





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__lowercase       :Optional[int]       =  [
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

__lowercase       :Dict       =  [
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

__lowercase       :Tuple       =  []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
				# loop over downblocks/upblocks

				for j in range(2):
								# loop over resnets/attentions for downblocks
								__lowercase       :Tuple       =  f"down_blocks.{i}.resnets.{j}."
								__lowercase       :List[Any]       =  f"input_blocks.{3*i + j + 1}.0."
								unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

								if i < 3:
												# no attention layers in down_blocks.3
												__lowercase       :Any       =  f"down_blocks.{i}.attentions.{j}."
												__lowercase       :Union[str, Any]       =  f"input_blocks.{3*i + j + 1}.1."
												unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

				for j in range(3):
								# loop over resnets/attentions for upblocks
								__lowercase       :Optional[int]       =  f"up_blocks.{i}.resnets.{j}."
								__lowercase       :List[Any]       =  f"output_blocks.{3*i + j}.0."
								unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

								if i > 0:
												# no attention layers in up_blocks.0
												__lowercase       :Tuple       =  f"up_blocks.{i}.attentions.{j}."
												__lowercase       :int       =  f"output_blocks.{3*i + j}.1."
												unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

				if i < 3:
								# no downsample in down_blocks.3
								__lowercase       :str       =  f"down_blocks.{i}.downsamplers.0.conv."
								__lowercase       :List[Any]       =  f"input_blocks.{3*(i+1)}.0.op."
								unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

								# no upsample in up_blocks.3
								__lowercase       :Optional[Any]       =  f"up_blocks.{i}.upsamplers.0."
								__lowercase       :List[Any]       =  f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
								unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__lowercase       :List[Any]       =  "mid_block.attentions.0."
__lowercase       :Any       =  "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
				__lowercase       :Tuple       =  f"mid_block.resnets.{j}."
				__lowercase       :int       =  f"middle_block.{2*j}."
				unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def       UpperCAmelCase							(     _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					{k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												SCREAMING_SNAKE_CASE__     :				Dict   =					sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								SCREAMING_SNAKE_CASE__     :				int   =					v.replace(_lowerCamelCase			,   _lowerCamelCase     )
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		SCREAMING_SNAKE_CASE__     :				Any   =					v.replace(_lowerCamelCase			,   _lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					v
						SCREAMING_SNAKE_CASE__     :				int   =					{v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


# ================#
# VAE Conversion #
# ================#

__lowercase       :str       =  [
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
				# down_blocks have two resnets
				for j in range(2):
								__lowercase       :Union[str, Any]       =  f"encoder.down_blocks.{i}.resnets.{j}."
								__lowercase       :Optional[Any]       =  f"encoder.down.{i}.block.{j}."
								vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

				if i < 3:
								__lowercase       :List[Any]       =  f"down_blocks.{i}.downsamplers.0."
								__lowercase       :Tuple       =  f"down.{i}.downsample."
								vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

								__lowercase       :Any       =  f"up_blocks.{i}.upsamplers.0."
								__lowercase       :Any       =  f"up.{3-i}.upsample."
								vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

				# up_blocks have three resnets
				# also, up blocks in hf are numbered in reverse from sd
				for j in range(3):
								__lowercase       :Dict       =  f"decoder.up_blocks.{i}.resnets.{j}."
								__lowercase       :Union[str, Any]       =  f"decoder.up.{3-i}.block.{j}."
								vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
				__lowercase       :Optional[Any]       =  f"mid_block.resnets.{i}."
				__lowercase       :Tuple       =  f"mid.block_{i+1}."
				vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__lowercase       :int       =  [
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[int]     ):





						'''simple docstring'''


						return w.reshape(*w.shape			,   1			,   1     )







def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					{k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		SCREAMING_SNAKE_CASE__     :				int   =					v.replace(_lowerCamelCase			,   _lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								SCREAMING_SNAKE_CASE__     :				Tuple   =					v.replace(_lowerCamelCase			,   _lowerCamelCase     )
																		SCREAMING_SNAKE_CASE__     :				int   =					v
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					{v: vae_state_dict[k] for k, v in mapping.items()}
						SCREAMING_SNAKE_CASE__     :				str   =					["q", "k", "v", "proj_out"]
						for k, v in new_state_dict.items():
												for weight_name in weights_to_convert:
																		if f"""mid.attn_1.{weight_name}.weight""" in k:
																								print(f"""Reshaping {k} for SD format"""     )
																								SCREAMING_SNAKE_CASE__     :				List[Any]   =					reshape_weight_for_sd(_lowerCamelCase     )
						return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__lowercase       :List[Any]       =  [
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
__lowercase       :Dict       =  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__lowercase       :Tuple       =  re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__lowercase       :Any       =  {"q": 0, "k": 1, "v": 2}







def       UpperCAmelCase							(     _lowerCamelCase      :  List[str]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[str]   =					{}
						SCREAMING_SNAKE_CASE__     :				Dict   =					{}
						SCREAMING_SNAKE_CASE__     :				int   =					{}
						for k, v in text_enc_dict.items():
												if (
												    k.endswith(".self_attn.q_proj.weight"     )
												    or k.endswith(".self_attn.k_proj.weight"     )
												    or k.endswith(".self_attn.v_proj.weight"     )
												):
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					k[: -len(".q_proj.weight"     )]
																		SCREAMING_SNAKE_CASE__     :				str   =					k[-len("q_proj.weight"     )]
																		if k_pre not in capture_qkv_weight:
																								SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[None, None, None]
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					v
																		continue

												if (
												    k.endswith(".self_attn.q_proj.bias"     )
												    or k.endswith(".self_attn.k_proj.bias"     )
												    or k.endswith(".self_attn.v_proj.bias"     )
												):
																		SCREAMING_SNAKE_CASE__     :				int   =					k[: -len(".q_proj.bias"     )]
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					k[-len("q_proj.bias"     )]
																		if k_pre not in capture_qkv_bias:
																								SCREAMING_SNAKE_CASE__     :				Tuple   =					[None, None, None]
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					v
																		continue

												SCREAMING_SNAKE_CASE__     :				Dict   =					textenc_pattern.sub(lambda _lowerCamelCase     : protected[re.escape(m.group(0     )     )]			,   _lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					v

						for k_pre, tensors in capture_qkv_weight.items():
												if None in tensors:
																		raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"     )
												SCREAMING_SNAKE_CASE__     :				str   =					textenc_pattern.sub(lambda _lowerCamelCase     : protected[re.escape(m.group(0     )     )]			,   _lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.cat(_lowerCamelCase     )

						for k_pre, tensors in capture_qkv_bias.items():
												if None in tensors:
																		raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"     )
												SCREAMING_SNAKE_CASE__     :				str   =					textenc_pattern.sub(lambda _lowerCamelCase     : protected[re.escape(m.group(0     )     )]			,   _lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				List[str]   =					torch.cat(_lowerCamelCase     )

						return new_state_dict







def       UpperCAmelCase							(     _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						return text_enc_dict


if __name__ == "__main__":
				__lowercase       :List[Any]       =  argparse.ArgumentParser()

				parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
				parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
				parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
				parser.add_argument(
				    "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
				)

				__lowercase       :str       =  parser.parse_args()

				assert args.model_path is not None, "Must provide a model path!"

				assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

				# Path for safetensors
				__lowercase       :List[Any]       =  osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
				__lowercase       :int       =  osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
				__lowercase       :Dict       =  osp.join(args.model_path, "text_encoder", "model.safetensors")

				# Load models from safetensors if it exists, if it doesn't pytorch
				if osp.exists(unet_path):
								__lowercase       :List[str]       =  load_file(unet_path, device="cpu")
				else:
								__lowercase       :int       =  osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
								__lowercase       :Tuple       =  torch.load(unet_path, map_location="cpu")

				if osp.exists(vae_path):
								__lowercase       :Union[str, Any]       =  load_file(vae_path, device="cpu")
				else:
								__lowercase       :Union[str, Any]       =  osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
								__lowercase       :Union[str, Any]       =  torch.load(vae_path, map_location="cpu")

				if osp.exists(text_enc_path):
								__lowercase       :Union[str, Any]       =  load_file(text_enc_path, device="cpu")
				else:
								__lowercase       :Dict       =  osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
								__lowercase       :Tuple       =  torch.load(text_enc_path, map_location="cpu")

				# Convert the UNet model
				__lowercase       :int       =  convert_unet_state_dict(unet_state_dict)
				__lowercase       :List[str]       =  {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

				# Convert the VAE model
				__lowercase       :List[str]       =  convert_vae_state_dict(vae_state_dict)
				__lowercase       :List[str]       =  {"first_stage_model." + k: v for k, v in vae_state_dict.items()}

				# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
				__lowercase       :List[str]       =  "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

				if is_vaa_model:
								# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
								__lowercase       :int       =  {"transformer." + k: v for k, v in text_enc_dict.items()}
								__lowercase       :Optional[Any]       =  convert_text_enc_state_dict_vaa(text_enc_dict)
								__lowercase       :List[Any]       =  {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
				else:
								__lowercase       :Dict       =  convert_text_enc_state_dict(text_enc_dict)
								__lowercase       :Tuple       =  {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

				# Put together new checkpoint
				__lowercase       :Optional[int]       =  {**unet_state_dict, **vae_state_dict, **text_enc_dict}
				if args.half:
								__lowercase       :Tuple       =  {k: v.half() for k, v in state_dict.items()}

				if args.use_safetensors:
								save_file(state_dict, args.checkpoint_path)
				else:
								__lowercase       :Dict       =  {"state_dict": state_dict}
								torch.save(state_dict, args.checkpoint_path)






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard



@slow
@require_flax
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     Dict    )	->List[Any]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()

							def      A_  (      self				:     Dict    )	->Tuple:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-canny"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Dict   =					"bird"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe.prepare_image_inputs([canny_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Tuple   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					jnp.array(
													    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






							def      A_  (      self				:     List[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-openpose"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Any   =					"Chef in the kitchen"
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"    )
													SCREAMING_SNAKE_CASE__     :				str   =					pipe.prepare_image_inputs([pose_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Any   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				str   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.array(
													    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2