
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__a         =   [
    # (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'),
]

__a         =   [
    # (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'),
]

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

__a         =   'mid_block.attentions.0.'
__a         =   'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				# 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.
				lowercase_	    =   {k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								lowercase_	    =   sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																lowercase_	    =   v.replace(snake_case__		,  snake_case__   )
												lowercase_	    =   v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												lowercase_	    =   v.replace(snake_case__		,  snake_case__   )
								lowercase_	    =   v
				lowercase_	    =   {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


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

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

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

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


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




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape		,  1		,  1   )




def 							a		(				snake_case__:   Dict   ):


				'''simple docstring'''
				lowercase_	    =   {k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												lowercase_	    =   v.replace(snake_case__		,  snake_case__   )
								lowercase_	    =   v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																lowercase_	    =   v.replace(snake_case__		,  snake_case__   )
												lowercase_	    =   v
				lowercase_	    =   {v: vae_state_dict[k] for k, v in mapping.items()}
				lowercase_	    =   ['''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'''   )
																lowercase_	    =   reshape_weight_for_sd(snake_case__   )
				return new_state_dict


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


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

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




def 							a		(				snake_case__:   Dict   ):


				'''simple docstring'''
				lowercase_	    =   {}
				lowercase_	    =   {}
				lowercase_	    =   {}
				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'''   )
								):
												lowercase_	    =   k[: -len('''.q_proj.weight'''   )]
												lowercase_	    =   k[-len('''q_proj.weight'''   )]
												if k_pre not in capture_qkv_weight:
																lowercase_	    =   [None, None, None]
												lowercase_	    =   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'''   )
								):
												lowercase_	    =   k[: -len('''.q_proj.bias'''   )]
												lowercase_	    =   k[-len('''q_proj.bias'''   )]
												if k_pre not in capture_qkv_bias:
																lowercase_	    =   [None, None, None]
												lowercase_	    =   v
												continue

								lowercase_	    =   textenc_pattern.sub(lambda snake_case__   : protected[re.escape(m.group(0   )   )]		,  snake_case__   )
								lowercase_	    =   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'''   )
								lowercase_	    =   textenc_pattern.sub(lambda snake_case__   : protected[re.escape(m.group(0   )   )]		,  snake_case__   )
								lowercase_	    =   torch.cat(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'''   )
								lowercase_	    =   textenc_pattern.sub(lambda snake_case__   : protected[re.escape(m.group(0   )   )]		,  snake_case__   )
								lowercase_	    =   torch.cat(snake_case__   )

				return new_state_dict




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				return text_enc_dict


if __name__ == "__main__":
						__a         =   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.'
						)

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

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

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

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

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

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

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








'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				'beit'


     def __init__(   self			,     lowerCamelCase=8192			,     lowerCamelCase=768			,     lowerCamelCase=12			,     lowerCamelCase=12			,     lowerCamelCase=3072			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.0			,     lowerCamelCase=0.0			,     lowerCamelCase=0.02			,     lowerCamelCase=1E-12			,     lowerCamelCase=224			,     lowerCamelCase=16			,     lowerCamelCase=3			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=True			,     lowerCamelCase=[3, 5, 7, 11]			,     lowerCamelCase=[1, 2, 3, 6]			,     lowerCamelCase=True			,     lowerCamelCase=0.4			,     lowerCamelCase=256			,     lowerCamelCase=1			,     lowerCamelCase=False			,     lowerCamelCase=255			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)

          __A	:       int      =							vocab_size
          __A	:       Tuple      =							hidden_size
          __A	:       List[str]      =							num_hidden_layers
          __A	:       Optional[int]      =							num_attention_heads
          __A	:       Dict      =							intermediate_size
          __A	:       Optional[int]      =							hidden_act
          __A	:       str      =							hidden_dropout_prob
          __A	:       Dict      =							attention_probs_dropout_prob
          __A	:       int      =							initializer_range
          __A	:       List[str]      =							layer_norm_eps

          __A	:       str      =							image_size
          __A	:       Optional[int]      =							patch_size
          __A	:       List[Any]      =							num_channels
          __A	:       List[str]      =							use_mask_token
          __A	:       Union[str, Any]      =							use_absolute_position_embeddings
          __A	:       Optional[int]      =							use_relative_position_bias
          __A	:       int      =							use_shared_relative_position_bias
          __A	:       int      =							layer_scale_init_value
          __A	:       Union[str, Any]      =							drop_path_rate
          __A	:       Dict      =							use_mean_pooling
          # decode head attributes (semantic segmentation)
          __A	:       List[str]      =							out_indices
          __A	:       str      =							pool_scales
          # auxiliary head attributes (semantic segmentation)
          __A	:       List[str]      =							use_auxiliary_head
          __A	:       Tuple      =							auxiliary_loss_weight
          __A	:       Dict      =							auxiliary_channels
          __A	:       Tuple      =							auxiliary_num_convs
          __A	:       List[str]      =							auxiliary_concat_input
          __A	:       int      =							semantic_loss_ignore_index







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				version.parse('1.11' )


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return 1E-4

