




'''simple docstring'''






import argparse

import torch

from diffusers import UNetaDConditionModel

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__=None ,				snake_case__=None ,				snake_case__=None						):
			assert isinstance(snake_case__ ,				snake_case__						), "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				:      Optional[Any]									=       old_checkpoint[path]
									__UpperCamelCase				:      Tuple									=       old_tensor.shape[0] // 3

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

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

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

									__UpperCamelCase				:      Optional[Any]									=       query.reshape(snake_case__						)
									__UpperCamelCase				:      Dict									=       key.reshape(snake_case__						)
									__UpperCamelCase				:      Optional[int]									=       value.reshape(snake_case__						)

			for path in paths:
						__UpperCamelCase				:      Optional[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

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

        # proj_attn.weight has to be converted from conv 1D to linear
						__UpperCamelCase				:      Any									=       old_checkpoint[path["old"]]
						__UpperCamelCase				:      Any									=       ["proj_attn.weight"]
						__UpperCamelCase				:      Optional[int]									=       ["proj_out.weight", "proj_in.weight"]
						if any(k in new_path for k in names						):
									__UpperCamelCase				:      List[Any]									=       weight[:, :, 0]
						elif any(k in new_path for k in names_a						) and len(weight.shape						) > 2 and ".attentions." not in new_path:
									__UpperCamelCase				:      Union[str, Any]									=       weight[:, :, 0]
						else:
									__UpperCamelCase				:      int									=       weight

def       __lowerCAmelCase (   snake_case__ ,				snake_case__=0						):
			__UpperCamelCase				:      str									=       []
			for old_item in old_list:
						__UpperCamelCase				:      List[str]									=       old_item

						#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
						#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

						#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
						#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

						#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

			return mapping

def       __lowerCAmelCase (   snake_case__ ,				snake_case__=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       __lowerCAmelCase (   snake_case__ ,				snake_case__=0						):
			__UpperCamelCase				:      Optional[int]									=       []
			for old_item in old_list:
						mapping.append({"old": old_item, "new": old_item}						)

			return mapping

def       __lowerCAmelCase (   snake_case__ ,				snake_case__=0						):
			__UpperCamelCase				:      Optional[Any]									=       []
			for old_item in old_list:
						__UpperCamelCase				:      Optional[Any]									=       old_item.replace("in_layers.0" ,				"norm1"						)
						__UpperCamelCase				:      Any									=       new_item.replace("in_layers.2" ,				"conv1"						)

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

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

						__UpperCamelCase				:      str									=       shave_segments(snake_case__ ,				n_shave_prefix_segments=snake_case__						)

						if "temopral_conv" not in old_item:
									mapping.append({"old": old_item, "new": new_item}						)

			return mapping

def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__=None ,				snake_case__=False						):
			__UpperCamelCase				:      Union[str, Any]									=       {}
			__UpperCamelCase				:      int									=       list(checkpoint.keys()						)

			__UpperCamelCase				:      Optional[Any]									=       "model.diffusion_model."

			# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
			if sum(k.startswith("model_ema"						) for k in keys						) > 100 and extract_ema:
						print(F"Checkpoint {path} has both EMA and non-EMA weights."						)
						print(
						    "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
						    " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."						)
						for key in keys:
									if key.startswith("model.diffusion_model"						):
												__UpperCamelCase				:      Any									=       "model_ema." + "".join(key.split("."						)[1:]						)
												__UpperCamelCase				:      str									=       checkpoint.pop(snake_case__						)
			else:
						if sum(k.startswith("model_ema"						) for k in keys						) > 100:
									print(
									    "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
									    " weights (usually better for inference), please make sure to add the `--extract_ema` flag."						)

						for key in keys:
									__UpperCamelCase				:      str									=       checkpoint.pop(snake_case__						)

			__UpperCamelCase				:      Dict									=       {}

			__UpperCamelCase				:      Tuple									=       unet_state_dict["time_embed.0.weight"]
			__UpperCamelCase				:      Optional[Any]									=       unet_state_dict["time_embed.0.bias"]
			__UpperCamelCase				:      List[str]									=       unet_state_dict["time_embed.2.weight"]
			__UpperCamelCase				:      List[str]									=       unet_state_dict["time_embed.2.bias"]

			if config["class_embed_type"] is None:
						# No parameters to port
						...
			elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
						__UpperCamelCase				:      int									=       unet_state_dict["label_emb.0.0.weight"]
						__UpperCamelCase				:      int									=       unet_state_dict["label_emb.0.0.bias"]
						__UpperCamelCase				:      Union[str, Any]									=       unet_state_dict["label_emb.0.2.weight"]
						__UpperCamelCase				:      Dict									=       unet_state_dict["label_emb.0.2.bias"]
			else:
						raise NotImplementedError(F"Not implemented `class_embed_type`: {config['class_embed_type']}"						)

			__UpperCamelCase				:      Tuple									=       unet_state_dict["input_blocks.0.0.weight"]
			__UpperCamelCase				:      Union[str, Any]									=       unet_state_dict["input_blocks.0.0.bias"]

			__UpperCamelCase				:      Optional[Any]									=       [v for v in unet_state_dict if v.startswith("input_blocks.0.1"						)]
			__UpperCamelCase				:      List[str]									=       renew_attention_paths(snake_case__						)
			__UpperCamelCase				:      Any									=       {"old": "input_blocks.0.1", "new": "transformer_in"}
			assign_to_checkpoint(snake_case__ ,				snake_case__ ,				snake_case__ ,				additional_replacements=[meta_path] ,				config=snake_case__						)

			__UpperCamelCase				:      str									=       unet_state_dict["out.0.weight"]
			__UpperCamelCase				:      str									=       unet_state_dict["out.0.bias"]
			__UpperCamelCase				:      Union[str, Any]									=       unet_state_dict["out.2.weight"]
			__UpperCamelCase				:      str									=       unet_state_dict["out.2.bias"]

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

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

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

			for i in range(1 ,				snake_case__						):
						__UpperCamelCase				:      Any									=       (i - 1) // (config["layers_per_block"] + 1)
						__UpperCamelCase				:      List[Any]									=       (i - 1) % (config["layers_per_block"] + 1)

						__UpperCamelCase				:      int									=       [
						    key for key in input_blocks[i] if F"input_blocks.{i}.0" in key and F"input_blocks.{i}.0.op" not in key
						]
						__UpperCamelCase				:      List[Any]									=       [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]
						__UpperCamelCase				:      List[Any]									=       [key for key in input_blocks[i] if F"input_blocks.{i}.2" in key]

						if F"input_blocks.{i}.op.weight" in unet_state_dict:
									__UpperCamelCase				:      List[str]									=       unet_state_dict.pop(
									    F"input_blocks.{i}.op.weight"						)
									__UpperCamelCase				:      Optional[Any]									=       unet_state_dict.pop(
									    F"input_blocks.{i}.op.bias"						)

						__UpperCamelCase				:      Optional[int]									=       renew_resnet_paths(snake_case__						)
						__UpperCamelCase				:      str									=       {"old": F"input_blocks.{i}.0", "new": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
						assign_to_checkpoint(
						    snake_case__ ,				snake_case__ ,				snake_case__ ,				additional_replacements=[meta_path] ,				config=snake_case__						)

						__UpperCamelCase				:      Any									=       [key for key in resnets if "temopral_conv" in key]
						__UpperCamelCase				:      List[str]									=       renew_temp_conv_paths(snake_case__						)
						__UpperCamelCase				:      Union[str, Any]									=       {
						    "old": F"input_blocks.{i}.0.temopral_conv",
						    "new": F"down_blocks.{block_id}.temp_convs.{layer_in_block_id}",
						}
						assign_to_checkpoint(
						    snake_case__ ,				snake_case__ ,				snake_case__ ,				additional_replacements=[meta_path] ,				config=snake_case__						)

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

						if len(snake_case__						):
									__UpperCamelCase				:      int									=       renew_attention_paths(snake_case__						)
									__UpperCamelCase				:      List[Any]									=       {
									    "old": F"input_blocks.{i}.2",
									    "new": F"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
									}
									assign_to_checkpoint(
									    snake_case__ ,				snake_case__ ,				snake_case__ ,				additional_replacements=[meta_path] ,				config=snake_case__						)

			__UpperCamelCase				:      List[str]									=       middle_blocks[0]
			__UpperCamelCase				:      Optional[Any]									=       [key for key in resnet_a if "temopral_conv" in key]
			__UpperCamelCase				:      Any									=       middle_blocks[1]
			__UpperCamelCase				:      str									=       middle_blocks[2]
			__UpperCamelCase				:      str									=       middle_blocks[3]
			__UpperCamelCase				:      List[str]									=       [key for key in resnet_a if "temopral_conv" in key]

			__UpperCamelCase				:      Any									=       renew_resnet_paths(snake_case__						)
			__UpperCamelCase				:      Optional[Any]									=       {"old": "middle_block.0", "new": "mid_block.resnets.0"}
			assign_to_checkpoint(
			    snake_case__ ,				snake_case__ ,				snake_case__ ,				config=snake_case__ ,				additional_replacements=[meta_path]						)

			__UpperCamelCase				:      int									=       renew_temp_conv_paths(snake_case__						)
			__UpperCamelCase				:      Optional[Any]									=       {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
			assign_to_checkpoint(
			    snake_case__ ,				snake_case__ ,				snake_case__ ,				config=snake_case__ ,				additional_replacements=[meta_path]						)

			__UpperCamelCase				:      Optional[int]									=       renew_resnet_paths(snake_case__						)
			__UpperCamelCase				:      List[Any]									=       {"old": "middle_block.3", "new": "mid_block.resnets.1"}
			assign_to_checkpoint(
			    snake_case__ ,				snake_case__ ,				snake_case__ ,				config=snake_case__ ,				additional_replacements=[meta_path]						)

			__UpperCamelCase				:      str									=       renew_temp_conv_paths(snake_case__						)
			__UpperCamelCase				:      Tuple									=       {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
			assign_to_checkpoint(
			    snake_case__ ,				snake_case__ ,				snake_case__ ,				config=snake_case__ ,				additional_replacements=[meta_path]						)

			__UpperCamelCase				:      List[Any]									=       renew_attention_paths(snake_case__						)
			__UpperCamelCase				:      int									=       {"old": "middle_block.1", "new": "mid_block.attentions.0"}
			assign_to_checkpoint(
			    snake_case__ ,				snake_case__ ,				snake_case__ ,				additional_replacements=[meta_path] ,				config=snake_case__						)

			__UpperCamelCase				:      List[Any]									=       renew_attention_paths(snake_case__						)
			__UpperCamelCase				:      List[Any]									=       {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
			assign_to_checkpoint(
			    snake_case__ ,				snake_case__ ,				snake_case__ ,				additional_replacements=[meta_path] ,				config=snake_case__						)

			for i in range(snake_case__						):
						__UpperCamelCase				:      Dict									=       i // (config["layers_per_block"] + 1)
						__UpperCamelCase				:      List[Any]									=       i % (config["layers_per_block"] + 1)
						__UpperCamelCase				:      Tuple									=       [shave_segments(snake_case__ ,				2						) for name in output_blocks[i]]
						__UpperCamelCase				:      str									=       {}

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

						if len(snake_case__						) > 1:
									__UpperCamelCase				:      int									=       [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
									__UpperCamelCase				:      Tuple									=       [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]
									__UpperCamelCase				:      Tuple									=       [key for key in output_blocks[i] if F"output_blocks.{i}.2" in key]

									__UpperCamelCase				:      Tuple									=       renew_resnet_paths(snake_case__						)
									__UpperCamelCase				:      Tuple									=       renew_resnet_paths(snake_case__						)

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

									__UpperCamelCase				:      Optional[int]									=       [key for key in resnets if "temopral_conv" in key]
									__UpperCamelCase				:      Dict									=       renew_temp_conv_paths(snake_case__						)
									__UpperCamelCase				:      str									=       {
									    "old": F"output_blocks.{i}.0.temopral_conv",
									    "new": F"up_blocks.{block_id}.temp_convs.{layer_in_block_id}",
									}
									assign_to_checkpoint(
									    snake_case__ ,				snake_case__ ,				snake_case__ ,				additional_replacements=[meta_path] ,				config=snake_case__						)

									__UpperCamelCase				:      List[str]									=       {k: sorted(snake_case__						) for k, v in output_block_list.items()}
									if ["conv.bias", "conv.weight"] in output_block_list.values():
												__UpperCamelCase				:      str									=       list(output_block_list.values()						).index(["conv.bias", "conv.weight"]						)
												__UpperCamelCase				:      Tuple									=       unet_state_dict[
												    F"output_blocks.{i}.{index}.conv.weight"
												]
												__UpperCamelCase				:      List[str]									=       unet_state_dict[
												    F"output_blocks.{i}.{index}.conv.bias"
												]

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

									if len(snake_case__						):
												__UpperCamelCase				:      int									=       renew_attention_paths(snake_case__						)
												__UpperCamelCase				:      List[Any]									=       {
												    "old": F"output_blocks.{i}.1",
												    "new": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
												}
												assign_to_checkpoint(
												    snake_case__ ,				snake_case__ ,				snake_case__ ,				additional_replacements=[meta_path] ,				config=snake_case__						)

									if len(snake_case__						):
												__UpperCamelCase				:      Optional[Any]									=       renew_attention_paths(snake_case__						)
												__UpperCamelCase				:      Any									=       {
												    "old": F"output_blocks.{i}.2",
												    "new": F"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
												}
												assign_to_checkpoint(
												    snake_case__ ,				snake_case__ ,				snake_case__ ,				additional_replacements=[meta_path] ,				config=snake_case__						)
						else:
									__UpperCamelCase				:      Dict									=       renew_resnet_paths(snake_case__ ,				n_shave_prefix_segments=1						)
									for path in resnet_0_paths:
												__UpperCamelCase				:      Union[str, Any]									=       ".".join(["output_blocks", str(snake_case__						), path["old"]]						)
												__UpperCamelCase				:      Tuple									=       ".".join(["up_blocks", str(snake_case__						), "resnets", str(snake_case__						), path["new"]]						)
												__UpperCamelCase				:      int									=       unet_state_dict[old_path]

									__UpperCamelCase				:      int									=       [l for l in output_block_layers if "temopral_conv" in l]
									for path in temopral_conv_paths:
												__UpperCamelCase				:      Tuple									=       path.split("temopral_conv."						)[-1]
												__UpperCamelCase				:      Dict									=       ".".join(["output_blocks", str(snake_case__						), str(snake_case__						), "temopral_conv", pruned_path]						)
												__UpperCamelCase				:      Optional[int]									=       ".".join(["up_blocks", str(snake_case__						), "temp_convs", str(snake_case__						), pruned_path]						)
												__UpperCamelCase				:      Optional[int]									=       unet_state_dict[old_path]

			return new_checkpoint


if __name__ == "__main__":
				_lowerCAmelCase				     = argparse.ArgumentParser()

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

				_lowerCAmelCase				     = torch.load(args.checkpoint_path, map_location='''cpu''')
				_lowerCAmelCase				     = UNetaDConditionModel()

				_lowerCAmelCase				     = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

				_lowerCAmelCase				     = set(unet.state_dict().keys()) - set(converted_ckpt.keys())
				_lowerCAmelCase				     = set(converted_ckpt.keys()) - set(unet.state_dict().keys())

				assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

				# load state_dict
				unet.load_state_dict(converted_ckpt)

				unet.save_pretrained(args.dump_path)

				# -- finish converting the unet --









'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase				     = {'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase				     = [
				    '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''VisualBertForMultipleChoice''',
				    '''VisualBertForPreTraining''',
				    '''VisualBertForQuestionAnswering''',
				    '''VisualBertForRegionToPhraseAlignment''',
				    '''VisualBertForVisualReasoning''',
				    '''VisualBertLayer''',
				    '''VisualBertModel''',
				    '''VisualBertPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_visual_bert import (
								    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VisualBertForMultipleChoice,
								    VisualBertForPreTraining,
								    VisualBertForQuestionAnswering,
								    VisualBertForRegionToPhraseAlignment,
								    VisualBertForVisualReasoning,
								    VisualBertLayer,
								    VisualBertModel,
								    VisualBertPreTrainedModel,
								)


else:
				import sys

				_lowerCAmelCase				     = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



