



'''simple docstring'''

import argparse
import json

import torch

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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						int=1							)			->						Tuple:







	"""simple docstring"""

	if n_shave_prefix_segments >= 0:
		return ".".join(path.split("""."""							)[n_shave_prefix_segments:]							)
	else:
		return ".".join(path.split("""."""							)[:n_shave_prefix_segments]							)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						Optional[int]=0							)			->						Optional[int]:







	"""simple docstring"""
	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(lowercase_							,		n_shave_prefix_segments=lowercase_							)

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

	return mapping






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						Dict=0							,		_SCREAMING_SNAKE_CASE     :						Any=False							)			->						Union[str, Any]:







	"""simple docstring"""
	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(lowercase_							,		n_shave_prefix_segments=lowercase_							)
		mapping.append({"""old""": old_item, """new""": new_item}							)

	return mapping






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						str=None							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]=None							,		_SCREAMING_SNAKE_CASE     :						Tuple=None							)			->						Union[str, Any]:







	"""simple docstring"""
	assert isinstance(lowercase_							,		lowercase_							), "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    =	old_tensor.split(channels // num_heads							,		dim=1							)

			lowerCAmelCase    =	query.reshape(lowercase_							).squeeze()
			lowerCAmelCase    =	key.reshape(lowercase_							).squeeze()
			lowerCAmelCase    =	value.reshape(lowercase_							).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 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						List[Any]							,		_SCREAMING_SNAKE_CASE     :						Optional[int]							)			->						int:







	"""simple docstring"""
	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(lowercase_							)
	}

	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(lowercase_							)}

	for i in range(lowercase_							):
		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(lowercase_							,		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(lowercase_							)
			}

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

		if any("""attn""" in layer for layer in down_blocks[i]							):
			lowerCAmelCase    =	len(
			    {""".""".join(shave_segments(lowercase_							,		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(lowercase_							)
			}

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

	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(lowercase_							)
	assign_to_checkpoint(
	    lowercase_							,		lowercase_							,		lowercase_							,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]							,		)

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

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

	for i in range(lowercase_							):
		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(lowercase_							,		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(lowercase_							)
			}

			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(lowercase_							,		lowercase_							,		lowercase_							,		additional_replacements=[replace_indices]							)

		if any("""attn""" in layer for layer in up_blocks[i]							):
			lowerCAmelCase    =	len(
			    {""".""".join(shave_segments(lowercase_							,		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(lowercase_							)
			}

			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(lowercase_							,		lowercase_							,		lowercase_							,		additional_replacements=[replace_indices]							)

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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Tuple							)			->						Optional[int]:







	"""simple docstring"""
	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(lowercase_							)
	}

	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(lowercase_							)}

	for i in range(lowercase_							):
		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(lowercase_							,		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(lowercase_							)
			}

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

		if any("""attn""" in layer for layer in down_blocks[i]							):
			lowerCAmelCase    =	len(
			    {""".""".join(shave_segments(lowercase_							,		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(lowercase_							)
			}

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

	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(lowercase_							)
	assign_to_checkpoint(
	    lowercase_							,		lowercase_							,		lowercase_							,		additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]							,		)

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

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

	for i in range(lowercase_							):
		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(lowercase_							,		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(lowercase_							)
			}

			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(lowercase_							,		lowercase_							,		lowercase_							,		additional_replacements=[replace_indices]							)

		if any("""attn""" in layer for layer in up_blocks[i]							):
			lowerCAmelCase    =	len(
			    {""".""".join(shave_segments(lowercase_							,		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(lowercase_							)
			}

			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(lowercase_							,		lowercase_							,		lowercase_							,		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__":
				UpperCAmelCase			=    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			=    parser.parse_args()
				UpperCAmelCase			=    torch.load(args.checkpoint_path)

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

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

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

				if config["_class_name"] == "VQModel":
								UpperCAmelCase			=    VQModel(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				elif config["_class_name"] == "AutoencoderKL":
								UpperCAmelCase			=    AutoencoderKL(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				else:
								UpperCAmelCase			=    UNetaDModel(**config)
								model.load_state_dict(converted_checkpoint)

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

								UpperCAmelCase			=    DDPMPipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)






"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

      # Save pytorch-model
      _UpperCamelCase					:		str  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _UpperCamelCase					:		Any  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
      torch.save(model.state_dict()						,lowercase_	)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
      with open(lowercase_						,"w"						,encoding="utf-8"	) as f:
            f.write(config.to_json_string()	)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
