





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 	lowerCAmelCase_					(			__a						)	->						Tuple:







  """simple docstring"""





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







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a						)	->						Any:







  """simple docstring"""





  lowerCamelCase__: Optional[Any]         ={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__: Dict         =v.replace(__a      ,       __a						)
    lowerCamelCase__: Dict         =v
  for k, v in mapping.items():
    if "attentions" in k:
      for sd_part, hf_part in vae_conversion_map_attn:
        lowerCamelCase__: Optional[int]         =v.replace(__a      ,       __a						)
      lowerCamelCase__: Dict         =v
  lowerCamelCase__: Any         ={v: vae_state_dict[k] for k, v in mapping.items()}
  lowerCamelCase__: List[Any]         =["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__: Any         =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 	lowerCAmelCase_					(			__a						)	->						Dict:







  """simple docstring"""





  lowerCamelCase__: List[str]         ={}
  lowerCamelCase__: Tuple         ={}
  lowerCamelCase__: List[str]         ={}
  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__: int         =k[: -len(".q_proj.weight"						)]
      lowerCamelCase__: str         =k[-len("q_proj.weight"						)]
      if k_pre not in capture_qkv_weight:
        lowerCamelCase__: Union[str, Any]         =[None, None, None]
      lowerCamelCase__: Optional[Any]         =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__: Dict         =k[: -len(".q_proj.bias"						)]
      lowerCamelCase__: Tuple         =k[-len("q_proj.bias"						)]
      if k_pre not in capture_qkv_bias:
        lowerCamelCase__: int         =[None, None, None]
      lowerCamelCase__: str         =v
      continue

    lowerCamelCase__: str         =textenc_pattern.sub(lambda __a						: protected[re.escape(m.group(0						)						)]      ,       __a						)
    lowerCamelCase__: Union[str, Any]         =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__: int         =textenc_pattern.sub(lambda __a						: protected[re.escape(m.group(0						)						)]      ,       __a						)
    lowerCamelCase__: Any         =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"						)
    lowerCamelCase__: Optional[Any]         =textenc_pattern.sub(lambda __a						: protected[re.escape(m.group(0						)						)]      ,       __a						)
    lowerCamelCase__: str         =torch.cat(__a						)

  return new_state_dict





def 	lowerCAmelCase_					(			__a						)	->						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 os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

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

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :int			       =list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__snake_case     :Optional[int]			       =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        lowerCAmelCase__					:
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Model type selected in the list: ' + ', '.join(_lowerCamelCase       )}       )
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}       )
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} ,				)
				A_   :     int								=     field(
				    default=6_4 ,				metadata={
				        'help': (
				            'The maximum number of tokens for the question. Questions longer than this will '
				            'be truncated to this length.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=3_0 ,				metadata={
				        'help': (
				            'The maximum length of an answer that can be generated. This is needed because the start '
				            'and end predictions are not conditioned on one another.'
				        )
				    } ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}       )
				A_   :     float								=     field(
				    default=0.0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=2_0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=0 ,				metadata={
				        'help': (
				            'language id of input for language-specific xlm models (see'
				            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
				        )
				    } ,				)
				A_   :     int								=     field(default=1 ,				metadata={'help': 'multiple threads for converting example to features'}       )





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     'train'
				A_   :     str								=     'dev'





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     SquadDataTrainingArguments
				A_   :     List[SquadFeatures]
				A_   :     Split
				A_   :     bool


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  SquadDataTrainingArguments						, __UpperCamelCase      :  PreTrainedTokenizer						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Union[str, Split] = Split.train						, __UpperCamelCase      :  Optional[bool] = False						, __UpperCamelCase      :  Optional[str] = None						, __UpperCamelCase      :  Optional[str] = "pt"						, )		->     Any:
											A      						=    args
											A      						=    is_language_sensitive
											A      						=    SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
											if isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																		try:
																									A      						=    Split[mode]
																		except KeyError:
																									raise KeyError('mode is not a valid split name'       )
											A      						=    mode
											# Load data features from cache or dataset file
											A      						=    'v2' if args.version_2_with_negative else 'v1'
											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}_{version_tag}'''						, )

											# 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       )

																									# Legacy cache files have only features, while new cache files
																									# will have dataset and examples also.
																									A      						=    self.old_features['features']
																									A      						=    self.old_features.get('dataset'						, __UpperCamelCase       )
																									A      						=    self.old_features.get('examples'						, __UpperCamelCase       )
																									logger.info(
																									    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''						, time.time() - start       )

																									if self.dataset is None or self.examples is None:
																																logger.warning(
																																    f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
																																    ' future run'       )
																		else:
																									if mode == Split.dev:
																																A      						=    self.processor.get_dev_examples(args.data_dir       )
																									else:
																																A      						=    self.processor.get_train_examples(args.data_dir       )

																									A						,   A      						=    squad_convert_examples_to_features(
																									    examples=self.examples						, tokenizer=__UpperCamelCase						, max_seq_length=args.max_seq_length						, doc_stride=args.doc_stride						, max_query_length=args.max_query_length						, is_training=mode == Split.train						, threads=args.threads						, return_dataset=__UpperCamelCase						, )

																									A      						=    time.time()
																									torch.save(
																									    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}						, __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      :  Optional[Any]       )		->     Tuple:
											return len(self.features       )


				def __getitem__(							self      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     Dict[str, torch.Tensor]:
											# Convert to Tensors and build dataset
											A      						=    self.features[i]

											A      						=    torch.tensor(feature.input_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.attention_mask						, dtype=torch.long       )
											A      						=    torch.tensor(feature.token_type_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.cls_index						, dtype=torch.long       )
											A      						=    torch.tensor(feature.p_mask						, dtype=torch.float       )
											A      						=    torch.tensor(feature.is_impossible						, dtype=torch.float       )

											A      						=    {
											    'input_ids': input_ids,
											    'attention_mask': attention_mask,
											    'token_type_ids': token_type_ids,
											}

											if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
																		del inputs["token_type_ids"]

											if self.args.model_type in ["xlnet", "xlm"]:
																		inputs.update({'cls_index': cls_index, 'p_mask': p_mask}       )
																		if self.args.version_2_with_negative:
																									inputs.update({'is_impossible': is_impossible}       )
																		if self.is_language_sensitive:
																									inputs.update({'langs': (torch.ones(input_ids.shape						, dtype=torch.intaa       ) * self.args.lang_id)}       )

											if self.mode == Split.train:
																		A      						=    torch.tensor(feature.start_position						, dtype=torch.long       )
																		A      						=    torch.tensor(feature.end_position						, dtype=torch.long       )
																		inputs.update({'start_positions': start_positions, 'end_positions': end_positions}       )

											return inputs