



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					(   lowerCAmelCase_       : Optional[Any]    )				->			Dict:






      '''simple docstring'''



      UpperCAmelCase_=       {k: k for k in unet_state_dict.keys()}
      for sd_name, hf_name in unet_conversion_map:
            UpperCAmelCase_=       sd_name
      for k, v in mapping.items():
            if "resnets" in k:
                  for sd_part, hf_part in unet_conversion_map_resnet:
                        UpperCAmelCase_=       v.replace(a__      ,a__    )
                  UpperCAmelCase_=       v
      for k, v in mapping.items():
            for sd_part, hf_part in unet_conversion_map_layer:
                  UpperCAmelCase_=       v.replace(a__      ,a__    )
            UpperCAmelCase_=       v
      UpperCAmelCase_=       {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					(   lowerCAmelCase_       : str    )				->			Union[str, Any]:






      '''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			Optional[int]:






      '''simple docstring'''



      UpperCAmelCase_=       {k: k for k in vae_state_dict.keys()}
      for k, v in mapping.items():
            for sd_part, hf_part in vae_conversion_map:
                  UpperCAmelCase_=       v.replace(a__      ,a__    )
            UpperCAmelCase_=       v
      for k, v in mapping.items():
            if "attentions" in k:
                  for sd_part, hf_part in vae_conversion_map_attn:
                        UpperCAmelCase_=       v.replace(a__      ,a__    )
                  UpperCAmelCase_=       v
      UpperCAmelCase_=       {v: vae_state_dict[k] for k, v in mapping.items()}
      UpperCAmelCase_=       ["""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"""    )
                        UpperCAmelCase_=       reshape_weight_for_sd(a__    )
      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					(   lowerCAmelCase_       : List[str]    )				->			str:






      '''simple docstring'''



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

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

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

      return new_state_dict



def 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			List[str]:






      '''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)









import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__A       =						16
__A       =						32



def 	__a					(   lowerCAmelCase_       : Accelerator      ,lowerCAmelCase_       : int = 16      ,lowerCAmelCase_       : str = "bert-base-cased"    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       AutoTokenizer.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       load_dataset("""glue"""      ,"""mrpc"""    )

						def tokenize_function(lowerCAmelCase_       : List[Any]    ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_=       tokenizer(examples["""sentence1"""]      ,examples["""sentence2"""]      ,truncation=lowerCAmelCase_      ,max_length=lowerCAmelCase_    )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						UpperCAmelCase_=       datasets.map(
						    lowerCAmelCase_      ,batched=lowerCAmelCase_      ,remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,load_from_cache_file=lowerCAmelCase_    )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						UpperCAmelCase_=       tokenized_datasets.rename_column("""label"""      ,"""labels"""    )

						def collate_fn(lowerCAmelCase_       : List[Any]    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(lowerCAmelCase_      ,padding="""max_length"""      ,max_length=1_28      ,return_tensors="""pt"""    )
												return tokenizer.pad(lowerCAmelCase_      ,padding="""longest"""      ,return_tensors="""pt"""    )

						# Instantiate dataloaders.
						UpperCAmelCase_=       DataLoader(
						    tokenized_datasets["""train"""]      ,shuffle=lowerCAmelCase_      ,collate_fn=lowerCAmelCase_      ,batch_size=lowerCAmelCase_    )
						UpperCAmelCase_=       DataLoader(
						    tokenized_datasets["""validation"""]      ,shuffle=lowerCAmelCase_      ,collate_fn=lowerCAmelCase_      ,batch_size=lowerCAmelCase_    )

						return train_dataloader, eval_dataloader



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : List[str]    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase_=       config["""lr"""]
						UpperCAmelCase_=       int(config["""num_epochs"""]    )
						UpperCAmelCase_=       int(config["""seed"""]    )
						UpperCAmelCase_=       int(config["""batch_size"""]    )
						UpperCAmelCase_=       args.model_name_or_path

						set_seed(lowerCAmelCase_    )
						UpperCAmelCase_,       UpperCAmelCase_=       get_dataloaders(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_=       AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_      ,return_dict=lowerCAmelCase_    )

						# Instantiate optimizer
						UpperCAmelCase_=       (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						UpperCAmelCase_=       optimizer_cls(params=model.parameters()      ,lr=lowerCAmelCase_    )

						if accelerator.state.deepspeed_plugin is not None:
												UpperCAmelCase_=       accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												UpperCAmelCase_=       1
						UpperCAmelCase_=       (len(lowerCAmelCase_    ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												UpperCAmelCase_=       get_linear_schedule_with_warmup(
												    optimizer=lowerCAmelCase_      ,num_warmup_steps=0      ,num_training_steps=lowerCAmelCase_      ,)
						else:
												UpperCAmelCase_=       DummyScheduler(lowerCAmelCase_      ,total_num_steps=lowerCAmelCase_      ,warmup_num_steps=0    )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       accelerator.prepare(
						    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						# We need to keep track of how many total steps we have iterated over
						UpperCAmelCase_=       0
						# We also need to keep track of the stating epoch so files are named properly
						UpperCAmelCase_=       0

						# Now we train the model
						UpperCAmelCase_=       evaluate.load("""glue"""      ,"""mrpc"""    )
						UpperCAmelCase_=       0
						UpperCAmelCase_=       {}
						for epoch in range(lowerCAmelCase_      ,lowerCAmelCase_    ):
												model.train()
												for step, batch in enumerate(lowerCAmelCase_    ):
																		UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.loss
																		UpperCAmelCase_=       loss / gradient_accumulation_steps
																		accelerator.backward(lowerCAmelCase_    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												UpperCAmelCase_=       0
												for step, batch in enumerate(lowerCAmelCase_    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		with torch.no_grad():
																								UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.logits.argmax(dim=-1    )
																		# It is slightly faster to call this once, than multiple times
																		UpperCAmelCase_,       UpperCAmelCase_=       accelerator.gather(
																		    (predictions, batch["""labels"""])    )  # If we are in a multiprocess environment, the last batch has duplicates
																		if accelerator.use_distributed:
																								if step == len(lowerCAmelCase_    ) - 1:
																														UpperCAmelCase_=       predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																														UpperCAmelCase_=       references[: len(eval_dataloader.dataset    ) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=lowerCAmelCase_      ,references=lowerCAmelCase_      ,)

												UpperCAmelCase_=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""      ,lowerCAmelCase_    )
												UpperCAmelCase_=       eval_metric["""accuracy"""]

												if best_performance < eval_metric["accuracy"]:
																		UpperCAmelCase_=       eval_metric["""accuracy"""]

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir      ,"""all_results.json"""    )      ,"""w"""    ) as f:
																		json.dump(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
						parser.add_argument(
						    """--model_name_or_path"""      ,type=lowerCAmelCase_      ,default="""bert-base-cased"""      ,help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,required=lowerCAmelCase_      ,)
						parser.add_argument(
						    """--output_dir"""      ,type=lowerCAmelCase_      ,default="""."""      ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,)
						parser.add_argument(
						    """--performance_lower_bound"""      ,type=lowerCAmelCase_      ,default=lowerCAmelCase_      ,help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""      ,)
						parser.add_argument(
						    """--num_epochs"""      ,type=lowerCAmelCase_      ,default=3      ,help="""Number of train epochs."""      ,)
						UpperCAmelCase_=       parser.parse_args()
						UpperCAmelCase_=       {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
						training_function(lowerCAmelCase_      ,lowerCAmelCase_    )


if __name__ == "__main__":
	main()




