


'''simple docstring'''

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

a_  :				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"""),
]

a_  :				Optional[Any]						= [
    # (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_  :				Union[str, Any]						= []
# 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_  :				Union[str, Any]						= f'''down_blocks.{i}.resnets.{j}.'''
          a_  :				str						= 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_  :				Optional[Any]						= f'''down_blocks.{i}.attentions.{j}.'''
               a_  :				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
          a_  :				Optional[int]						= f'''up_blocks.{i}.resnets.{j}.'''
          a_  :				str						= 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_  :				str						= f'''up_blocks.{i}.attentions.{j}.'''
               a_  :				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
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.downsamplers.0.conv.'''
          a_  :				str						= 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_  :				Optional[int]						= f'''up_blocks.{i}.upsamplers.0.'''
          a_  :				Any						= f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

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

for j in range(2):
     a_  :				Dict						= f'''mid_block.resnets.{j}.'''
     a_  :				Union[str, Any]						= f'''middle_block.{2*j}.'''
     unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 # 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(UpperCAmelCase_					,				UpperCAmelCase_		)
   lowerCamelCase_      =  v
 for k, v in mapping.items():
  for sd_part, hf_part in unet_conversion_map_layer:
   lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
  lowerCamelCase_      =  v
 lowerCamelCase_      =  {v: unet_state_dict[k] for k, v in mapping.items()}
 return new_state_dict


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

a_  :				int						= [
    # (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_  :				Dict						= f'''encoder.down_blocks.{i}.resnets.{j}.'''
          a_  :				List[str]						= f'''encoder.down.{i}.block.{j}.'''
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

     if i < 3:
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.downsamplers.0.'''
          a_  :				List[Any]						= f'''down.{i}.downsample.'''
          vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

          a_  :				str						= f'''up_blocks.{i}.upsamplers.0.'''
          a_  :				List[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):
          a_  :				List[Any]						= f'''decoder.up_blocks.{i}.resnets.{j}.'''
          a_  :				Dict						= 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_  :				Optional[int]						= f'''mid_block.resnets.{i}.'''
     a_  :				List[Any]						= f'''mid.block_{i+1}.'''
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 # convert HF linear weights to SD conv2d weights
 return w.reshape(*w.shape					,				1					,				1		)





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 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(UpperCAmelCase_					,				UpperCAmelCase_		)
  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(UpperCAmelCase_					,				UpperCAmelCase_		)
   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(UpperCAmelCase_		)
 return new_state_dict


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


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

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





def __snake_case					(      UpperCAmelCase_						:						Tuple		):
 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 UpperCAmelCase_		: protected[re.escape(m.group(0		)		)]					,				UpperCAmelCase_		)
  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 UpperCAmelCase_		: protected[re.escape(m.group(0		)		)]					,				UpperCAmelCase_		)
  lowerCamelCase_      =  torch.cat(UpperCAmelCase_		)

 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 UpperCAmelCase_		: protected[re.escape(m.group(0		)		)]					,				UpperCAmelCase_		)
  lowerCamelCase_      =  torch.cat(UpperCAmelCase_		)

 return new_state_dict





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 return text_enc_dict


if __name__ == "__main__":
     a_  :				List[str]						= 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_  :				List[Any]						= 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_  :				str						= osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
     a_  :				Optional[int]						= osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
     a_  :				Optional[Any]						= 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_  :				int						= load_file(unet_path, device="""cpu""")
     else:
          a_  :				str						= osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
          a_  :				int						= torch.load(unet_path, map_location="""cpu""")

     if osp.exists(vae_path):
          a_  :				List[Any]						= load_file(vae_path, device="""cpu""")
     else:
          a_  :				Dict						= osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
          a_  :				Tuple						= torch.load(vae_path, map_location="""cpu""")

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

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

     # Convert the VAE model
     a_  :				Any						= convert_vae_state_dict(vae_state_dict)
     a_  :				List[Any]						= {"""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_  :				List[Any]						= """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_  :				List[Any]						= {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
          a_  :				List[Any]						= convert_text_enc_state_dict_vaa(text_enc_dict)
          a_  :				str						= {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
     else:
          a_  :				List[str]						= convert_text_enc_state_dict(text_enc_dict)
          a_  :				Tuple						= {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

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

     if args.use_safetensors:
          save_file(state_dict, args.checkpoint_path)
     else:
          a_  :				Optional[int]						= {"""state_dict""": state_dict}
          torch.save(state_dict, args.checkpoint_path)










import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowerCAmelCase_    =				logging.getLogger(__name__)






def      lowerCamelCase_						(				)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  argparse.ArgumentParser(
				    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.''' )
				parser.add_argument(
				    '''--dataset_name''' ,				type=_UpperCamelCase ,				default='''wikitext''' ,				help='''Name of the training. Explore datasets at: hf.co/datasets.''' ,				)
				parser.add_argument(
				    '''--dataset_config''' ,				type=_UpperCamelCase ,				default='''wikitext-103-raw-v1''' ,				help='''Configuration name of the dataset.''' )
				parser.add_argument(
				    '''--tokenizer_name_or_path''' ,				type=_UpperCamelCase ,				default='''sayakpaul/unigram-tokenizer-wikitext''' ,				help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.''' ,				)
				parser.add_argument(
				    '''--shard_size''' ,				type=_UpperCamelCase ,				default=1_000 ,				help='''Number of entries to go in a single shard.''' ,				)
				parser.add_argument('''--split''' ,				type=_UpperCamelCase ,				default='''train''' ,				choices=['''train''', '''test''', '''validation'''] )
				parser.add_argument(
				    '''--limit''' ,				default=_UpperCamelCase ,				type=_UpperCamelCase ,				help='''Limit the number of shards (used for debugging).''' ,				)
				parser.add_argument(
				    '''--max_length''' ,				type=_UpperCamelCase ,				default=512 ,				help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
				    ''' sequence length that is a multiple of 8.''' ,				)
				parser.add_argument(
				    '''--output_dir''' ,				default='''tf-tpu''' ,				type=_UpperCamelCase ,				help='''Output directory where the TFRecord shards will be saved. If the'''
				    ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
				    ''' shards will be directly saved to a Google Cloud Storage bucket.''' ,				)

				snake_case_						:    List[Any]				=  parser.parse_args()
				return args






def      lowerCamelCase_						(				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				def fn(_UpperCamelCase ):
								return tokenizer(examples['''text'''] )

				return fn






def      lowerCamelCase_						(				_UpperCamelCase )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:    Any				=  []
				for i in range(len(tokenized_data['''input_ids'''] ) ):
								snake_case_						:    Any				=  {
								    '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i] ) ),
								    '''attention_mask''': tf.train.Feature(
								        intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i] ) ),
								}
								snake_case_						:    Optional[int]				=  tf.train.Features(feature=_UpperCamelCase )
								snake_case_						:    Optional[Any]				=  tf.train.Example(features=_UpperCamelCase )
								snake_case_						:    Optional[Any]				=  example.SerializeToString()
								records.append(_UpperCamelCase )
				return records






def      lowerCamelCase_						(				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    int				=  datasets.load_dataset(args.dataset_name ,				args.dataset_config ,				split=args.split )

				if args.limit is not None:
								snake_case_						:    Union[str, Any]				=  min(len(_UpperCamelCase ) ,				args.limit )
								snake_case_						:    int				=  dataset.select(range(_UpperCamelCase ) )
								print(f'''Limiting the dataset to {args.limit} entries.''' )

				snake_case_						:    Dict				=  AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )

				# Handle output directory creation.
				# For serializing into a Google Cloud Storage Bucket, one needs to first
				# create a bucket.
				if "gs" not in args.output_dir:
								if not os.path.exists(args.output_dir ):
												os.makedirs(args.output_dir )
								snake_case_						:    str				=  os.path.join(args.output_dir ,				args.split )
								if not os.path.exists(_UpperCamelCase ):
												os.makedirs(_UpperCamelCase )
				else:
								snake_case_						:    Optional[Any]				=  os.path.join(args.output_dir ,				args.split )

				# Tokenize the whole dataset at once.
				snake_case_						:    Optional[Any]				=  tokenize_function(_UpperCamelCase )
				snake_case_						:    List[Any]				=  dataset.map(_UpperCamelCase ,				batched=_UpperCamelCase ,				num_proc=4 ,				remove_columns=['''text'''] )

				# We need to concatenate all our texts together, and then split the result
				# into chunks of a fixed size, which we will call block_size. To do this, we
				# will use the map method again, with the option batched=True. When we use batched=True,
				# the function we pass to map() will be passed multiple inputs at once, allowing us
				# to group them into more or fewer examples than we had in the input.
				# This allows us to create our new fixed-length samples. The advantage of this
				# method is that we don't lose a whole lot of content from the dataset compared to the
				# case where we simply tokenize with a pre-defined max_length.

				def group_texts(_UpperCamelCase ):
								# Concatenate all texts.
								snake_case_						:    Tuple				=  {k: sum(examples[k] ,				[] ) for k in examples.keys()}
								snake_case_						:    List[str]				=  len(concatenated_examples[list(examples.keys() )[0]] )
								# We drop the small remainder, though you could add padding instead if the model supports it
								# In this, as in all things, we advise you to follow your heart 🫀
								snake_case_						:    int				=  (total_length // args.max_length) * args.max_length
								# Split by chunks of max_len.
								snake_case_						:    Union[str, Any]				=  {
								    k: [t[i : i + args.max_length] for i in range(0 ,				_UpperCamelCase ,				args.max_length )]
								    for k, t in concatenated_examples.items()
								}
								return result

				snake_case_						:    int				=  dataset_tokenized.map(_UpperCamelCase ,				batched=_UpperCamelCase ,				batch_size=1_000 ,				num_proc=4 )

				snake_case_						:    str				=  0
				snake_case_						:    Optional[Any]				=  0
				for shard in range(0 ,				len(_UpperCamelCase ) ,				args.shard_size ):
								snake_case_						:    Any				=  grouped_dataset[shard : shard + args.shard_size]
								snake_case_						:    str				=  len(dataset_snapshot['''input_ids'''] )
								snake_case_						:    Union[str, Any]				=  os.path.join(_UpperCamelCase ,				f'''dataset-{shard_count}-{records_containing}.tfrecord''' )
								snake_case_						:    Dict				=  get_serialized_examples(_UpperCamelCase )

								with tf.io.TFRecordWriter(_UpperCamelCase ) as out_file:
												for i in range(len(_UpperCamelCase ) ):
																snake_case_						:    List[str]				=  serialized_examples[i]
																out_file.write(_UpperCamelCase )
												print('''Wrote file {} containing {} records'''.format(_UpperCamelCase ,				_UpperCamelCase ) )

								shard_count += 1
								total_records += records_containing

				with open(f'''split-{args.split}-records-count.txt''' ,				'''w''' ) as f:
								print(f'''Total {args.split} records: {total_records}''' ,				file=_UpperCamelCase )


if __name__ == "__main__":
	lowerCAmelCase_    =				parse_args()
	main(args)
