





import argparse
import json

import torch

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






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							List[str]=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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Dict   ,				UpperCamelCase__:							str=0      ):
		SCREAMING_SNAKE_CASE__								= []
		for old_item in old_list:
				SCREAMING_SNAKE_CASE__								= old_item
				SCREAMING_SNAKE_CASE__								= new_item.replace("""block."""   ,				"""resnets."""      )
				SCREAMING_SNAKE_CASE__								= new_item.replace("""conv_shorcut"""   ,				"""conv1"""      )
				SCREAMING_SNAKE_CASE__								= new_item.replace("""in_shortcut"""   ,				"""conv_shortcut"""      )
				SCREAMING_SNAKE_CASE__								= new_item.replace("""temb_proj"""   ,				"""time_emb_proj"""      )

				SCREAMING_SNAKE_CASE__								= shave_segments(UpperCamelCase__   ,				n_shave_prefix_segments=UpperCamelCase__      )

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

		return mapping






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Tuple   ,				UpperCamelCase__:							Dict=0   ,				UpperCamelCase__:							int=False      ):
		SCREAMING_SNAKE_CASE__								= []
		for old_item in old_list:
				SCREAMING_SNAKE_CASE__								= old_item

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

				SCREAMING_SNAKE_CASE__								= new_item.replace("""proj_out"""   ,				"""proj_attn"""      )
				SCREAMING_SNAKE_CASE__								= new_item.replace("""norm"""   ,				"""group_norm"""      )

				SCREAMING_SNAKE_CASE__								= shave_segments(UpperCamelCase__   ,				n_shave_prefix_segments=UpperCamelCase__      )
				mapping.append({"""old""": old_item, """new""": new_item}      )

		return mapping






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							int   ,				UpperCamelCase__:							Dict   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							List[str]=None   ,				UpperCamelCase__:							Tuple=None   ,				UpperCamelCase__:							Dict=None      ):
		assert isinstance(UpperCamelCase__   ,				UpperCamelCase__      ), "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():
						SCREAMING_SNAKE_CASE__								= old_checkpoint[path]
						SCREAMING_SNAKE_CASE__								= old_tensor.shape[0] // 3

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

						SCREAMING_SNAKE_CASE__								= old_tensor.shape[0] // config.get("""num_head_channels"""   ,				1      ) // 3

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

						SCREAMING_SNAKE_CASE__								= query.reshape(UpperCamelCase__      ).squeeze()
						SCREAMING_SNAKE_CASE__								= key.reshape(UpperCamelCase__      ).squeeze()
						SCREAMING_SNAKE_CASE__								= value.reshape(UpperCamelCase__      ).squeeze()

		for path in paths:
				SCREAMING_SNAKE_CASE__								= path["""new"""]

				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				SCREAMING_SNAKE_CASE__								= new_path.replace("""down."""   ,				"""down_blocks."""      )
				SCREAMING_SNAKE_CASE__								= new_path.replace("""up."""   ,				"""up_blocks."""      )

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

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






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							str   ,				UpperCamelCase__:							Dict      ):
		SCREAMING_SNAKE_CASE__								= {}

		SCREAMING_SNAKE_CASE__								= checkpoint["""temb.dense.0.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""temb.dense.0.bias"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""temb.dense.1.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""temb.dense.1.bias"""]

		SCREAMING_SNAKE_CASE__								= checkpoint["""norm_out.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""norm_out.bias"""]

		SCREAMING_SNAKE_CASE__								= checkpoint["""conv_in.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""conv_in.bias"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""conv_out.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""conv_out.bias"""]

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

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

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

				if any("""downsample""" in layer for layer in down_blocks[i]      ):
						SCREAMING_SNAKE_CASE__								= checkpoint[
						    f'''down.{i}.downsample.op.weight'''
						]
						SCREAMING_SNAKE_CASE__								= 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]      ):
						SCREAMING_SNAKE_CASE__								= len(
						    {""".""".join(shave_segments(UpperCamelCase__   ,				2      ).split("""."""      )[:2]      ) for layer in down_blocks[i] if """block""" in layer}      )
						SCREAMING_SNAKE_CASE__								= {
						    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
						    for layer_id in range(UpperCamelCase__      )
						}

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

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

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

		SCREAMING_SNAKE_CASE__								= [key for key in checkpoint if """mid.block_1""" in key]
		SCREAMING_SNAKE_CASE__								= [key for key in checkpoint if """mid.block_2""" in key]
		SCREAMING_SNAKE_CASE__								= [key for key in checkpoint if """mid.attn_1""" in key]

		# Mid new 2
		SCREAMING_SNAKE_CASE__								= renew_resnet_paths(UpperCamelCase__      )
		assign_to_checkpoint(
		    UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]   ,				)

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

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

		for i in range(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= num_up_blocks - 1 - i

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

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

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

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

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

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






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							Any      ):
		SCREAMING_SNAKE_CASE__								= {}

		SCREAMING_SNAKE_CASE__								= checkpoint["""encoder.norm_out.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""encoder.norm_out.bias"""]

		SCREAMING_SNAKE_CASE__								= checkpoint["""encoder.conv_in.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""encoder.conv_in.bias"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""encoder.conv_out.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""encoder.conv_out.bias"""]

		SCREAMING_SNAKE_CASE__								= checkpoint["""decoder.norm_out.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""decoder.norm_out.bias"""]

		SCREAMING_SNAKE_CASE__								= checkpoint["""decoder.conv_in.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""decoder.conv_in.bias"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""decoder.conv_out.weight"""]
		SCREAMING_SNAKE_CASE__								= checkpoint["""decoder.conv_out.bias"""]

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

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

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

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

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

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

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

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

		SCREAMING_SNAKE_CASE__								= [key for key in checkpoint if """mid.block_1""" in key]
		SCREAMING_SNAKE_CASE__								= [key for key in checkpoint if """mid.block_2""" in key]
		SCREAMING_SNAKE_CASE__								= [key for key in checkpoint if """mid.attn_1""" in key]

		# Mid new 2
		SCREAMING_SNAKE_CASE__								= renew_resnet_paths(UpperCamelCase__      )
		assign_to_checkpoint(
		    UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]   ,				)

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

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

		for i in range(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= num_up_blocks - 1 - i

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

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

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

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

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

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

		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(
	    '--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.')

	_lowerCamelCase							=     parser.parse_args()
	_lowerCamelCase							=     torch.load(args.checkpoint_path)

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

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

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

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

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

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






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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# Construct configuration depending on size
		SCREAMING_SNAKE_CASE__								= {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE__								= ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ImageGPTForCausalLM(UpperCamelCase__      )

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

		# Save pytorch-model
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''      )
		torch.save(model.state_dict()   ,				UpperCamelCase__      )
		print(f'''Save configuration file to {pytorch_config_dump_path}'''      )
		with open(UpperCamelCase__   ,				"""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
	)