






'''simple docstring'''







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def     A__      (					__lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      Union[str, 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     A__      (					__lowerCAmelCase		:      List[Any] ,     __lowerCAmelCase		:      Dict=0    ):
						lowerCamelCase__								=	[]
						for old_item in old_list:
												lowerCamelCase__								=	old_item.replace("""in_layers.0""" ,     """norm1"""    )
												lowerCamelCase__								=	new_item.replace("""in_layers.2""" ,     """conv1"""    )

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

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

												lowerCamelCase__								=	shave_segments(__lowerCAmelCase ,     n_shave_prefix_segments=__lowerCAmelCase    )

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

						return mapping
def     A__      (					__lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      List[str]=0    ):
						lowerCamelCase__								=	[]
						for old_item in old_list:
												lowerCamelCase__								=	old_item

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

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

												lowerCamelCase__								=	shave_segments(__lowerCAmelCase ,     n_shave_prefix_segments=__lowerCAmelCase    )

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

						return mapping
def     A__      (					__lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      List[str]=None ,     __lowerCAmelCase		:      str=None ,     __lowerCAmelCase		:      Tuple=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():
																		lowerCamelCase__								=	old_checkpoint[path]
																		lowerCamelCase__								=	old_tensor.shape[0] // 3

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

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

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

																		lowerCamelCase__								=	query.reshape(__lowerCAmelCase    )
																		lowerCamelCase__								=	key.reshape(__lowerCAmelCase    )
																		lowerCamelCase__								=	value.reshape(__lowerCAmelCase    )

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

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								lowerCamelCase__								=	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:
																		lowerCamelCase__								=	old_checkpoint[path["""old"""]][:, :, 0]
												else:
																		lowerCamelCase__								=	old_checkpoint[path["""old"""]]
def     A__      (					__lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      List[str]    ):
						lowerCamelCase__								=	{}

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

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

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

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

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

												lowerCamelCase__								=	renew_resnet_paths(__lowerCAmelCase    )
												lowerCamelCase__								=	{"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												lowerCamelCase__								=	{"""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    ):
																		lowerCamelCase__								=	renew_attention_paths(__lowerCAmelCase    )
																		lowerCamelCase__								=	{
																		    """old""": F'''input_blocks.{i}.1''',
																		    """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
																		}
																		lowerCamelCase__								=	{
																		    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 ,     )

						lowerCamelCase__								=	middle_blocks[0]
						lowerCamelCase__								=	middle_blocks[1]
						lowerCamelCase__								=	middle_blocks[2]

						lowerCamelCase__								=	renew_resnet_paths(__lowerCAmelCase    )
						assign_to_checkpoint(__lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase ,     config=__lowerCAmelCase    )

						lowerCamelCase__								=	renew_resnet_paths(__lowerCAmelCase    )
						assign_to_checkpoint(__lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase ,     config=__lowerCAmelCase    )

						lowerCamelCase__								=	renew_attention_paths(__lowerCAmelCase    )
						lowerCamelCase__								=	{
						    """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    ):
												lowerCamelCase__								=	i // (config["""num_res_blocks"""] + 1)
												lowerCamelCase__								=	i % (config["""num_res_blocks"""] + 1)
												lowerCamelCase__								=	[shave_segments(__lowerCAmelCase ,     2    ) for name in output_blocks[i]]
												lowerCamelCase__								=	{}

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

												if len(__lowerCAmelCase    ) > 1:
																		lowerCamelCase__								=	[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
																		lowerCamelCase__								=	[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

																		lowerCamelCase__								=	renew_resnet_paths(__lowerCAmelCase    )
																		lowerCamelCase__								=	renew_resnet_paths(__lowerCAmelCase    )

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

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

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

																								lowerCamelCase__								=	checkpoint[old_path]

						return new_checkpoint


if __name__ == "__main__":
							UpperCamelCase     :   Dict	      =		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     :   List[str]	      =		parser.parse_args()

							UpperCamelCase     :   List[str]	      =		torch.load(args.checkpoint_path)

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

							UpperCamelCase     :   Any	      =		convert_ldm_checkpoint(checkpoint, config)

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

							UpperCamelCase     :   Tuple	      =		UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

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

														UpperCamelCase     :   str	      =		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 pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline


@pytest.mark.integration
def lowerCAmelCase_   (					):






 """simple docstring"""







 with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT			):
  with pytest.raises(UpperCamelCase__			):
   requests.request("""GET"""					, """https://huggingface.co"""			)
  with pytest.raises(requests.exceptions.ConnectTimeout			):
   requests.request("""GET"""					, """https://huggingface.co"""					, timeout=1.0			)


@pytest.mark.integration
def lowerCAmelCase_   (					):






 """simple docstring"""







 with offline(OfflineSimulationMode.CONNECTION_FAILS			):
  with pytest.raises(requests.exceptions.ConnectionError			):
   requests.request("""GET"""					, """https://huggingface.co"""			)


def lowerCAmelCase_   (					):






 """simple docstring"""







 with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1			):
  with pytest.raises(UpperCamelCase__			):
   http_head("""https://huggingface.co"""			)
