






'''simple docstring'''
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase=1				):


   """simple docstring"""




   if n_shave_prefix_segments >= 0:
      return ".".join(path.split("."				)[n_shave_prefix_segments:]				)
   else:
      return ".".join(path.split("."				)[:n_shave_prefix_segments]				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase=0				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  []
   for old_item in old_list:
      lowercase_						:     Union[str, Any]							     =  old_item.replace("in_layers.0"    ,		"norm1"				)
      lowercase_						:     Any							     =  new_item.replace("in_layers.2"    ,		"conv1"				)

      lowercase_						:     Any							     =  new_item.replace("out_layers.0"    ,		"norm2"				)
      lowercase_						:     Optional[int]							     =  new_item.replace("out_layers.3"    ,		"conv2"				)

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

      lowercase_						:     Optional[Any]							     =  shave_segments(_UpperCamelCase    ,		n_shave_prefix_segments=_UpperCamelCase				)

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

   return mapping




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase=0				):


   """simple docstring"""




   lowercase_						:     int							     =  []
   for old_item in old_list:
      lowercase_						:     Optional[Any]							     =  old_item

      lowercase_						:     str							     =  new_item.replace("norm.weight"    ,		"group_norm.weight"				)
      lowercase_						:     Optional[Any]							     =  new_item.replace("norm.bias"    ,		"group_norm.bias"				)

      lowercase_						:     Dict							     =  new_item.replace("proj_out.weight"    ,		"proj_attn.weight"				)
      lowercase_						:     Optional[Any]							     =  new_item.replace("proj_out.bias"    ,		"proj_attn.bias"				)

      lowercase_						:     Optional[Any]							     =  shave_segments(_UpperCamelCase    ,		n_shave_prefix_segments=_UpperCamelCase				)

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

   return mapping




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None				):


   """simple docstring"""




   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():
         lowercase_						:     Union[str, Any]							     =  old_checkpoint[path]
         lowercase_						:     Optional[Any]							     =  old_tensor.shape[0] // 3

         lowercase_						:     int							     =  (-1, channels) if len(old_tensor.shape				) == 3 else (-1)

         lowercase_						:     Union[str, Any]							     =  old_tensor.shape[0] // config["num_head_channels"] // 3

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

         lowercase_						:     Union[str, Any]							     =  query.reshape(_UpperCamelCase				)
         lowercase_						:     str							     =  key.reshape(_UpperCamelCase				)
         lowercase_						:     Dict							     =  value.reshape(_UpperCamelCase				)

   for path in paths:
      lowercase_						:     Union[str, 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
      lowercase_						:     List[str]							     =  new_path.replace("middle_block.0"    ,		"mid_block.resnets.0"				)
      lowercase_						:     Dict							     =  new_path.replace("middle_block.1"    ,		"mid_block.attentions.0"				)
      lowercase_						:     List[Any]							     =  new_path.replace("middle_block.2"    ,		"mid_block.resnets.1"				)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            lowercase_						:     Optional[int]							     =  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:
         lowercase_						:     Optional[Any]							     =  old_checkpoint[path["old"]][:, :, 0]
      else:
         lowercase_						:     Optional[int]							     =  old_checkpoint[path["old"]]




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  {}

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

   lowercase_						:     List[Any]							     =  checkpoint["input_blocks.0.0.weight"]
   lowercase_						:     Any							     =  checkpoint["input_blocks.0.0.bias"]

   lowercase_						:     List[Any]							     =  checkpoint["out.0.weight"]
   lowercase_						:     Any							     =  checkpoint["out.0.bias"]
   lowercase_						:     Tuple							     =  checkpoint["out.2.weight"]
   lowercase_						:     Optional[Any]							     =  checkpoint["out.2.bias"]

   # Retrieves the keys for the input blocks only
   lowercase_						:     Any							     =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "input_blocks" in layer}				)
   lowercase_						:     List[Any]							     =  {
       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
   lowercase_						:     List[Any]							     =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "middle_block" in layer}				)
   lowercase_						:     Union[str, Any]							     =  {
       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
   lowercase_						:     List[Any]							     =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "output_blocks" in layer}				)
   lowercase_						:     Dict							     =  {
       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				):
      lowercase_						:     List[Any]							     =  (i - 1) // (config["num_res_blocks"] + 1)
      lowercase_						:     Any							     =  (i - 1) % (config["num_res_blocks"] + 1)

      lowercase_						:     Dict							     =  [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
      lowercase_						:     str							     =  [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:
         lowercase_						:     int							     =  checkpoint[
             F"""input_blocks.{i}.0.op.weight"""
         ]
         lowercase_						:     Any							     =  checkpoint[
             F"""input_blocks.{i}.0.op.bias"""
         ]
         continue

      lowercase_						:     Union[str, Any]							     =  renew_resnet_paths(_UpperCamelCase				)
      lowercase_						:     Union[str, Any]							     =  {"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      lowercase_						:     Any							     =  {"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				):
         lowercase_						:     Tuple							     =  renew_attention_paths(_UpperCamelCase				)
         lowercase_						:     Dict							     =  {
             "old": F"""input_blocks.{i}.1""",
             "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
         }
         lowercase_						:     List[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(
             _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		additional_replacements=[meta_path]    ,		attention_paths_to_split=_UpperCamelCase    ,		config=_UpperCamelCase    ,		)

   lowercase_						:     Optional[Any]							     =  middle_blocks[0]
   lowercase_						:     Optional[Any]							     =  middle_blocks[1]
   lowercase_						:     Any							     =  middle_blocks[2]

   lowercase_						:     int							     =  renew_resnet_paths(_UpperCamelCase				)
   assign_to_checkpoint(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		config=_UpperCamelCase				)

   lowercase_						:     Optional[int]							     =  renew_resnet_paths(_UpperCamelCase				)
   assign_to_checkpoint(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		config=_UpperCamelCase				)

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

      for layer in output_block_layers:
         lowercase_     ,		lowercase_						:     int							     =  layer.split("."				)[0], shave_segments(_UpperCamelCase    ,		1				)
         if layer_id in output_block_list:
            output_block_list[layer_id].append(_UpperCamelCase				)
         else:
            lowercase_						:     Dict							     =  [layer_name]

      if len(_UpperCamelCase				) > 1:
         lowercase_						:     Optional[int]							     =  [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
         lowercase_						:     List[str]							     =  [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

         lowercase_						:     Optional[int]							     =  renew_resnet_paths(_UpperCamelCase				)
         lowercase_						:     Tuple							     =  renew_resnet_paths(_UpperCamelCase				)

         lowercase_						:     Optional[Any]							     =  {"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():
            lowercase_						:     Dict							     =  list(output_block_list.values()				).index(["conv.weight", "conv.bias"]				)
            lowercase_						:     Union[str, Any]							     =  checkpoint[
                F"""output_blocks.{i}.{index}.conv.weight"""
            ]
            lowercase_						:     int							     =  checkpoint[
                F"""output_blocks.{i}.{index}.conv.bias"""
            ]

            # Clear attentions as they have been attributed above.
            if len(_UpperCamelCase				) == 2:
               lowercase_						:     List[Any]							     =  []

         if len(_UpperCamelCase				):
            lowercase_						:     Optional[int]							     =  renew_attention_paths(_UpperCamelCase				)
            lowercase_						:     Any							     =  {
                "old": F"""output_blocks.{i}.1""",
                "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            lowercase_						:     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(
                _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:
         lowercase_						:     Union[str, Any]							     =  renew_resnet_paths(_UpperCamelCase    ,		n_shave_prefix_segments=1				)
         for path in resnet_0_paths:
            lowercase_						:     str							     =  ".".join(["output_blocks", str(_UpperCamelCase				), path["old"]]				)
            lowercase_						:     Optional[int]							     =  ".".join(["up_blocks", str(_UpperCamelCase				), "resnets", str(_UpperCamelCase				), path["new"]]				)

            lowercase_						:     List[str]							     =  checkpoint[old_path]

   return new_checkpoint


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

 UpperCamelCase__			=      parser.parse_args()

 UpperCamelCase__			=      torch.load(args.checkpoint_path)

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

 UpperCamelCase__			=      convert_ldm_checkpoint(checkpoint, config)

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

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

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

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












'''simple docstring'''
import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
 from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
 from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
 from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


UpperCamelCase__			=      DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

UpperCamelCase__			=      'main'
# Default branch name
UpperCamelCase__			=      'f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'
# One particular commit (not the top of `main`)
UpperCamelCase__			=      'aaaaaaa'
# This commit does not exist, so we should 404.

UpperCamelCase__			=      'd9e9f15bc825e4b2c9249e9578f884bbcb5e3684'
# Sha-1 of config.json on the top of `main`, for checking purposes
UpperCamelCase__			=      '4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'




@contextlib.contextmanager
def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   print("Welcome!"				)
   yield
   print("Bye!"				)




@contextlib.contextmanager
def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   print("Bonjour!"				)
   yield
   print("Au revoir!"				)


class 							_UpperCAmelCase    (		unittest.TestCase      ):




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       assert transformers.__spec__ is not None
       assert importlib.util.find_spec("transformers"      ) is not None


class 							_UpperCAmelCase    (		unittest.TestCase      ):




    @unittest.mock.patch("sys.stdout"	,  new_callable=io.StringIO      )
    def 					lowerCAmelCase__					(      self	:							str	,  a	:							List[str]      ):






       '''simple docstring'''






       with ContextManagers([]      ):
          print("Transformers are awesome!"      )
       # The print statement adds a new line at the end of the output
       self.assertEqual(mock_stdout.getvalue()	,  "Transformers are awesome!\n"      )




    @unittest.mock.patch("sys.stdout"	,  new_callable=io.StringIO      )
    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       with ContextManagers([context_en()]      ):
          print("Transformers are awesome!"      )
       # The output should be wrapped with an English welcome and goodbye
       self.assertEqual(mock_stdout.getvalue()	,  "Welcome!\nTransformers are awesome!\nBye!\n"      )




    @unittest.mock.patch("sys.stdout"	,  new_callable=io.StringIO      )
    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							List[Any]      ):






       '''simple docstring'''






       with ContextManagers([context_fr(), context_en()]      ):
          print("Transformers are awesome!"      )
       # The output should be wrapped with an English and French welcome and goodbye
       self.assertEqual(mock_stdout.getvalue()	,  "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"      )




    @require_torch
    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       self.assertEqual(find_labels(a      )	,  ["labels"]      )
       self.assertEqual(find_labels(a      )	,  ["labels", "next_sentence_label"]      )
       self.assertEqual(find_labels(a      )	,  ["start_positions", "end_positions"]      )


       class 							_UpperCAmelCase    (		snake_case      ):
           pass


       self.assertEqual(find_labels(a      )	,  ["labels"]      )




    @require_tf
    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       self.assertEqual(find_labels(a      )	,  ["labels"]      )
       self.assertEqual(find_labels(a      )	,  ["labels", "next_sentence_label"]      )
       self.assertEqual(find_labels(a      )	,  ["start_positions", "end_positions"]      )


       class 							_UpperCAmelCase    (		snake_case      ):
           pass


       self.assertEqual(find_labels(a      )	,  ["labels"]      )





    @require_flax
    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       self.assertEqual(find_labels(a      )	,  []      )
       self.assertEqual(find_labels(a      )	,  []      )
       self.assertEqual(find_labels(a      )	,  []      )


       class 							_UpperCAmelCase    (		snake_case      ):
           pass


       self.assertEqual(find_labels(a      )	,  []      )




