



'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def lowerCAmelCase  (__A  ,	__A=1):



     """simple docstring"""



     if n_shave_prefix_segments >= 0:
          return ".".join(path.split('''.''')[n_shave_prefix_segments:])
     else:
          return ".".join(path.split('''.''')[:n_shave_prefix_segments])




def lowerCAmelCase  (__A  ,	__A=0):



     """simple docstring"""



     _a      =  []
     for old_item in old_list:
          _a      =  old_item.replace('''in_layers.0'''  ,	'''norm1''')
          _a      =  new_item.replace('''in_layers.2'''  ,	'''conv1''')

          _a      =  new_item.replace('''out_layers.0'''  ,	'''norm2''')
          _a      =  new_item.replace('''out_layers.3'''  ,	'''conv2''')

          _a      =  new_item.replace('''emb_layers.1'''  ,	'''time_emb_proj''')
          _a      =  new_item.replace('''skip_connection'''  ,	'''conv_shortcut''')

          _a      =  shave_segments(__A  ,	n_shave_prefix_segments=__A)

          mapping.append({'''old''': old_item, '''new''': new_item})

     return mapping




def lowerCAmelCase  (__A  ,	__A=0):



     """simple docstring"""



     _a      =  []
     for old_item in old_list:
          _a      =  old_item

          _a      =  new_item.replace('''norm.weight'''  ,	'''group_norm.weight''')
          _a      =  new_item.replace('''norm.bias'''  ,	'''group_norm.bias''')

          _a      =  new_item.replace('''proj_out.weight'''  ,	'''proj_attn.weight''')
          _a      =  new_item.replace('''proj_out.bias'''  ,	'''proj_attn.bias''')

          _a      =  shave_segments(__A  ,	n_shave_prefix_segments=__A)

          mapping.append({'''old''': old_item, '''new''': new_item})

     return mapping




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A=None  ,	__A=None  ,	__A=None):



     """simple docstring"""



     assert isinstance(__A  ,	__A), "Paths should be a list of dicts containing 'old' and 'new' keys."

     # Splits the attention layers into three variables.
     if attention_paths_to_split is not None:
          for path, path_map in attention_paths_to_split.items():
               _a      =  old_checkpoint[path]
               _a      =  old_tensor.shape[0] // 3

               _a      =  (-1, channels) if len(old_tensor.shape) == 3 else (-1)

               _a      =  old_tensor.shape[0] // config['''num_head_channels'''] // 3

               _a      =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
               _a  ,    _a  ,    _a      =  old_tensor.split(channels // num_heads  ,	dim=1)

               _a      =  query.reshape(__A)
               _a      =  key.reshape(__A)
               _a      =  value.reshape(__A)

     for path in paths:
          _a      =  path['''new''']

          # These have already been assigned
          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          # Global renaming happens here
          _a      =  new_path.replace('''middle_block.0'''  ,	'''mid_block.resnets.0''')
          _a      =  new_path.replace('''middle_block.1'''  ,	'''mid_block.attentions.0''')
          _a      =  new_path.replace('''middle_block.2'''  ,	'''mid_block.resnets.1''')

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    _a      =  new_path.replace(replacement['''old''']  ,	replacement['''new'''])

        # proj_attn.weight has to be converted from conv 1D to linear
          if "proj_attn.weight" in new_path:
               _a      =  old_checkpoint[path['''old''']][:, :, 0]
          else:
               _a      =  old_checkpoint[path['''old''']]




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  {}

     _a      =  checkpoint['''time_embed.0.weight''']
     _a      =  checkpoint['''time_embed.0.bias''']
     _a      =  checkpoint['''time_embed.2.weight''']
     _a      =  checkpoint['''time_embed.2.bias''']

     _a      =  checkpoint['''input_blocks.0.0.weight''']
     _a      =  checkpoint['''input_blocks.0.0.bias''']

     _a      =  checkpoint['''out.0.weight''']
     _a      =  checkpoint['''out.0.bias''']
     _a      =  checkpoint['''out.2.weight''']
     _a      =  checkpoint['''out.2.bias''']

     # Retrieves the keys for the input blocks only
     _a      =  len({'''.'''.join(layer.split('''.''')[:2]) for layer in checkpoint if '''input_blocks''' in layer})
     _a      =  {
         layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
         for layer_id in range(__A)
     }

     # Retrieves the keys for the middle blocks only
     _a      =  len({'''.'''.join(layer.split('''.''')[:2]) for layer in checkpoint if '''middle_block''' in layer})
     _a      =  {
         layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
         for layer_id in range(__A)
     }

     # Retrieves the keys for the output blocks only
     _a      =  len({'''.'''.join(layer.split('''.''')[:2]) for layer in checkpoint if '''output_blocks''' in layer})
     _a      =  {
         layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
         for layer_id in range(__A)
     }

     for i in range(1  ,	__A):
          _a      =  (i - 1) // (config['''num_res_blocks'''] + 1)
          _a      =  (i - 1) % (config['''num_res_blocks'''] + 1)

          _a      =  [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
          _a      =  [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

          if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
               _a      =  checkpoint[
                   F'''input_blocks.{i}.0.op.weight'''
               ]
               _a      =  checkpoint[
                   F'''input_blocks.{i}.0.op.bias'''
               ]
               continue

          _a      =  renew_resnet_paths(__A)
          _a      =  {'''old''': F'''input_blocks.{i}.0''', '''new''': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
          _a      =  {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
          assign_to_checkpoint(
              __A  ,	__A  ,	__A  ,	additional_replacements=[meta_path, resnet_op]  ,	config=__A)

          if len(__A):
               _a      =  renew_attention_paths(__A)
               _a      =  {
                   '''old''': F'''input_blocks.{i}.1''',
                   '''new''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
               }
               _a      =  {
                   F'''input_blocks.{i}.1.qkv.bias''': {
                       '''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                       '''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                       '''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
                   },
                   F'''input_blocks.{i}.1.qkv.weight''': {
                       '''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                       '''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                       '''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
                   },
               }
               assign_to_checkpoint(
                   __A  ,	__A  ,	__A  ,	additional_replacements=[meta_path]  ,	attention_paths_to_split=__A  ,	config=__A  ,	)

     _a      =  middle_blocks[0]
     _a      =  middle_blocks[1]
     _a      =  middle_blocks[2]

     _a      =  renew_resnet_paths(__A)
     assign_to_checkpoint(__A  ,	__A  ,	__A  ,	config=__A)

     _a      =  renew_resnet_paths(__A)
     assign_to_checkpoint(__A  ,	__A  ,	__A  ,	config=__A)

     _a      =  renew_attention_paths(__A)
     _a      =  {
         '''middle_block.1.qkv.bias''': {
             '''key''': '''mid_block.attentions.0.key.bias''',
             '''query''': '''mid_block.attentions.0.query.bias''',
             '''value''': '''mid_block.attentions.0.value.bias''',
         },
         '''middle_block.1.qkv.weight''': {
             '''key''': '''mid_block.attentions.0.key.weight''',
             '''query''': '''mid_block.attentions.0.query.weight''',
             '''value''': '''mid_block.attentions.0.value.weight''',
         },
     }
     assign_to_checkpoint(
         __A  ,	__A  ,	__A  ,	attention_paths_to_split=__A  ,	config=__A)

     for i in range(__A):
          _a      =  i // (config['''num_res_blocks'''] + 1)
          _a      =  i % (config['''num_res_blocks'''] + 1)
          _a      =  [shave_segments(__A  ,	2) for name in output_blocks[i]]
          _a      =  {}

          for layer in output_block_layers:
               _a  ,    _a      =  layer.split('''.''')[0], shave_segments(__A  ,	1)
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(__A)
               else:
                    _a      =  [layer_name]

          if len(__A) > 1:
               _a      =  [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
               _a      =  [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

               _a      =  renew_resnet_paths(__A)
               _a      =  renew_resnet_paths(__A)

               _a      =  {'''old''': F'''output_blocks.{i}.0''', '''new''': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
               assign_to_checkpoint(__A  ,	__A  ,	__A  ,	additional_replacements=[meta_path]  ,	config=__A)

               if ["conv.weight", "conv.bias"] in output_block_list.values():
                    _a      =  list(output_block_list.values()).index(['''conv.weight''', '''conv.bias'''])
                    _a      =  checkpoint[
                        F'''output_blocks.{i}.{index}.conv.weight'''
                    ]
                    _a      =  checkpoint[
                        F'''output_blocks.{i}.{index}.conv.bias'''
                    ]

                    # Clear attentions as they have been attributed above.
                    if len(__A) == 2:
                         _a      =  []

               if len(__A):
                    _a      =  renew_attention_paths(__A)
                    _a      =  {
                        '''old''': F'''output_blocks.{i}.1''',
                        '''new''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                    }
                    _a      =  {
                        F'''output_blocks.{i}.1.qkv.bias''': {
                            '''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                            '''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                            '''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
                        },
                        F'''output_blocks.{i}.1.qkv.weight''': {
                            '''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                            '''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                            '''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
                        },
                    }
                    assign_to_checkpoint(
                        __A  ,	__A  ,	__A  ,	additional_replacements=[meta_path]  ,	attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions) else None  ,	config=__A  ,	)
          else:
               _a      =  renew_resnet_paths(__A  ,	n_shave_prefix_segments=1)
               for path in resnet_0_paths:
                    _a      =  '''.'''.join(['''output_blocks''', str(__A), path['''old''']])
                    _a      =  '''.'''.join(['''up_blocks''', str(__A), '''resnets''', str(__A), path['''new''']])

                    _a      =  checkpoint[old_path]

     return new_checkpoint


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

  parser.add_argument(
      "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  )

  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the architecture.",
  )

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

  lowercase_		       =  parser.parse_args()

  lowercase_		       =  torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    lowercase_		       =  json.loads(f.read())

  lowercase_		       =  convert_ldm_checkpoint(checkpoint, config)

  if "ldm" in config:
    del config["ldm"]

  lowercase_		       =  UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    lowercase_		       =  DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
    lowercase_		       =  VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

    lowercase_		       =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)








'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_		       =  {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_		       =  {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       List[Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Any          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       str          =       ['input_ids', 'attention_mask']
 __lowerCamelCase    :       Union[str, Any]          =       RobertaTokenizer



 def __init__(self ,   A=None ,   A=None ,   A=None ,   A="replace" ,   A="<s>" ,   A="</s>" ,   A="</s>" ,   A="<s>" ,   A="<unk>" ,   A="<pad>" ,   A="<mask>" ,   A=False ,   A=True ,   **A ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(
          A ,   A ,   tokenizer_file=A ,   errors=A ,   bos_token=A ,   eos_token=A ,   sep_token=A ,   cls_token=A ,   unk_token=A ,   pad_token=A ,   mask_token=A ,   add_prefix_space=A ,   trim_offsets=A ,   **A ,   )

      _a      =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
      if pre_tok_state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
           _a      =  getattr(A ,   pre_tok_state.pop('''type'''       )       )
           _a      =  add_prefix_space
           _a      =  pre_tok_class(**A       )

      _a      =  add_prefix_space

      _a      =  '''post_processor'''
      _a      =  getattr(self.backend_tokenizer ,   A ,   A       )
      if tokenizer_component_instance:
           _a      =  json.loads(tokenizer_component_instance.__getstate__()       )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
                _a      =  tuple(state['''sep''']       )
           if "cls" in state:
                _a      =  tuple(state['''cls''']       )

           _a      =  False

           if state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
                _a      =  add_prefix_space
                _a      =  True

           if state.get('''trim_offsets''' ,   A       ) != trim_offsets:
                _a      =  trim_offsets
                _a      =  True

           if changes_to_apply:
                _a      =  getattr(A ,   state.pop('''type'''       )       )
                _a      =  component_class(**A       )
                setattr(self.backend_tokenizer ,   A ,   A       )



 @property
 def 					a__  (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 					a__  (self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  AddedToken(A ,   lstrip=A ,   rstrip=A       ) if isinstance(A ,   A       ) else value
      _a      =  value



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  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 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  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 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  self._tokenizer.model.save(A ,   name=A       )
      return tuple(A       )



 def 					a__  (self ,   A ,   A=None       )    ->     List[str]:


      """simple docstring"""



      _a      =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
           return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



 def 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """simple docstring"""



      _a      =  [self.sep_token_id]
      _a      =  [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 + sep + token_ids_a + sep       ) * [0]



