




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

UpperCamelCase__					    =    [
    # (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"""),
]

UpperCamelCase__					    =    [
    # (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"""),
]

UpperCamelCase__					    =    []
# 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
										UpperCamelCase__					    =    f'''down_blocks.{i}.resnets.{j}.'''
										UpperCamelCase__					    =    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
															UpperCamelCase__					    =    f'''down_blocks.{i}.attentions.{j}.'''
															UpperCamelCase__					    =    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
										UpperCamelCase__					    =    f'''up_blocks.{i}.resnets.{j}.'''
										UpperCamelCase__					    =    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
															UpperCamelCase__					    =    f'''up_blocks.{i}.attentions.{j}.'''
															UpperCamelCase__					    =    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
										UpperCamelCase__					    =    f'''down_blocks.{i}.downsamplers.0.conv.'''
										UpperCamelCase__					    =    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
										UpperCamelCase__					    =    f'''up_blocks.{i}.upsamplers.0.'''
										UpperCamelCase__					    =    f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
										unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCamelCase__					    =    """mid_block.attentions.0."""
UpperCamelCase__					    =    """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
					UpperCamelCase__					    =    f'''mid_block.resnets.{j}.'''
					UpperCamelCase__					    =    f'''middle_block.{2*j}.'''
					unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def 			_a   (		SCREAMING_SNAKE_CASE_							:       str  ):
					# buyer beware: this is a *brittle* function,
					# and correct output requires that all of these pieces interact in
					# the exact order in which I have arranged them.
					__lowerCAmelCase            = {k: k for k in unet_state_dict.keys()}
					for sd_name, hf_name in unet_conversion_map:
										__lowerCAmelCase            = sd_name
					for k, v in mapping.items():
										if "resnets" in k:
															for sd_part, hf_part in unet_conversion_map_resnet:
																				__lowerCAmelCase            = v.replace(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
															__lowerCAmelCase            = v
					for k, v in mapping.items():
										for sd_part, hf_part in unet_conversion_map_layer:
															__lowerCAmelCase            = v.replace(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
										__lowerCAmelCase            = v
					__lowerCAmelCase            = {v: unet_state_dict[k] for k, v in mapping.items()}
					return new_state_dict


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

UpperCamelCase__					    =    [
    # (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):
										UpperCamelCase__					    =    f'''encoder.down_blocks.{i}.resnets.{j}.'''
										UpperCamelCase__					    =    f'''encoder.down.{i}.block.{j}.'''
										vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

					if i < 3:
										UpperCamelCase__					    =    f'''down_blocks.{i}.downsamplers.0.'''
										UpperCamelCase__					    =    f'''down.{i}.downsample.'''
										vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

										UpperCamelCase__					    =    f'''up_blocks.{i}.upsamplers.0.'''
										UpperCamelCase__					    =    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):
										UpperCamelCase__					    =    f'''decoder.up_blocks.{i}.resnets.{j}.'''
										UpperCamelCase__					    =    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):
					UpperCamelCase__					    =    f'''mid_block.resnets.{i}.'''
					UpperCamelCase__					    =    f'''mid.block_{i+1}.'''
					vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[Any]  ):
					# convert HF linear weights to SD conv2d weights
					return w.reshape(*w.shape   ,   1   ,   1  )







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[str]  ):
					__lowerCAmelCase            = {k: k for k in vae_state_dict.keys()}
					for k, v in mapping.items():
										for sd_part, hf_part in vae_conversion_map:
															__lowerCAmelCase            = v.replace(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
										__lowerCAmelCase            = v
					for k, v in mapping.items():
										if "attentions" in k:
															for sd_part, hf_part in vae_conversion_map_attn:
																				__lowerCAmelCase            = v.replace(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
															__lowerCAmelCase            = v
					__lowerCAmelCase            = {v: vae_state_dict[k] for k, v in mapping.items()}
					__lowerCAmelCase            = ["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"""  )
																				__lowerCAmelCase            = reshape_weight_for_sd(SCREAMING_SNAKE_CASE_  )
					return new_state_dict


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


UpperCamelCase__					    =    [
    # (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"""),
]
UpperCamelCase__					    =    {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCamelCase__					    =    re.compile("""|""".join(protected.keys()))

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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       str  ):
					__lowerCAmelCase            = {}
					__lowerCAmelCase            = {}
					__lowerCAmelCase            = {}
					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"  )
										):
															__lowerCAmelCase            = k[: -len(".q_proj.weight"  )]
															__lowerCAmelCase            = k[-len("q_proj.weight"  )]
															if k_pre not in capture_qkv_weight:
																				__lowerCAmelCase            = [None, None, None]
															__lowerCAmelCase            = 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"  )
										):
															__lowerCAmelCase            = k[: -len(".q_proj.bias"  )]
															__lowerCAmelCase            = k[-len("q_proj.bias"  )]
															if k_pre not in capture_qkv_bias:
																				__lowerCAmelCase            = [None, None, None]
															__lowerCAmelCase            = v
															continue

										__lowerCAmelCase            = textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE_  : protected[re.escape(m.group(0  )  )]   ,   SCREAMING_SNAKE_CASE_  )
										__lowerCAmelCase            = 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"  )
										__lowerCAmelCase            = textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE_  : protected[re.escape(m.group(0  )  )]   ,   SCREAMING_SNAKE_CASE_  )
										__lowerCAmelCase            = torch.cat(SCREAMING_SNAKE_CASE_  )

					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"  )
										__lowerCAmelCase            = textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE_  : protected[re.escape(m.group(0  )  )]   ,   SCREAMING_SNAKE_CASE_  )
										__lowerCAmelCase            = torch.cat(SCREAMING_SNAKE_CASE_  )

					return new_state_dict







def 			_a   (		SCREAMING_SNAKE_CASE_							:       str  ):
					return text_enc_dict


if __name__ == "__main__":
					UpperCamelCase__					    =    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."""
					)

					UpperCamelCase__					    =    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
					UpperCamelCase__					    =    osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
					UpperCamelCase__					    =    osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
					UpperCamelCase__					    =    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):
										UpperCamelCase__					    =    load_file(unet_path, device="""cpu""")
					else:
										UpperCamelCase__					    =    osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
										UpperCamelCase__					    =    torch.load(unet_path, map_location="""cpu""")

					if osp.exists(vae_path):
										UpperCamelCase__					    =    load_file(vae_path, device="""cpu""")
					else:
										UpperCamelCase__					    =    osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
										UpperCamelCase__					    =    torch.load(vae_path, map_location="""cpu""")

					if osp.exists(text_enc_path):
										UpperCamelCase__					    =    load_file(text_enc_path, device="""cpu""")
					else:
										UpperCamelCase__					    =    osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
										UpperCamelCase__					    =    torch.load(text_enc_path, map_location="""cpu""")

					# Convert the UNet model
					UpperCamelCase__					    =    convert_unet_state_dict(unet_state_dict)
					UpperCamelCase__					    =    {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

					# Convert the VAE model
					UpperCamelCase__					    =    convert_vae_state_dict(vae_state_dict)
					UpperCamelCase__					    =    {"""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
					UpperCamelCase__					    =    """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
										UpperCamelCase__					    =    {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
										UpperCamelCase__					    =    convert_text_enc_state_dict_vaa(text_enc_dict)
										UpperCamelCase__					    =    {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
					else:
										UpperCamelCase__					    =    convert_text_enc_state_dict(text_enc_dict)
										UpperCamelCase__					    =    {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

					# Put together new checkpoint
					UpperCamelCase__					    =    {**unet_state_dict, **vae_state_dict, **text_enc_dict}
					if args.half:
										UpperCamelCase__					    =    {k: v.half() for k, v in state_dict.items()}

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










import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     a__					(      snake_case__   ):
						_a							:					Optional[Any]      =					"""M-CLIP"""






						def __init__(     self	,    _A=1_0_2_4	,    _A=7_6_8	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = transformerDimSize
											__lowerCAmelCase            = imageDimSize
											super().__init__(**_A    )






class     a__					(      snake_case__   ):
						_a							:					Tuple      =					MCLIPConfig






						def __init__(     self	,    _A	,    *_A	,    **_A    ):




											"""simple docstring"""



											super().__init__(_A	,    *_A	,    **_A    )
											__lowerCAmelCase            = XLMRobertaModel(_A    )
											__lowerCAmelCase            = torch.nn.Linear(
											    in_features=config.transformerDimensions	,    out_features=config.numDims    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.transformer(input_ids=_A	,    attention_mask=_A    )[0]
											__lowerCAmelCase            = (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
											return self.LinearTransformation(_A    ), embs




