

'''simple docstring'''







import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__lowerCamelCase						    =      [
    # (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'),
]

__lowerCamelCase						    =      [
    # (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'),
]

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

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

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




def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = {k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              A_		   = sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            A_		   = v.replace(_A,    _A			)
                     A_		   = v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     A_		   = v.replace(_A,    _A			)
              A_		   = v
       A_		   = {v: unet_state_dict[k] for k, v in mapping.items()}
       return new_state_dict


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

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

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

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


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




def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     List[str]:
       return w.reshape(*w.shape,    1,    1			)




def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Union[str, Any]:
       A_		   = {k: k for k in vae_state_dict.keys()}
       for k, v in mapping.items():
              for sd_part, hf_part in vae_conversion_map:
                     A_		   = v.replace(_A,    _A			)
              A_		   = v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            A_		   = v.replace(_A,    _A			)
                     A_		   = v
       A_		   = {v: vae_state_dict[k] for k, v in mapping.items()}
       A_		   = ["""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'''			)
                            A_		   = reshape_weight_for_sd(_A			)
       return new_state_dict


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


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

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




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

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

       return new_state_dict




def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[Any]:
       return text_enc_dict


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

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

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

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

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

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

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

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



'''simple docstring'''







import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCamelCase						    =      logging.get_logger(__name__)


@dataclass
class    A__ :
    lowercase		   =						field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()     )}     )
    lowercase		   =						field(
        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}     )
    lowercase		   =						field(
        default=128  ,  metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }  ,  )
    lowercase		   =						field(
        default=_snake_case  ,  metadata={"help": "Overwrite the cached training and evaluation sets"}     )



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = self.task_name.lower()


class    A__ ( _snake_case     ):
    lowercase		   =						"train"
    lowercase		   =						"dev"
    lowercase		   =						"test"


class    A__ ( _snake_case     ):
    lowercase		   =						42
    lowercase		   =						42
    lowercase		   =						42



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = Split.train						,			UpperCamelCase__ = None						,			)  -> List[Any]:





           '''simple docstring'''
           warnings.warn(
               """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
               """library. You can have a look at this example script for pointers: """
               """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""						,			UpperCamelCase__						,			)
           A_		   = args
           A_		   = glue_processors[args.task_name]()
           A_		   = glue_output_modes[args.task_name]
           if isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                  try:
                         A_		   = Split[mode]
                  except KeyError:
                         raise KeyError("""mode is not a valid split name"""    )
        # Load data features from cache or dataset file
           A_		   = os.path.join(
               cache_dir if cache_dir is not None else args.data_dir						,			f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''						,			)
           A_		   = self.processor.get_labels()
           if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
               "RobertaTokenizer",
               "RobertaTokenizerFast",
               "XLMRobertaTokenizer",
               "BartTokenizer",
               "BartTokenizerFast",
           ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  A_   ,     A_		   = label_list[2], label_list[1]
           A_		   = label_list

           # Make sure only the first process in distributed training processes the dataset,
           # and the others will use the cache.
           A_		   = cached_features_file + """.lock"""
           with FileLock(UpperCamelCase__    ):
                  if os.path.exists(UpperCamelCase__    ) and not args.overwrite_cache:
                         A_		   = time.time()
                         A_		   = torch.load(UpperCamelCase__    )
                         logger.info(
                             f'''Loading features from cached file {cached_features_file} [took %.3f s]'''						,			time.time() - start    )
                  else:
                         logger.info(f'''Creating features from dataset file at {args.data_dir}'''    )

                         if mode == Split.dev:
                                A_		   = self.processor.get_dev_examples(args.data_dir    )
                         elif mode == Split.test:
                                A_		   = self.processor.get_test_examples(args.data_dir    )
                         else:
                                A_		   = self.processor.get_train_examples(args.data_dir    )
                         if limit_length is not None:
                                A_		   = examples[:limit_length]
                         A_		   = glue_convert_examples_to_features(
                             UpperCamelCase__						,			UpperCamelCase__						,			max_length=args.max_seq_length						,			label_list=UpperCamelCase__						,			output_mode=self.output_mode						,			)
                         A_		   = time.time()
                         torch.save(self.features						,			UpperCamelCase__    )
                         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                         logger.info(
                             f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''    )



    def __len__(   self    )  -> str:





           '''simple docstring'''
           return len(self.features    )



    def __getitem__(   self						,			UpperCamelCase__    )  -> InputFeatures:





           '''simple docstring'''
           return self.features[i]







    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           return self.label_list
