optional[Any]        =      old_tensor.shape[0] // config["num_head_channels"] // 3

         A_       :					int        =      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
         A_   ,    A_   ,    A_       :					Optional[int]        =      old_tensor.split(channels // num_heads					,					dim=1						)

         A_       :					Tuple        =      query.reshape(_lowerCAmelCase						)
         A_       :					List[Any]        =      key.reshape(_lowerCAmelCase						)
         A_       :					Union[str, Any]        =      value.reshape(_lowerCAmelCase						)

   for path in paths:
      A_       :					Any        =      path["new"]

      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      # Global renaming happens here
      A_       :					List[str]        =      new_path.replace("middle_block.0"					,					"mid_block.resnets.0"						)
      A_       :					int        =      new_path.replace("middle_block.1"					,					"mid_block.attentions.0"						)
      A_       :					Tuple        =      new_path.replace("middle_block.2"					,					"mid_block.resnets.1"						)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            A_       :					Tuple        =      new_path.replace(replacement["old"]					,					replacement["new"]						)

        # proj_attn.weight has to be converted from conv 1D to linear
      if "proj_attn.weight" in new_path:
         A_       :					Tuple        =      old_checkpoint[path["old"]][:, :, 0]
      else:
         A_       :					str        =      old_checkpoint[path["old"]]

def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			List[Any]						)			->    Optional[int]:
   A_       :					Any        =      {}

   A_       :					Union[str, Any]        =      checkpoint["time_embed.0.weight"]
   A_       :					List[Any]        =      checkpoint["time_embed.0.bias"]
   A_       :					Any        =      checkpoint["time_embed.2.weight"]
   A_       :					Optional[Any]        =      checkpoint["time_embed.2.bias"]

   A_       :					List[str]        =      checkpoint["input_blocks.0.0.weight"]
   A_       :					Union[str, Any]        =      checkpoint["input_blocks.0.0.bias"]

   A_       :					str        =      checkpoint["out.0.weight"]
   A_       :					Optional[int]        =      checkpoint["out.0.bias"]
   A_       :					Optional[int]        =      checkpoint["out.2.weight"]
   A_       :					Optional[int]        =      checkpoint["out.2.bias"]

   # Retrieves the keys for the input blocks only
   A_       :					List[str]        =      len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "input_blocks" in layer}						)
   A_       :					Union[str, Any]        =      {
       layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key]
       for layer_id in range(_lowerCAmelCase						)
   }

   # Retrieves the keys for the middle blocks only
   A_       :					Union[str, Any]        =      len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "middle_block" in layer}						)
   A_       :					Tuple        =      {
       layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key]
       for layer_id in range(_lowerCAmelCase						)
   }

   # Retrieves the keys for the output blocks only
   A_       :					Dict        =      len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "output_blocks" in layer}						)
   A_       :					Optional[Any]        =      {
       layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key]
       for layer_id in range(_lowerCAmelCase						)
   }

   for i in range(1					,					_lowerCAmelCase						):
      A_       :					Union[str, Any]        =      (i - 1) // (config["num_res_blocks"] + 1)
      A_       :					Optional[int]        =      (i - 1) % (config["num_res_blocks"] + 1)

      A_       :					str        =      [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
      A_       :					List[Any]        =      [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

      if f"input_blocks.{i}.0.op.weight" in checkpoint:
         A_       :					Tuple        =      checkpoint[
             f"input_blocks.{i}.0.op.weight"
         ]
         A_       :					List[Any]        =      checkpoint[
             f"input_blocks.{i}.0.op.bias"
         ]
         continue

      A_       :					List[Any]        =      renew_resnet_paths(_lowerCAmelCase						)
      A_       :					Any        =      {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
      A_       :					Any        =      {"old": "resnets.2.op", "new": "downsamplers.0.op"}
      assign_to_checkpoint(
          _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					additional_replacements=[meta_path, resnet_op]					,					config=_lowerCAmelCase						)

      if len(_lowerCAmelCase						):
         A_       :					Tuple        =      renew_attention_paths(_lowerCAmelCase						)
         A_       :					List[str]        =      {
             "old": f"input_blocks.{i}.1",
             "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
         }
         A_       :					Any        =      {
             f"input_blocks.{i}.1.qkv.bias": {
                 "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                 "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                 "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
             },
             f"input_blocks.{i}.1.qkv.weight": {
                 "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                 "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                 "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
             },
         }
         assign_to_checkpoint(
             _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					additional_replacements=[meta_path]					,					attention_paths_to_split=_lowerCAmelCase					,					config=_lowerCAmelCase					,					)

   A_       :					List[Any]        =      middle_blocks[0]
   A_       :					Tuple        =      middle_blocks[1]
   A_       :					Dict        =      middle_blocks[2]

   A_       :					str        =      renew_resnet_paths(_lowerCAmelCase						)
   assign_to_checkpoint(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					config=_lowerCAmelCase						)

   A_       :					Tuple        =      renew_resnet_paths(_lowerCAmelCase						)
   assign_to_checkpoint(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					config=_lowerCAmelCase						)

   A_       :					Any        =      renew_attention_paths(_lowerCAmelCase						)
   A_       :					Optional[int]        =      {
       "middle_block.1.qkv.bias": {
           "key": "mid_block.attentions.0.key.bias",
           "query": "mid_block.attentions.0.query.bias",
           "value": "mid_block.attentions.0.value.bias",
       },
       "middle_block.1.qkv.weight": {
           "key": "mid_block.attentions.0.key.weight",
           "query": "mid_block.attentions.0.query.weight",
           "value": "mid_block.attentions.0.value.weight",
       },
   }
   assign_to_checkpoint(
       _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					attention_paths_to_split=_lowerCAmelCase					,					config=_lowerCAmelCase						)

   for i in range(_lowerCAmelCase						):
      A_       :					Optional[int]        =      i // (config["num_res_blocks"] + 1)
      A_       :					Optional[Any]        =      i % (config["num_res_blocks"] + 1)
      A_       :					Optional[Any]        =      [shave_segments(_lowerCAmelCase					,					2						) for name in output_blocks[i]]
      A_       :					Union[str, Any]        =      {}

      for layer in output_block_layers:
         A_   ,    A_       :					str        =      layer.split("."						)[0], shave_segments(_lowerCAmelCase					,					1						)
         if layer_id in output_block_list:
            output_block_list[layer_id].append(_lowerCAmelCase						)
         else:
            A_       :					List[str]        =      [layer_name]

      if len(_lowerCAmelCase						) > 1:
         A_       :					Dict        =      [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
         A_       :					List[Any]        =      [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

         A_       :					Optional[Any]        =      renew_resnet_paths(_lowerCAmelCase						)
         A_       :					Any        =      renew_resnet_paths(_lowerCAmelCase						)

         A_       :					Optional[Any]        =      {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
         assign_to_checkpoint(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					additional_replacements=[meta_path]					,					config=_lowerCAmelCase						)

         if ["conv.weight", "conv.bias"] in output_block_list.values():
            A_       :					int        =      list(output_block_list.values()						).index(["conv.weight", "conv.bias"]						)
            A_       :					Union[str, Any]        =      checkpoint[
                f"output_blocks.{i}.{index}.conv.weight"
            ]
            A_       :					int        =      checkpoint[
                f"output_blocks.{i}.{index}.conv.bias"
            ]

            # Clear attentions as they have been attributed above.
            if len(_lowerCAmelCase						) == 2:
               A_       :					Union[str, Any]        =      []

         if len(_lowerCAmelCase						):
            A_       :					Dict        =      renew_attention_paths(_lowerCAmelCase						)
            A_       :					Union[str, Any]        =      {
                "old": f"output_blocks.{i}.1",
                "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
            }
            A_       :					Any        =      {
                f"output_blocks.{i}.1.qkv.bias": {
                    "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                    "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                    "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                },
                f"output_blocks.{i}.1.qkv.weight": {
                    "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                    "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                    "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                },
            }
            assign_to_checkpoint(
                _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					additional_replacements=[meta_path]					,					attention_paths_to_split=to_split if any("qkv" in key for key in attentions						) else None					,					config=_lowerCAmelCase					,					)
      else:
         A_       :					Optional[int]        =      renew_resnet_paths(_lowerCAmelCase					,					n_shave_prefix_segments=1						)
         for path in resnet_0_paths:
            A_       :					List[Any]        =      ".".join(["output_blocks", str(_lowerCAmelCase						), path["old"]]						)
            A_       :					Optional[Any]        =      ".".join(["up_blocks", str(_lowerCAmelCase						), "resnets", str(_lowerCAmelCase						), path["new"]]						)

            A_       :					Optional[int]        =      checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase				:   List[Any]						       =						argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
 )

 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 _lowerCAmelCase				:   Optional[Any]						       =						parser.parse_args()

 _lowerCAmelCase				:   Optional[Any]						       =						torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  _lowerCAmelCase				:   str						       =						json.loads(f.read())

 _lowerCAmelCase				:   Tuple						       =						convert_ldm_checkpoint(checkpoint, config)

 if "ldm" in config:
  del config["ldm"]

 _lowerCAmelCase				:   Optional[Any]						       =						UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _lowerCAmelCase				:   List[str]						       =						DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  _lowerCAmelCase				:   List[str]						       =						VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  _lowerCAmelCase				:   Optional[Any]						       =						LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)
