import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__=1							)					->     Any:
							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_					(						snake_case__       ,  snake_case__=0							)					->     Dict:
							lowerCAmelCase     		=		[]
							for old_item in old_list:
														lowerCAmelCase     		=		old_item
														lowerCAmelCase     		=		new_item.replace('''block.'''       ,  '''resnets.'''							)
														lowerCAmelCase     		=		new_item.replace('''conv_shorcut'''       ,  '''conv1'''							)
														lowerCAmelCase     		=		new_item.replace('''in_shortcut'''       ,  '''conv_shortcut'''							)
														lowerCAmelCase     		=		new_item.replace('''temb_proj'''       ,  '''time_emb_proj'''							)

														lowerCAmelCase     		=		shave_segments(snake_case__       ,  n_shave_prefix_segments=snake_case__							)

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

							return mapping

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__=0       ,  snake_case__=False							)					->     str:
							lowerCAmelCase     		=		[]
							for old_item in old_list:
														lowerCAmelCase     		=		old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					lowerCAmelCase     		=		new_item.replace('''attn'''       ,  '''attentions'''							)
														lowerCAmelCase     		=		new_item.replace('''.k.'''       ,  '''.key.'''							)
														lowerCAmelCase     		=		new_item.replace('''.v.'''       ,  '''.value.'''							)
														lowerCAmelCase     		=		new_item.replace('''.q.'''       ,  '''.query.'''							)

														lowerCAmelCase     		=		new_item.replace('''proj_out'''       ,  '''proj_attn'''							)
														lowerCAmelCase     		=		new_item.replace('''norm'''       ,  '''group_norm'''							)

														lowerCAmelCase     		=		shave_segments(snake_case__       ,  n_shave_prefix_segments=snake_case__							)
														mapping.append({'''old''': old_item, '''new''': new_item}							)

							return mapping

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__=None       ,  snake_case__=None       ,  snake_case__=None							)					->     Union[str, Any]:
							assert isinstance(snake_case__       ,  snake_case__							), "Paths should be a list of dicts containing 'old' and 'new' keys."

							if attention_paths_to_split is not None:
														if config is None:
																					raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'''							)

														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.get('''num_head_channels'''       ,  1							) // 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(snake_case__							).squeeze()
																					lowerCAmelCase     		=		key.reshape(snake_case__							).squeeze()
																					lowerCAmelCase     		=		value.reshape(snake_case__							).squeeze()

							for path in paths:
														lowerCAmelCase     		=		path['''new''']

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														lowerCAmelCase     		=		new_path.replace('''down.'''       ,  '''down_blocks.'''							)
														lowerCAmelCase     		=		new_path.replace('''up.'''       ,  '''up_blocks.'''							)

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												lowerCAmelCase     		=		new_path.replace(replacement['''old''']       ,  replacement['''new''']							)

														if "attentions" in new_path:
																					lowerCAmelCase     		=		old_checkpoint[path['''old''']].squeeze()
														else:
																					lowerCAmelCase     		=		old_checkpoint[path['''old''']]

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Any:
							lowerCAmelCase     		=		{}

							lowerCAmelCase     		=		checkpoint['''temb.dense.0.weight''']
							lowerCAmelCase     		=		checkpoint['''temb.dense.0.bias''']
							lowerCAmelCase     		=		checkpoint['''temb.dense.1.weight''']
							lowerCAmelCase     		=		checkpoint['''temb.dense.1.bias''']

							lowerCAmelCase     		=		checkpoint['''norm_out.weight''']
							lowerCAmelCase     		=		checkpoint['''norm_out.bias''']

							lowerCAmelCase     		=		checkpoint['''conv_in.weight''']
							lowerCAmelCase     		=		checkpoint['''conv_in.bias''']
							lowerCAmelCase     		=		checkpoint['''conv_out.weight''']
							lowerCAmelCase     		=		checkpoint['''conv_out.bias''']

							lowerCAmelCase     		=		len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''down''' in layer}							)
							lowerCAmelCase     		=		{
							    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(snake_case__							)
							}

							lowerCAmelCase     		=		len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''up''' in layer}							)
							lowerCAmelCase     		=		{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(snake_case__							)}

							for i in range(snake_case__							):
														lowerCAmelCase     		=		(i - 1) // (config['''layers_per_block'''] + 1)

														if any('''downsample''' in layer for layer in down_blocks[i]							):
																					lowerCAmelCase     		=		checkpoint[
																					    f"down.{i}.downsample.op.weight"
																					]
																					lowerCAmelCase     		=		checkpoint[f"down.{i}.downsample.op.bias"]
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

														if any('''block''' in layer for layer in down_blocks[i]							):
																					lowerCAmelCase     		=		len(
																					    {'''.'''.join(shave_segments(snake_case__       ,  2							).split('''.'''							)[:2]							) for layer in down_blocks[i] if '''block''' in layer}							)
																					lowerCAmelCase     		=		{
																					    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
																					    for layer_id in range(snake_case__							)
																					}

																					if num_blocks > 0:
																												for j in range(config['''layers_per_block''']							):
																																			lowerCAmelCase     		=		renew_resnet_paths(blocks[j]							)
																																			assign_to_checkpoint(snake_case__       ,  snake_case__       ,  snake_case__							)

														if any('''attn''' in layer for layer in down_blocks[i]							):
																					lowerCAmelCase     		=		len(
																					    {'''.'''.join(shave_segments(snake_case__       ,  2							).split('''.'''							)[:2]							) for layer in down_blocks[i] if '''attn''' in layer}							)
																					lowerCAmelCase     		=		{
																					    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
																					    for layer_id in range(snake_case__							)
																					}

																					if num_attn > 0:
																												for j in range(config['''layers_per_block''']							):
																																			lowerCAmelCase     		=		renew_attention_paths(attns[j]							)
																																			assign_to_checkpoint(snake_case__       ,  snake_case__       ,  snake_case__       ,  config=snake_case__							)

							lowerCAmelCase     		=		[key for key in checkpoint if '''mid.block_1''' in key]
							lowerCAmelCase     		=		[key for key in checkpoint if '''mid.block_2''' in key]
							lowerCAmelCase     		=		[key for key in checkpoint if '''mid.attn_1''' in key]

							# Mid new 2
							lowerCAmelCase     		=		renew_resnet_paths(snake_case__							)
							assign_to_checkpoint(
							    snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]       ,  )

							lowerCAmelCase     		=		renew_resnet_paths(snake_case__							)
							assign_to_checkpoint(
							    snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]       ,  )

							lowerCAmelCase     		=		renew_attention_paths(snake_case__       ,  in_mid=snake_case__							)
							assign_to_checkpoint(
							    snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]       ,  )

							for i in range(snake_case__							):
														lowerCAmelCase     		=		num_up_blocks - 1 - i

														if any('''upsample''' in layer for layer in up_blocks[i]							):
																					lowerCAmelCase     		=		checkpoint[
																					    f"up.{i}.upsample.conv.weight"
																					]
																					lowerCAmelCase     		=		checkpoint[f"up.{i}.upsample.conv.bias"]

														if any('''block''' in layer for layer in up_blocks[i]							):
																					lowerCAmelCase     		=		len(
																					    {'''.'''.join(shave_segments(snake_case__       ,  2							).split('''.'''							)[:2]							) for layer in up_blocks[i] if '''block''' in layer}							)
																					lowerCAmelCase     		=		{
																					    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(snake_case__							)
																					}

																					if num_blocks > 0:
																												for j in range(config['''layers_per_block'''] + 1							):
																																			lowerCAmelCase     		=		{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
																																			lowerCAmelCase     		=		renew_resnet_paths(blocks[j]							)
																																			assign_to_checkpoint(snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[replace_indices]							)

														if any('''attn''' in layer for layer in up_blocks[i]							):
																					lowerCAmelCase     		=		len(
																					    {'''.'''.join(shave_segments(snake_case__       ,  2							).split('''.'''							)[:2]							) for layer in up_blocks[i] if '''attn''' in layer}							)
																					lowerCAmelCase     		=		{
																					    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(snake_case__							)
																					}

																					if num_attn > 0:
																												for j in range(config['''layers_per_block'''] + 1							):
																																			lowerCAmelCase     		=		{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
																																			lowerCAmelCase     		=		renew_attention_paths(attns[j]							)
																																			assign_to_checkpoint(snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[replace_indices]							)

							lowerCAmelCase     		=		{k.replace('''mid_new_2'''       ,  '''mid_block'''							): v for k, v in new_checkpoint.items()}
							return new_checkpoint

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     str:
							lowerCAmelCase     		=		{}

							lowerCAmelCase     		=		checkpoint['''encoder.norm_out.weight''']
							lowerCAmelCase     		=		checkpoint['''encoder.norm_out.bias''']

							lowerCAmelCase     		=		checkpoint['''encoder.conv_in.weight''']
							lowerCAmelCase     		=		checkpoint['''encoder.conv_in.bias''']
							lowerCAmelCase     		=		checkpoint['''encoder.conv_out.weight''']
							lowerCAmelCase     		=		checkpoint['''encoder.conv_out.bias''']

							lowerCAmelCase     		=		checkpoint['''decoder.norm_out.weight''']
							lowerCAmelCase     		=		checkpoint['''decoder.norm_out.bias''']

							lowerCAmelCase     		=		checkpoint['''decoder.conv_in.weight''']
							lowerCAmelCase     		=		checkpoint['''decoder.conv_in.bias''']
							lowerCAmelCase     		=		checkpoint['''decoder.conv_out.weight''']
							lowerCAmelCase     		=		checkpoint['''decoder.conv_out.bias''']

							lowerCAmelCase     		=		len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in checkpoint if '''down''' in layer}							)
							lowerCAmelCase     		=		{
							    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(snake_case__							)
							}

							lowerCAmelCase     		=		len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in checkpoint if '''up''' in layer}							)
							lowerCAmelCase     		=		{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(snake_case__							)}

							for i in range(snake_case__							):
														lowerCAmelCase     		=		(i - 1) // (config['''layers_per_block'''] + 1)

														if any('''downsample''' in layer for layer in down_blocks[i]							):
																					lowerCAmelCase     		=		checkpoint[
																					    f"encoder.down.{i}.downsample.conv.weight"
																					]
																					lowerCAmelCase     		=		checkpoint[
																					    f"encoder.down.{i}.downsample.conv.bias"
																					]

														if any('''block''' in layer for layer in down_blocks[i]							):
																					lowerCAmelCase     		=		len(
																					    {'''.'''.join(shave_segments(snake_case__       ,  3							).split('''.'''							)[:3]							) for layer in down_blocks[i] if '''block''' in layer}							)
																					lowerCAmelCase     		=		{
																					    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
																					    for layer_id in range(snake_case__							)
																					}

																					if num_blocks > 0:
																												for j in range(config['''layers_per_block''']							):
																																			lowerCAmelCase     		=		renew_resnet_paths(blocks[j]							)
																																			assign_to_checkpoint(snake_case__       ,  snake_case__       ,  snake_case__							)

														if any('''attn''' in layer for layer in down_blocks[i]							):
																					lowerCAmelCase     		=		len(
																					    {'''.'''.join(shave_segments(snake_case__       ,  3							).split('''.'''							)[:3]							) for layer in down_blocks[i] if '''attn''' in layer}							)
																					lowerCAmelCase     		=		{
																					    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
																					    for layer_id in range(snake_case__							)
																					}

																					if num_attn > 0:
																												for j in range(config['''layers_per_block''']							):
																																			lowerCAmelCase     		=		renew_attention_paths(attns[j]							)
																																			assign_to_checkpoint(snake_case__       ,  snake_case__       ,  snake_case__       ,  config=snake_case__							)

							lowerCAmelCase     		=		[key for key in checkpoint if '''mid.block_1''' in key]
							lowerCAmelCase     		=		[key for key in checkpoint if '''mid.block_2''' in key]
							lowerCAmelCase     		=		[key for key in checkpoint if '''mid.attn_1''' in key]

							# Mid new 2
							lowerCAmelCase     		=		renew_resnet_paths(snake_case__							)
							assign_to_checkpoint(
							    snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]       ,  )

							lowerCAmelCase     		=		renew_resnet_paths(snake_case__							)
							assign_to_checkpoint(
							    snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]       ,  )

							lowerCAmelCase     		=		renew_attention_paths(snake_case__       ,  in_mid=snake_case__							)
							assign_to_checkpoint(
							    snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]       ,  )

							for i in range(snake_case__							):
														lowerCAmelCase     		=		num_up_blocks - 1 - i

														if any('''upsample''' in layer for layer in up_blocks[i]							):
																					lowerCAmelCase     		=		checkpoint[
																					    f"decoder.up.{i}.upsample.conv.weight"
																					]
																					lowerCAmelCase     		=		checkpoint[
																					    f"decoder.up.{i}.upsample.conv.bias"
																					]

														if any('''block''' in layer for layer in up_blocks[i]							):
																					lowerCAmelCase     		=		len(
																					    {'''.'''.join(shave_segments(snake_case__       ,  3							).split('''.'''							)[:3]							) for layer in up_blocks[i] if '''block''' in layer}							)
																					lowerCAmelCase     		=		{
																					    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(snake_case__							)
																					}

																					if num_blocks > 0:
																												for j in range(config['''layers_per_block'''] + 1							):
																																			lowerCAmelCase     		=		{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
																																			lowerCAmelCase     		=		renew_resnet_paths(blocks[j]							)
																																			assign_to_checkpoint(snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[replace_indices]							)

														if any('''attn''' in layer for layer in up_blocks[i]							):
																					lowerCAmelCase     		=		len(
																					    {'''.'''.join(shave_segments(snake_case__       ,  3							).split('''.'''							)[:3]							) for layer in up_blocks[i] if '''attn''' in layer}							)
																					lowerCAmelCase     		=		{
																					    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(snake_case__							)
																					}

																					if num_attn > 0:
																												for j in range(config['''layers_per_block'''] + 1							):
																																			lowerCAmelCase     		=		{'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
																																			lowerCAmelCase     		=		renew_attention_paths(attns[j]							)
																																			assign_to_checkpoint(snake_case__       ,  snake_case__       ,  snake_case__       ,  additional_replacements=[replace_indices]							)

							lowerCAmelCase     		=		{k.replace('''mid_new_2'''       ,  '''mid_block'''							): v for k, v in new_checkpoint.items()}
							lowerCAmelCase     		=		checkpoint['''quant_conv.weight''']
							lowerCAmelCase     		=		checkpoint['''quant_conv.bias''']
							if "quantize.embedding.weight" in checkpoint:
														lowerCAmelCase     		=		checkpoint['''quantize.embedding.weight''']
							lowerCAmelCase     		=		checkpoint['''post_quant_conv.weight''']
							lowerCAmelCase     		=		checkpoint['''post_quant_conv.bias''']

							return new_checkpoint


if __name__ == "__main__":
	lowercase__      :    Any          =     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.''')

	lowercase__      :    int          =     parser.parse_args()
	lowercase__      :    Tuple          =     torch.load(args.checkpoint_path)

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

	# unet case
	lowercase__      :    List[str]          =     {key.split('''.''')[0] for key in checkpoint.keys()}
	if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
		lowercase__      :    str          =     convert_vq_autoenc_checkpoint(checkpoint, config)
	else:
		lowercase__      :    Optional[int]          =     convert_ddpm_checkpoint(checkpoint, config)

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

	if config["_class_name"] == "VQModel":
		lowercase__      :    Union[str, Any]          =     VQModel(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	elif config["_class_name"] == "AutoencoderKL":
		lowercase__      :    Optional[int]          =     AutoencoderKL(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	else:
		lowercase__      :    Any          =     UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		lowercase__      :    int          =     DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

		lowercase__      :    Optional[Any]          =     DDPMPipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)




from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__      :    int          =     load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
lowercase__     ,   lowercase__     ,   lowercase__     ,   lowercase__      :    Any          =     train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

lowercase__      :    int          =     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__      :    List[str]          =     [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__      :    int          =     knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)



