





'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Optional[Any]=1       ):
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("."       )[n_shave_prefix_segments:]       )
    else:
        return ".".join(path.split("."       )[:n_shave_prefix_segments]       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict						,       _lowerCamelCase					:					Dict=0       ):
    A__				=			[]
    for old_item in old_list:
        A__				=			old_item.replace("in_layers.0"						,       "norm1"       )
        A__				=			new_item.replace("in_layers.2"						,       "conv1"       )

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

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

        A__				=			shave_segments(_lowerCamelCase						,       n_shave_prefix_segments=_lowerCamelCase       )

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

    return mapping





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					Optional[int]=0       ):
    A__				=			[]
    for old_item in old_list:
        A__				=			old_item

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

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

        A__				=			shave_segments(_lowerCamelCase						,       n_shave_prefix_segments=_lowerCamelCase       )

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

    return mapping





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					Optional[Any]						,       _lowerCamelCase					:					Union[str, Any]=None						,       _lowerCamelCase					:					Any=None						,       _lowerCamelCase					:					Dict=None       ):
    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__				=			old_checkpoint[path]
            A__				=			old_tensor.shape[0] // 3

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

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

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

            A__				=			query.reshape(_lowerCamelCase       )
            A__				=			key.reshape(_lowerCamelCase       )
            A__				=			value.reshape(_lowerCamelCase       )

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

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





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict						,       _lowerCamelCase					:					int       ):
    A__				=			{}

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

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

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

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

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

        A__				=			renew_resnet_paths(_lowerCamelCase       )
        A__				=			{"old": F"input_blocks.{i}.0", "new": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
        A__				=			{"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__				=			renew_attention_paths(_lowerCamelCase       )
            A__				=			{
                "old": F"input_blocks.{i}.1",
                "new": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
            }
            A__				=			{
                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__				=			middle_blocks[0]
    A__				=			middle_blocks[1]
    A__				=			middle_blocks[2]

    A__				=			renew_resnet_paths(_lowerCamelCase       )
    assign_to_checkpoint(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       config=_lowerCamelCase       )

    A__				=			renew_resnet_paths(_lowerCamelCase       )
    assign_to_checkpoint(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       config=_lowerCamelCase       )

    A__				=			renew_attention_paths(_lowerCamelCase       )
    A__				=			{
        "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__				=			i // (config["num_res_blocks"] + 1)
        A__				=			i % (config["num_res_blocks"] + 1)
        A__				=			[shave_segments(_lowerCamelCase						,       2       ) for name in output_blocks[i]]
        A__				=			{}

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

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

            A__				=			renew_resnet_paths(_lowerCamelCase       )
            A__				=			renew_resnet_paths(_lowerCamelCase       )

            A__				=			{"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__				=			list(output_block_list.values()       ).index(["conv.weight", "conv.bias"]       )
                A__				=			checkpoint[
                    F"output_blocks.{i}.{index}.conv.weight"
                ]
                A__				=			checkpoint[
                    F"output_blocks.{i}.{index}.conv.bias"
                ]

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

            if len(_lowerCamelCase       ):
                A__				=			renew_attention_paths(_lowerCamelCase       )
                A__				=			{
                    "old": F"output_blocks.{i}.1",
                    "new": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                }
                A__				=			{
                    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__				=			renew_resnet_paths(_lowerCamelCase						,       n_shave_prefix_segments=1       )
            for path in resnet_0_paths:
                A__				=			".".join(["output_blocks", str(_lowerCamelCase       ), path["old"]]       )
                A__				=			".".join(["up_blocks", str(_lowerCamelCase       ), "resnets", str(_lowerCamelCase       ), path["new"]]       )

                A__				=			checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
  __lowerCAmelCase							:	Optional[int]			=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							:	List[Any]			=parser.parse_args()

  __lowerCAmelCase							:	int			=torch.load(args.checkpoint_path)

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

  __lowerCAmelCase							:	List[str]			=convert_ldm_checkpoint(checkpoint, config)

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

  __lowerCAmelCase							:	Any			=UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    __lowerCAmelCase							:	Optional[Any]			=DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
    __lowerCAmelCase							:	List[str]			=VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

    __lowerCAmelCase							:	Optional[int]			=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


__lowerCAmelCase							:	Dict			=DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__lowerCAmelCase							:	List[Any]			="main"
# Default branch name
__lowerCAmelCase							:	int			="f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
__lowerCAmelCase							:	List[Any]			="aaaaaaa"
# This commit does not exist, so we should 404.

__lowerCAmelCase							:	Optional[int]			="d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
__lowerCAmelCase							:	Dict			="4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"





@contextlib.contextmanager
def 						UpperCamelCase				(							):
    print("Welcome!"       )
    yield
    print("Bye!"       )





@contextlib.contextmanager
def 						UpperCamelCase				(							):
    print("Bonjour!"       )
    yield
    print("Au revoir!"       )






class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :str							)->							List[str]:
     # If the spec is missing, importlib would not be able to import the module dynamically.
     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      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :Union[str, Any]							)->							Any:
     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      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							Tuple:
     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      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :int							)->							Union[str, Any]:
     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      UpperCAmelCase_							(				self      :int							)->							Dict:
     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["labels", "next_sentence_label"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["start_positions", "end_positions"]							)






     class    UpperCAmelCase	(       UpperCamelCase__				):
      pass

     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)
 @require_tf
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Union[str, Any]:
     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["labels", "next_sentence_label"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["start_positions", "end_positions"]							)






     class    UpperCAmelCase	(       UpperCamelCase__				):
      pass

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



 @require_flax
 def      UpperCAmelCase_							(				self      :Dict							)->							str:
     # Flax models don't have labels
     self.assertEqual(find_labels(lowercase_							)						,       []							)
     self.assertEqual(find_labels(lowercase_							)						,       []							)
     self.assertEqual(find_labels(lowercase_							)						,       []							)






     class    UpperCAmelCase	(       UpperCamelCase__				):
      pass

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



