





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def    A_(			A				,      A=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    A_(			A				,      A=0   ):
	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(A				,      n_shave_prefix_segments=A   )

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

	return mapping




def    A_(			A				,      A=0   ):
	UpperCAmelCase_								=     []
	for old_item in old_list:
		UpperCAmelCase_								=     old_item

		UpperCAmelCase_								=     new_item.replace("""norm.weight"""				,      """group_norm.weight"""   )
		UpperCAmelCase_								=     new_item.replace("""norm.bias"""				,      """group_norm.bias"""   )

		UpperCAmelCase_								=     new_item.replace("""proj_out.weight"""				,      """proj_attn.weight"""   )
		UpperCAmelCase_								=     new_item.replace("""proj_out.bias"""				,      """proj_attn.bias"""   )

		UpperCAmelCase_								=     shave_segments(A				,      n_shave_prefix_segments=A   )

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

	return mapping




def    A_(			A				,      A				,      A				,      A=None				,      A=None				,      A=None   ):
	assert isinstance(A				,      A   ), "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(A   )
			UpperCAmelCase_								=     key.reshape(A   )
			UpperCAmelCase_								=     value.reshape(A   )

	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

		# Global renaming happens here
		UpperCAmelCase_								=     new_path.replace("""middle_block.0"""				,      """mid_block.resnets.0"""   )
		UpperCAmelCase_								=     new_path.replace("""middle_block.1"""				,      """mid_block.attentions.0"""   )
		UpperCAmelCase_								=     new_path.replace("""middle_block.2"""				,      """mid_block.resnets.1"""   )

		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
		if "proj_attn.weight" in new_path:
			UpperCAmelCase_								=     old_checkpoint[path["""old"""]][:, :, 0]
		else:
			UpperCAmelCase_								=     old_checkpoint[path["""old"""]]




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     {}

	UpperCAmelCase_								=     checkpoint["""time_embed.0.weight"""]
	UpperCAmelCase_								=     checkpoint["""time_embed.0.bias"""]
	UpperCAmelCase_								=     checkpoint["""time_embed.2.weight"""]
	UpperCAmelCase_								=     checkpoint["""time_embed.2.bias"""]

	UpperCAmelCase_								=     checkpoint["""input_blocks.0.0.weight"""]
	UpperCAmelCase_								=     checkpoint["""input_blocks.0.0.bias"""]

	UpperCAmelCase_								=     checkpoint["""out.0.weight"""]
	UpperCAmelCase_								=     checkpoint["""out.0.bias"""]
	UpperCAmelCase_								=     checkpoint["""out.2.weight"""]
	UpperCAmelCase_								=     checkpoint["""out.2.bias"""]

	# Retrieves the keys for the input blocks only
	UpperCAmelCase_								=     len({""".""".join(layer.split("""."""   )[:2]   ) for layer in checkpoint if """input_blocks""" in layer}   )
	UpperCAmelCase_								=     {
	    layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
	    for layer_id in range(A   )
	}

	# Retrieves the keys for the middle blocks only
	UpperCAmelCase_								=     len({""".""".join(layer.split("""."""   )[:2]   ) for layer in checkpoint if """middle_block""" in layer}   )
	UpperCAmelCase_								=     {
	    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
	    for layer_id in range(A   )
	}

	# Retrieves the keys for the output blocks only
	UpperCAmelCase_								=     len({""".""".join(layer.split("""."""   )[:2]   ) for layer in checkpoint if """output_blocks""" in layer}   )
	UpperCAmelCase_								=     {
	    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
	    for layer_id in range(A   )
	}

	for i in range(1				,      A   ):
		UpperCAmelCase_								=     (i - 1) // (config["""num_res_blocks"""] + 1)
		UpperCAmelCase_								=     (i - 1) % (config["""num_res_blocks"""] + 1)

		UpperCAmelCase_								=     [key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
		UpperCAmelCase_								=     [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

		if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
			UpperCAmelCase_								=     checkpoint[
			    f"""input_blocks.{i}.0.op.weight"""
			]
			UpperCAmelCase_								=     checkpoint[
			    f"""input_blocks.{i}.0.op.bias"""
			]
			continue

		UpperCAmelCase_								=     renew_resnet_paths(A   )
		UpperCAmelCase_								=     {"""old""": f"""input_blocks.{i}.0""", """new""": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
		UpperCAmelCase_								=     {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
		assign_to_checkpoint(
		    A				,      A				,      A				,      additional_replacements=[meta_path, resnet_op]				,      config=A   )

		if len(A   ):
			UpperCAmelCase_								=     renew_attention_paths(A   )
			UpperCAmelCase_								=     {
			    """old""": f"""input_blocks.{i}.1""",
			    """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
			}
			UpperCAmelCase_								=     {
			    f"""input_blocks.{i}.1.qkv.bias""": {
			        """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
			        """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
			        """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
			    },
			    f"""input_blocks.{i}.1.qkv.weight""": {
			        """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
			        """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
			        """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
			    },
			}
			assign_to_checkpoint(
			    A				,      A				,      A				,      additional_replacements=[meta_path]				,      attention_paths_to_split=A				,      config=A				,      )

	UpperCAmelCase_								=     middle_blocks[0]
	UpperCAmelCase_								=     middle_blocks[1]
	UpperCAmelCase_								=     middle_blocks[2]

	UpperCAmelCase_								=     renew_resnet_paths(A   )
	assign_to_checkpoint(A				,      A				,      A				,      config=A   )

	UpperCAmelCase_								=     renew_resnet_paths(A   )
	assign_to_checkpoint(A				,      A				,      A				,      config=A   )

	UpperCAmelCase_								=     renew_attention_paths(A   )
	UpperCAmelCase_								=     {
	    """middle_block.1.qkv.bias""": {
	        """key""": """mid_block.attentions.0.key.bias""",
	        """query""": """mid_block.attentions.0.query.bias""",
	        """value""": """mid_block.attentions.0.value.bias""",
	    },
	    """middle_block.1.qkv.weight""": {
	        """key""": """mid_block.attentions.0.key.weight""",
	        """query""": """mid_block.attentions.0.query.weight""",
	        """value""": """mid_block.attentions.0.value.weight""",
	    },
	}
	assign_to_checkpoint(
	    A				,      A				,      A				,      attention_paths_to_split=A				,      config=A   )

	for i in range(A   ):
		UpperCAmelCase_								=     i // (config["""num_res_blocks"""] + 1)
		UpperCAmelCase_								=     i % (config["""num_res_blocks"""] + 1)
		UpperCAmelCase_								=     [shave_segments(A				,      2   ) for name in output_blocks[i]]
		UpperCAmelCase_								=     {}

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

		if len(A   ) > 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_								=     renew_resnet_paths(A   )
			UpperCAmelCase_								=     renew_resnet_paths(A   )

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

			if ["conv.weight", "conv.bias"] in output_block_list.values():
				UpperCAmelCase_								=     list(output_block_list.values()   ).index(["""conv.weight""", """conv.bias"""]   )
				UpperCAmelCase_								=     checkpoint[
				    f"""output_blocks.{i}.{index}.conv.weight"""
				]
				UpperCAmelCase_								=     checkpoint[
				    f"""output_blocks.{i}.{index}.conv.bias"""
				]

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

			if len(A   ):
				UpperCAmelCase_								=     renew_attention_paths(A   )
				UpperCAmelCase_								=     {
				    """old""": f"""output_blocks.{i}.1""",
				    """new""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
				}
				UpperCAmelCase_								=     {
				    f"""output_blocks.{i}.1.qkv.bias""": {
				        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
				        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
				        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
				    },
				    f"""output_blocks.{i}.1.qkv.weight""": {
				        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
				        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
				        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
				    },
				}
				assign_to_checkpoint(
				    A				,      A				,      A				,      additional_replacements=[meta_path]				,      attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions   ) else None				,      config=A				,      )
		else:
			UpperCAmelCase_								=     renew_resnet_paths(A				,      n_shave_prefix_segments=1   )
			for path in resnet_0_paths:
				UpperCAmelCase_								=     """.""".join(["""output_blocks""", str(A   ), path["""old"""]]   )
				UpperCAmelCase_								=     """.""".join(["""up_blocks""", str(A   ), """resnets""", str(A   ), path["""new"""]]   )

				UpperCAmelCase_								=     checkpoint[old_path]

	return new_checkpoint


if __name__ == "__main__":
				UpperCamelCase__   : Tuple					  = 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__   : str					  = parser.parse_args()

				UpperCamelCase__   : List[Any]					  = torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								UpperCamelCase__   : Optional[Any]					  = json.loads(f.read())

				UpperCamelCase__   : str					  = convert_ldm_checkpoint(checkpoint, config)

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

				UpperCamelCase__   : Union[str, Any]					  = UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								UpperCamelCase__   : List[Any]					  = DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
								UpperCamelCase__   : Dict					  = VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

								UpperCamelCase__   : Union[str, Any]					  = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase__   : Tuple					  = 5_00_00
UpperCamelCase__   : Any					  = 50_00

UpperCamelCase__  ,			UpperCamelCase__   : Tuple					  = os.path.split(__file__)
UpperCamelCase__   : Union[str, Any]					  = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))




@get_duration
def    A_(			A				,      A   ):
	for i in range(A   ):
		UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A   ):
	for i in range(0				,      len(A   )				,      A   ):
		UpperCAmelCase_								=     dataset[i : i + batch_size]




@get_duration
def    A_(			A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(A   ):
			UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(0				,      A				,      A   ):
			UpperCAmelCase_								=     dataset[i : i + batch_size]




def    A_(			):
	UpperCAmelCase_								=     {"""num examples""": SPEED_TEST_N_EXAMPLES}
	UpperCAmelCase_								=     [
	    (read, {"""length""": SMALL_TEST}),
	    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
	]

	UpperCAmelCase_								=     [
	    (read, {"""length""": SMALL_TEST}),
	    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
	]
	with tempfile.TemporaryDirectory() as tmp_dir:
		print("""generating dataset"""   )
		UpperCAmelCase_								=     datasets.Features(
		    {"""list""": datasets.Sequence(datasets.Value("""float32"""   )   ), """numbers""": datasets.Value("""float32"""   )}   )
		UpperCAmelCase_								=     generate_example_dataset(
		    os.path.join(A				,      """dataset.arrow"""   )				,      A				,      num_examples=A				,      seq_shapes={"""list""": (100,)}				,      )
		print("""first set of iterations"""   )
		for func, kwargs in functions:
			print(func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(A				,      **A   )

		print("""shuffling dataset"""   )
		UpperCAmelCase_								=     dataset.shuffle()
		print("""Second set of iterations (after shuffling"""   )
		for func, kwargs in functions_shuffled:
			print("""shuffled """				,      func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(
			    A				,      **A   )

	with open(A				,      """wb"""   ) as f:
		f.write(json.dumps(A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_iterating()


