

"""simple docstring"""




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__UpperCAmelCase	=			'pcuenq/k-upscaler'
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      *, __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[Any]   				=   {
     # norm1
     F"{diffusers_resnet_prefix}.norm1.linear.weight": checkpoint[F"{resnet_prefix}.main.0.mapper.weight"],
     F"{diffusers_resnet_prefix}.norm1.linear.bias": checkpoint[F"{resnet_prefix}.main.0.mapper.bias"],
     # conv1
     F"{diffusers_resnet_prefix}.conv1.weight": checkpoint[F"{resnet_prefix}.main.2.weight"],
     F"{diffusers_resnet_prefix}.conv1.bias": checkpoint[F"{resnet_prefix}.main.2.bias"],
     # norm2
     F"{diffusers_resnet_prefix}.norm2.linear.weight": checkpoint[F"{resnet_prefix}.main.4.mapper.weight"],
     F"{diffusers_resnet_prefix}.norm2.linear.bias": checkpoint[F"{resnet_prefix}.main.4.mapper.bias"],
     # conv2
     F"{diffusers_resnet_prefix}.conv2.weight": checkpoint[F"{resnet_prefix}.main.6.weight"],
     F"{diffusers_resnet_prefix}.conv2.bias": checkpoint[F"{resnet_prefix}.main.6.bias"],
 }

 if resnet.conv_shortcut is not None:
  rv.update(
      {
          F"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[F"{resnet_prefix}.skip.weight"],
      } )

 return rv
def 					lowerCAmelCase  (   __UpperCamelCase							,      *, __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					Any   				=   checkpoint[F"{attention_prefix}.qkv_proj.weight"].chunk(3							,      dim=0 )
 UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					Dict   				=   checkpoint[F"{attention_prefix}.qkv_proj.bias"].chunk(3							,      dim=0 )
 UpperCAmelCase__   :					int   				=   {
     # norm
     F"{diffusers_attention_prefix}.norm1.linear.weight": checkpoint[F"{attention_prefix}.norm_in.mapper.weight"],
     F"{diffusers_attention_prefix}.norm1.linear.bias": checkpoint[F"{attention_prefix}.norm_in.mapper.bias"],
     # to_q
     F"{diffusers_attention_prefix}.attn1.to_q.weight": weight_q.squeeze(-1 ).squeeze(-1 ),
     F"{diffusers_attention_prefix}.attn1.to_q.bias": bias_q,
     # to_k
     F"{diffusers_attention_prefix}.attn1.to_k.weight": weight_k.squeeze(-1 ).squeeze(-1 ),
     F"{diffusers_attention_prefix}.attn1.to_k.bias": bias_k,
     # to_v
     F"{diffusers_attention_prefix}.attn1.to_v.weight": weight_v.squeeze(-1 ).squeeze(-1 ),
     F"{diffusers_attention_prefix}.attn1.to_v.bias": bias_v,
     # to_out
     F"{diffusers_attention_prefix}.attn1.to_out.0.weight": checkpoint[F"{attention_prefix}.out_proj.weight"]
     .squeeze(-1 )
     .squeeze(-1 ),
     F"{diffusers_attention_prefix}.attn1.to_out.0.bias": checkpoint[F"{attention_prefix}.out_proj.bias"],
 }

 return rv
def 					lowerCAmelCase  (   __UpperCamelCase							,      *, __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__				, UpperCAmelCase__   :					str   				=   checkpoint[F"{attention_prefix}.kv_proj.weight"].chunk(2							,      dim=0 )
 UpperCAmelCase__				, UpperCAmelCase__   :					Optional[Any]   				=   checkpoint[F"{attention_prefix}.kv_proj.bias"].chunk(2							,      dim=0 )

 UpperCAmelCase__   :					List[Any]   				=   {
     # norm2 (ada groupnorm)
     F"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight": checkpoint[
         F"{attention_prefix}.norm_dec.mapper.weight"
     ],
     F"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias": checkpoint[
         F"{attention_prefix}.norm_dec.mapper.bias"
     ],
     # layernorm on encoder_hidden_state
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight": checkpoint[
         F"{attention_prefix}.norm_enc.weight"
     ],
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias": checkpoint[
         F"{attention_prefix}.norm_enc.bias"
     ],
     # to_q
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight": checkpoint[
         F"{attention_prefix}.q_proj.weight"
     ]
     .squeeze(-1 )
     .squeeze(-1 ),
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias": checkpoint[
         F"{attention_prefix}.q_proj.bias"
     ],
     # to_k
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight": weight_k.squeeze(-1 ).squeeze(-1 ),
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias": bias_k,
     # to_v
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight": weight_v.squeeze(-1 ).squeeze(-1 ),
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias": bias_v,
     # to_out
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight": checkpoint[
         F"{attention_prefix}.out_proj.weight"
     ]
     .squeeze(-1 )
     .squeeze(-1 ),
     F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias": checkpoint[
         F"{attention_prefix}.out_proj.bias"
     ],
 }

 return rv
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					int   				=   """inner_model.u_net.u_blocks""" if block_type == """up""" else """inner_model.u_net.d_blocks"""
 UpperCAmelCase__   :					Tuple   				=   F"{block_prefix}.{block_idx}"

 UpperCAmelCase__   :					int   				=   {}

 if not hasattr(__UpperCamelCase							,      """attentions""" ):
  UpperCAmelCase__   :					List[Any]   				=   1  # resnet only
 elif not block.attentions[0].add_self_attention:
  UpperCAmelCase__   :					Union[str, Any]   				=   2  # resnet -> cross-attention
 else:
  UpperCAmelCase__   :					Optional[Any]   				=   3  # resnet -> self-attention -> cross-attention)

 for resnet_idx, resnet in enumerate(block.resnets ):
  # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
  UpperCAmelCase__   :					str   				=   F"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
  UpperCAmelCase__   :					Tuple   				=   n * resnet_idx if block_type == """up""" else n * resnet_idx + 1
  UpperCAmelCase__   :					Optional[int]   				=   F"{block_prefix}.{idx}" if block_type == """up""" else F"{block_prefix}.{idx}"

  diffusers_checkpoint.update(
      resnet_to_diffusers_checkpoint(
          __UpperCamelCase							,      __UpperCamelCase							,      diffusers_resnet_prefix=__UpperCamelCase							,      resnet_prefix=__UpperCamelCase ) )

 if hasattr(__UpperCamelCase							,      """attentions""" ):
  for attention_idx, attention in enumerate(block.attentions ):
   UpperCAmelCase__   :					int   				=   F"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
   UpperCAmelCase__   :					Optional[int]   				=   n * attention_idx + 1 if block_type == """up""" else n * attention_idx + 2
   UpperCAmelCase__   :					int   				=   F"{block_prefix}.{idx}"
   UpperCAmelCase__   :					List[Any]   				=   F"{block_prefix}.{idx }"
   UpperCAmelCase__   :					List[str]   				=   1 if not attention.add_self_attention else 2
   UpperCAmelCase__   :					Optional[Any]   				=   (
       n * attention_idx + cross_attention_index
       if block_type == """up"""
       else n * attention_idx + cross_attention_index + 1
   )
   UpperCAmelCase__   :					List[Any]   				=   F"{block_prefix}.{idx }"

   diffusers_checkpoint.update(
       cross_attn_to_diffusers_checkpoint(
           __UpperCamelCase							,      diffusers_attention_prefix=__UpperCamelCase							,      diffusers_attention_index=2							,      attention_prefix=__UpperCamelCase							,      ) )

   if attention.add_self_attention is True:
    diffusers_checkpoint.update(
        self_attn_to_diffusers_checkpoint(
            __UpperCamelCase							,      diffusers_attention_prefix=__UpperCamelCase							,      attention_prefix=__UpperCamelCase							,      ) )

 return diffusers_checkpoint
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   {}

 # pre-processing
 diffusers_checkpoint.update(
     {
         """conv_in.weight""": checkpoint["""inner_model.proj_in.weight"""],
         """conv_in.bias""": checkpoint["""inner_model.proj_in.bias"""],
     } )

 # timestep and class embedding
 diffusers_checkpoint.update(
     {
         """time_proj.weight""": checkpoint["""inner_model.timestep_embed.weight"""].squeeze(-1 ),
         """time_embedding.linear_1.weight""": checkpoint["""inner_model.mapping.0.weight"""],
         """time_embedding.linear_1.bias""": checkpoint["""inner_model.mapping.0.bias"""],
         """time_embedding.linear_2.weight""": checkpoint["""inner_model.mapping.2.weight"""],
         """time_embedding.linear_2.bias""": checkpoint["""inner_model.mapping.2.bias"""],
         """time_embedding.cond_proj.weight""": checkpoint["""inner_model.mapping_cond.weight"""],
     } )

 # down_blocks
 for down_block_idx, down_block in enumerate(model.down_blocks ):
  diffusers_checkpoint.update(block_to_diffusers_checkpoint(__UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      """down""" ) )

 # up_blocks
 for up_block_idx, up_block in enumerate(model.up_blocks ):
  diffusers_checkpoint.update(block_to_diffusers_checkpoint(__UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      """up""" ) )

 # post-processing
 diffusers_checkpoint.update(
     {
         """conv_out.weight""": checkpoint["""inner_model.proj_out.weight"""],
         """conv_out.bias""": checkpoint["""inner_model.proj_out.bias"""],
     } )

 return diffusers_checkpoint
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Any   				=   original_config["""input_channels"""] + original_config["""unet_cond_dim"""]
 UpperCAmelCase__   :					Dict   				=   original_config["""input_channels"""] + (1 if original_config["""has_variance"""] else 0)

 UpperCAmelCase__   :					str   				=   original_config["""channels"""]

 assert (
     len(set(original_config["""depths"""] ) ) == 1
 ), "UNet2DConditionModel currently do not support blocks with different number of layers"
 UpperCAmelCase__   :					Dict   				=   original_config["""depths"""][0]

 UpperCAmelCase__   :					Dict   				=   original_config["""mapping_cond_dim"""]
 UpperCAmelCase__   :					int   				=   original_config["""cross_cond_dim"""]

 UpperCAmelCase__   :					int   				=   []
 UpperCAmelCase__   :					Any   				=   []
 for s, c in zip(original_config["""self_attn_depths"""]							,      original_config["""cross_attn_depths"""] ):
  if s:
   UpperCAmelCase__   :					Optional[Any]   				=   """self"""
   UpperCAmelCase__   :					Tuple   				=   """cross""" if c else None
  elif c:
   UpperCAmelCase__   :					Optional[int]   				=   """cross"""
   UpperCAmelCase__   :					Any   				=   None
  else:
   UpperCAmelCase__   :					Dict   				=   None
   UpperCAmelCase__   :					int   				=   None
  attna_types.append(__UpperCamelCase )
  attna_types.append(__UpperCamelCase )

 UpperCAmelCase__   :					Union[str, Any]   				=   UNetaDConditionModel(
     in_channels=__UpperCamelCase							,      out_channels=__UpperCamelCase							,      down_block_types=("""KDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""", """KCrossAttnDownBlock2D""")							,      mid_block_type=__UpperCamelCase							,      up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")							,      block_out_channels=__UpperCamelCase							,      layers_per_block=__UpperCamelCase							,      act_fn="""gelu"""							,      norm_num_groups=__UpperCamelCase							,      cross_attention_dim=__UpperCamelCase							,      attention_head_dim=64							,      time_cond_proj_dim=__UpperCamelCase							,      resnet_time_scale_shift="""scale_shift"""							,      time_embedding_type="""fourier"""							,      timestep_post_act="""gelu"""							,      conv_in_kernel=1							,      conv_out_kernel=1							,      )

 return unet
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					str   				=   torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )

 UpperCAmelCase__   :					Optional[int]   				=   huggingface_hub.hf_hub_download(__UpperCamelCase							,      """config_laion_text_cond_latent_upscaler_2.json""" )
 UpperCAmelCase__   :					Any   				=   huggingface_hub.hf_hub_download(
     __UpperCamelCase							,      """laion_text_cond_latent_upscaler_2_1_00470000_slim.pth""" )
 print(F"loading original model configuration from {orig_config_path}" )
 print(F"loading original model checkpoint from {orig_weights_path}" )

 print("""converting to diffusers unet""" )
 UpperCAmelCase__   :					Union[str, Any]   				=   K.config.load_config(open(__UpperCamelCase ) )["""model"""]
 UpperCAmelCase__   :					Optional[Any]   				=   unet_model_from_original_config(__UpperCamelCase )

 UpperCAmelCase__   :					List[Any]   				=   torch.load(__UpperCamelCase							,      map_location=__UpperCamelCase )["""model_ema"""]
 UpperCAmelCase__   :					int   				=   unet_to_diffusers_checkpoint(__UpperCamelCase							,      __UpperCamelCase )

 model.load_state_dict(__UpperCamelCase							,      strict=__UpperCamelCase )
 model.save_pretrained(args.dump_path )
 print(F"saving converted unet model in {args.dump_path}" )


if __name__ == "__main__":
      __UpperCAmelCase	=			argparse.ArgumentParser()

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
      __UpperCAmelCase	=			parser.parse_args()

      main(args)








"""simple docstring"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	=			logging.get_logger(__name__)

__UpperCAmelCase	=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase	=			{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask""", """token_type_ids"""]
  snake_case_			    =    DebertaTokenizer

  def __init__(					self						:     Optional[Any]   ,A						:     Any=None   ,A						:     Any=None   ,A						:     Dict=None   ,A						:     Optional[int]="replace"   ,A						:     Optional[int]="[CLS]"   ,A						:     str="[SEP]"   ,A						:     Optional[int]="[SEP]"   ,A						:     List[Any]="[CLS]"   ,A						:     Dict="[UNK]"   ,A						:     List[str]="[PAD]"   ,A						:     Optional[Any]="[MASK]"   ,A						:     str=False   ,**A						:     Dict   ,):






   '''simple docstring'''
   super().__init__(
       A   ,A   ,tokenizer_file=A   ,errors=A   ,bos_token=A   ,eos_token=A   ,unk_token=A   ,sep_token=A   ,cls_token=A   ,pad_token=A   ,mask_token=A   ,add_prefix_space=A   ,**A   ,)
   UpperCAmelCase__   :					Tuple   				=   kwargs.pop("""add_bos_token"""   ,A      )

   UpperCAmelCase__   :					Tuple   				=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
   if pre_tok_state.get("""add_prefix_space"""   ,A      ) != add_prefix_space:
    UpperCAmelCase__   :					List[Any]   				=   getattr(A   ,pre_tok_state.pop("""type"""      )      )
    UpperCAmelCase__   :					Any   				=   add_prefix_space
    UpperCAmelCase__   :					List[Any]   				=   pre_tok_class(**A      )

   UpperCAmelCase__   :					str   				=   add_prefix_space

  @property
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   if self._mask_token is None:
    if self.verbose:
     logger.error("""Using mask_token, but it is not set yet."""      )
    return None
   return str(self._mask_token      )

  @mask_token.setter
  def 				__lowercase    (					self						:     Dict   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   AddedToken(A   ,lstrip=A   ,rstrip=A      ) if isinstance(A   ,A      ) else value
   UpperCAmelCase__   :					List[str]   				=   value

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   UpperCAmelCase__   :					Dict   				=   [self.cls_token_id]
   UpperCAmelCase__   :					str   				=   [self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   [self.sep_token_id]
   UpperCAmelCase__   :					str   				=   [self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep      ) * [0]
   return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]

  def 				__lowercase    (					self						:     int   ,*A						:     Tuple   ,**A						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   kwargs.get("""is_split_into_words"""   ,A      )
   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*A   ,**A      )

  def 				__lowercase    (					self						:     str   ,*A						:     List[str]   ,**A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   kwargs.get("""is_split_into_words"""   ,A      )

   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*A   ,**A      )

  def 				__lowercase    (					self						:     Tuple   ,A						:     str   ,A						:     Optional[str] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   self._tokenizer.model.save(A   ,name=A      )
   return tuple(A      )




  def 				__lowercase    (					self						:     List[str]   ,A						:     "Conversation"      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   []
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(A   ,add_special_tokens=A      ) + [self.eos_token_id]      )

   if len(A      ) > self.model_max_length:
    UpperCAmelCase__   :					Optional[int]   				=   input_ids[-self.model_max_length :]
   return input_ids





