"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__   			=      {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      ['MPNetTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MPNetForMaskedLM',
     'MPNetForMultipleChoice',
     'MPNetForQuestionAnswering',
     'MPNetForSequenceClassification',
     'MPNetForTokenClassification',
     'MPNetLayer',
     'MPNetModel',
     'MPNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMPNetEmbeddings',
     'TFMPNetForMaskedLM',
     'TFMPNetForMultipleChoice',
     'TFMPNetForQuestionAnswering',
     'TFMPNetForSequenceClassification',
     'TFMPNetForTokenClassification',
     'TFMPNetMainLayer',
     'TFMPNetModel',
     'TFMPNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

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









import argparse

import torch

from diffusers import UNetaDConditionModel

def      a    (   _lowercase :  Dict			,    _lowercase :  Dict			,    _lowercase :  Any			,    _lowercase :  Tuple=None			,    _lowercase :  List[Any]=None			,    _lowercase :  Optional[Any]=None							)  ->Any:
		assert isinstance(_lowercase			,    _lowercase							), "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(_lowercase							)
						UpperCamelCase__								=						key.reshape(_lowercase							)
						UpperCamelCase__								=						value.reshape(_lowercase							)

		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

				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
				UpperCamelCase__								=						old_checkpoint[path["""old"""]]
				UpperCamelCase__								=						["""proj_attn.weight"""]
				UpperCamelCase__								=						["""proj_out.weight""", """proj_in.weight"""]
				if any(k in new_path for k in names							):
						UpperCamelCase__								=						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__								=						weight[:, :, 0]
				else:
						UpperCamelCase__								=						weight

def      a    (   _lowercase :  Tuple			,    _lowercase :  List[Any]=0							)  ->int:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				UpperCamelCase__								=						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      a    (   _lowercase :  Union[str, Any]			,    _lowercase :  int=1							)  ->str:
		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    (   _lowercase :  List[str]			,    _lowercase :  Any=0							)  ->Any:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				mapping.append({"""old""": old_item, """new""": old_item}							)

		return mapping

def      a    (   _lowercase :  List[str]			,    _lowercase :  int=0							)  ->Tuple:
		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(_lowercase			,    n_shave_prefix_segments=_lowercase							)

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

		return mapping

def      a    (   _lowercase :  List[Any]			,    _lowercase :  List[Any]			,    _lowercase :  int=None			,    _lowercase :  Optional[Any]=False							)  ->List[str]:
		UpperCamelCase__								=						{}
		UpperCamelCase__								=						list(checkpoint.keys()							)

		UpperCamelCase__								=						"""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							) > 1_0_0 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__								=						"""model_ema.""" + """""".join(key.split("""."""							)[1:]							)
								UpperCamelCase__								=						checkpoint.pop(_lowercase							)
		else:
				if sum(k.startswith("""model_ema"""							) for k in keys							) > 1_0_0:
						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__								=						checkpoint.pop(_lowercase							)

		UpperCamelCase__								=						{}

		UpperCamelCase__								=						unet_state_dict["""time_embed.0.weight"""]
		UpperCamelCase__								=						unet_state_dict["""time_embed.0.bias"""]
		UpperCamelCase__								=						unet_state_dict["""time_embed.2.weight"""]
		UpperCamelCase__								=						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__								=						unet_state_dict["""label_emb.0.0.weight"""]
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.0.bias"""]
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.2.weight"""]
				UpperCamelCase__								=						unet_state_dict["""label_emb.0.2.bias"""]
		else:
				raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}"""							)

		UpperCamelCase__								=						unet_state_dict["""input_blocks.0.0.weight"""]
		UpperCamelCase__								=						unet_state_dict["""input_blocks.0.0.bias"""]

		UpperCamelCase__								=						[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""							)]
		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
		assign_to_checkpoint(_lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		UpperCamelCase__								=						unet_state_dict["""out.0.weight"""]
		UpperCamelCase__								=						unet_state_dict["""out.0.bias"""]
		UpperCamelCase__								=						unet_state_dict["""out.2.weight"""]
		UpperCamelCase__								=						unet_state_dict["""out.2.bias"""]

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

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

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

		for i in range(1			,    _lowercase							):
				UpperCamelCase__								=						(i - 1) // (config["""layers_per_block"""] + 1)
				UpperCamelCase__								=						(i - 1) % (config["""layers_per_block"""] + 1)

				UpperCamelCase__								=						[
				    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__								=						[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
				UpperCamelCase__								=						[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__								=						unet_state_dict.pop(
						    F"""input_blocks.{i}.op.weight"""							)
						UpperCamelCase__								=						unet_state_dict.pop(
						    F"""input_blocks.{i}.op.bias"""							)

				UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
				UpperCamelCase__								=						{"""old""": F"""input_blocks.{i}.0""", """new""": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
				assign_to_checkpoint(
				    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

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

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

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

		UpperCamelCase__								=						middle_blocks[0]
		UpperCamelCase__								=						[key for key in resnet_a if """temopral_conv""" in key]
		UpperCamelCase__								=						middle_blocks[1]
		UpperCamelCase__								=						middle_blocks[2]
		UpperCamelCase__								=						middle_blocks[3]
		UpperCamelCase__								=						[key for key in resnet_a if """temopral_conv""" in key]

		UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

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

		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

		for i in range(_lowercase							):
				UpperCamelCase__								=						i // (config["""layers_per_block"""] + 1)
				UpperCamelCase__								=						i % (config["""layers_per_block"""] + 1)
				UpperCamelCase__								=						[shave_segments(_lowercase			,    2							) for name in output_blocks[i]]
				UpperCamelCase__								=						{}

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

				if len(_lowercase							) > 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__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

						UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
						UpperCamelCase__								=						renew_resnet_paths(_lowercase							)

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

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

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

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

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

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

						UpperCamelCase__								=						[l for l in output_block_layers if """temopral_conv""" in l]
						for path in temopral_conv_paths:
								UpperCamelCase__								=						path.split("""temopral_conv."""							)[-1]
								UpperCamelCase__								=						""".""".join(["""output_blocks""", str(_lowercase							), str(_lowercase							), """temopral_conv""", pruned_path]							)
								UpperCamelCase__								=						""".""".join(["""up_blocks""", str(_lowercase							), """temp_convs""", str(_lowercase							), pruned_path]							)
								UpperCamelCase__								=						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 --





