






'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor






@require_vision
class        a_		(    unittest.TestCase       ):

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      tempfile.mkdtemp()

        UpperCamelCase       						=      [
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """[PAD]""",
            """[MASK]""",
            """的""",
            """价""",
            """格""",
            """是""",
            """15""",
            """便""",
            """alex""",
            """##andra""",
            """，""",
            """。""",
            """-""",
            """t""",
            """shirt""",
        ]
        UpperCamelCase       						=      os.path.join(self.tmpdirname    ,	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]   )   )

        UpperCamelCase       						=      {
            """do_resize""": True,
            """size""": {"""height""": 224, """width""": 224},
            """do_center_crop""": True,
            """crop_size""": {"""height""": 18, """width""": 18},
            """do_normalize""": True,
            """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
            """do_convert_rgb""": True,
        }
        UpperCamelCase       						=      os.path.join(self.tmpdirname    ,	_SCREAMING_SNAKE_CASE   )
        with open(self.image_processor_file    ,	"""w"""    ,	encoding="""utf-8"""   ) as fp:
              json.dump(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        return BertTokenizer.from_pretrained(self.tmpdirname    ,	**_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






        return BertTokenizerFast.from_pretrained(self.tmpdirname    ,	**_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname    ,	**_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        shutil.rmtree(self.tmpdirname   )

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      [np.random.randint(255    ,	size=(3, 30, 400)    ,	dtype=np.uinta   )]

        UpperCamelCase       						=      [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE    ,	0    ,	-1   )   ) for x in image_inputs]

        return image_inputs

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.get_tokenizer()
        UpperCamelCase       						=      self.get_rust_tokenizer()
        UpperCamelCase       						=      self.get_image_processor()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )
        processor_slow.save_pretrained(self.tmpdirname   )
        UpperCamelCase       						=      ChineseCLIPProcessor.from_pretrained(self.tmpdirname    ,	use_fast=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )
        processor_fast.save_pretrained(self.tmpdirname   )
        UpperCamelCase       						=      ChineseCLIPProcessor.from_pretrained(self.tmpdirname   )

        self.assertEqual(processor_slow.tokenizer.get_vocab()    ,	tokenizer_slow.get_vocab()   )
        self.assertEqual(processor_fast.tokenizer.get_vocab()    ,	tokenizer_fast.get_vocab()   )
        self.assertEqual(tokenizer_slow.get_vocab()    ,	tokenizer_fast.get_vocab()   )
        self.assertIsInstance(processor_slow.tokenizer    ,	_SCREAMING_SNAKE_CASE   )
        self.assertIsInstance(processor_fast.tokenizer    ,	_SCREAMING_SNAKE_CASE   )

        self.assertEqual(processor_slow.image_processor.to_json_string()    ,	image_processor.to_json_string()   )
        self.assertEqual(processor_fast.image_processor.to_json_string()    ,	image_processor.to_json_string()   )
        self.assertIsInstance(processor_slow.image_processor    ,	_SCREAMING_SNAKE_CASE   )
        self.assertIsInstance(processor_fast.image_processor    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=self.get_tokenizer()    ,	image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )

        UpperCamelCase       						=      self.get_tokenizer(cls_token="""(CLS)"""    ,	sep_token="""(SEP)"""   )
        UpperCamelCase       						=      self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname    ,	cls_token="""(CLS)"""    ,	sep_token="""(SEP)"""    ,	do_normalize=_SCREAMING_SNAKE_CASE   )

        self.assertEqual(processor.tokenizer.get_vocab()    ,	tokenizer_add_kwargs.get_vocab()   )
        self.assertIsInstance(processor.tokenizer    ,	_SCREAMING_SNAKE_CASE   )

        self.assertEqual(processor.image_processor.to_json_string()    ,	image_processor_add_kwargs.to_json_string()   )
        self.assertIsInstance(processor.image_processor    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.prepare_image_inputs()

        UpperCamelCase       						=      image_processor(_SCREAMING_SNAKE_CASE    ,	return_tensors="""np"""   )
        UpperCamelCase       						=      processor(images=_SCREAMING_SNAKE_CASE    ,	return_tensors="""np"""   )

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()    ,	input_processor[key].sum()    ,	delta=1e-2   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      """Alexandra，T-shirt的价格是15便士。"""

        UpperCamelCase       						=      processor(text=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      tokenizer(_SCREAMING_SNAKE_CASE   )

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]    ,	encoded_processor[key]   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      """Alexandra，T-shirt的价格是15便士。"""
        UpperCamelCase       						=      self.prepare_image_inputs()

        UpperCamelCase       						=      processor(text=_SCREAMING_SNAKE_CASE    ,	images=_SCREAMING_SNAKE_CASE   )

        self.assertListEqual(list(inputs.keys()   )    ,	["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]   )

        # test if it raises when no input is passed
        with pytest.raises(_SCREAMING_SNAKE_CASE   ):
              processor()

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        UpperCamelCase       						=      processor.batch_decode(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   )

        self.assertListEqual(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )


  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.get_image_processor()
        UpperCamelCase       						=      self.get_tokenizer()

        UpperCamelCase       						=      ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,	image_processor=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      """Alexandra，T-shirt的价格是15便士。"""
        UpperCamelCase       						=      self.prepare_image_inputs()

        UpperCamelCase       						=      processor(text=_SCREAMING_SNAKE_CASE    ,	images=_SCREAMING_SNAKE_CASE   )

        self.assertListEqual(list(inputs.keys()   )    ,	processor.model_input_names   )








'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=1					)->				Tuple:
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split("""."""					)[n_shave_prefix_segments:]					)
      else:
            return ".".join(path.split("""."""					)[:n_shave_prefix_segments]					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				Dict:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item.replace("""in_layers.0"""	,		"""norm1"""					)
            UpperCamelCase       						=      new_item.replace("""in_layers.2"""	,		"""conv1"""					)

            UpperCamelCase       						=      new_item.replace("""out_layers.0"""	,		"""norm2"""					)
            UpperCamelCase       						=      new_item.replace("""out_layers.3"""	,		"""conv2"""					)

            UpperCamelCase       						=      new_item.replace("""emb_layers.1"""	,		"""time_emb_proj"""					)
            UpperCamelCase       						=      new_item.replace("""skip_connection"""	,		"""conv_shortcut"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

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

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				List[str]:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item

            UpperCamelCase       						=      new_item.replace("""norm.weight"""	,		"""group_norm.weight"""					)
            UpperCamelCase       						=      new_item.replace("""norm.bias"""	,		"""group_norm.bias"""					)

            UpperCamelCase       						=      new_item.replace("""proj_out.weight"""	,		"""proj_attn.weight"""					)
            UpperCamelCase       						=      new_item.replace("""proj_out.bias"""	,		"""proj_attn.bias"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

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

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				str:
      assert isinstance(__UpperCamelCase	,		__UpperCamelCase					), "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():
                  UpperCamelCase       						=      old_checkpoint[path]
                  UpperCamelCase       						=      old_tensor.shape[0] // 3

                  UpperCamelCase       						=      (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

                  UpperCamelCase       						=      old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

                  UpperCamelCase       						=      query.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      key.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      value.reshape(__UpperCamelCase					)

      for path in paths:
            UpperCamelCase       						=      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
            UpperCamelCase       						=      new_path.replace("""middle_block.0"""	,		"""mid_block.resnets.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.1"""	,		"""mid_block.attentions.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.2"""	,		"""mid_block.resnets.1"""					)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        UpperCamelCase       						=      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:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]][:, :, 0]
            else:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]]







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      UpperCamelCase       						=      {}

      UpperCamelCase       						=      checkpoint["""time_embed.0.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.0.bias"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.bias"""]

      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.weight"""]
      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.bias"""]

      UpperCamelCase       						=      checkpoint["""out.0.weight"""]
      UpperCamelCase       						=      checkpoint["""out.0.bias"""]
      UpperCamelCase       						=      checkpoint["""out.2.weight"""]
      UpperCamelCase       						=      checkpoint["""out.2.bias"""]

      # Retrieves the keys for the input blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """input_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the middle blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """middle_block""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the output blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """output_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      for i in range(1	,		__UpperCamelCase					):
            UpperCamelCase       						=      (i - 1) // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      (i - 1) % (config["""num_res_blocks"""] + 1)

            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
            UpperCamelCase       						=      [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:
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.weight"
                  ]
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.bias"
                  ]
                  continue

            UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
            UpperCamelCase       						=      {"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
            UpperCamelCase       						=      {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
            assign_to_checkpoint(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path, resnet_op]	,		config=__UpperCamelCase					)

            if len(__UpperCamelCase					):
                  UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      {
                      """old""": F"input_blocks.{i}.1",
                      """new""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
                  }
                  UpperCamelCase       						=      {
                      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(
                      __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase	,		)

      UpperCamelCase       						=      middle_blocks[0]
      UpperCamelCase       						=      middle_blocks[1]
      UpperCamelCase       						=      middle_blocks[2]

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
      UpperCamelCase       						=      {
          """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(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase					)

      for i in range(__UpperCamelCase					):
            UpperCamelCase       						=      i // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      i % (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      [shave_segments(__UpperCamelCase	,		2					) for name in output_blocks[i]]
            UpperCamelCase       						=      {}

            for layer in output_block_layers:
                  UpperCamelCase						,UpperCamelCase       						=      layer.split("""."""					)[0], shave_segments(__UpperCamelCase	,		1					)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(__UpperCamelCase					)
                  else:
                        UpperCamelCase       						=      [layer_name]

            if len(__UpperCamelCase					) > 1:
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)

                  UpperCamelCase       						=      {"""old""": F"output_blocks.{i}.0", """new""": F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
                  assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		config=__UpperCamelCase					)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        UpperCamelCase       						=      list(output_block_list.values()					).index(["""conv.weight""", """conv.bias"""]					)
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.weight"
                        ]
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.bias"
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(__UpperCamelCase					) == 2:
                              UpperCamelCase       						=      []

                  if len(__UpperCamelCase					):
                        UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                        UpperCamelCase       						=      {
                            """old""": F"output_blocks.{i}.1",
                            """new""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                        }
                        UpperCamelCase       						=      {
                            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(
                            __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions					) else None	,		config=__UpperCamelCase	,		)
            else:
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase	,		n_shave_prefix_segments=1					)
                  for path in resnet_0_paths:
                        UpperCamelCase       						=      """.""".join(["""output_blocks""", str(__UpperCamelCase					), path["""old"""]]					)
                        UpperCamelCase       						=      """.""".join(["""up_blocks""", str(__UpperCamelCase					), """resnets""", str(__UpperCamelCase					), path["""new"""]]					)

                        UpperCamelCase       						=      checkpoint[old_path]

      return new_checkpoint


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

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     torch.load(args.checkpoint_path)

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

     SCREAMING_SNAKE_CASE__											=     convert_ldm_checkpoint(checkpoint, config)

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

     SCREAMING_SNAKE_CASE__											=     UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

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

          SCREAMING_SNAKE_CASE__											=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)
