



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 		a__      (		_UpperCamelCase      :			Any			,_UpperCamelCase      :			Optional[int]=1				):
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split('''.'''				)[n_shave_prefix_segments:]				)
   else:
      return ".".join(path.split('''.'''				)[:n_shave_prefix_segments]				)





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			Optional[Any]=0				):
   __lowerCamelCase    				=     []
   for old_item in old_list:
      __lowerCamelCase    				=     old_item.replace('''in_layers.0'''			,'''norm1'''				)
      __lowerCamelCase    				=     new_item.replace('''in_layers.2'''			,'''conv1'''				)

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

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

      __lowerCamelCase    				=     shave_segments(_UpperCamelCase			,n_shave_prefix_segments=_UpperCamelCase				)

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

   return mapping





def 		a__      (		_UpperCamelCase      :			Any			,_UpperCamelCase      :			List[Any]=0				):
   __lowerCamelCase    				=     []
   for old_item in old_list:
      __lowerCamelCase    				=     old_item

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

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

      __lowerCamelCase    				=     shave_segments(_UpperCamelCase			,n_shave_prefix_segments=_UpperCamelCase				)

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

   return mapping





def 		a__      (		_UpperCamelCase      :			Any			,_UpperCamelCase      :			str			,_UpperCamelCase      :			Optional[int]			,_UpperCamelCase      :			Union[str, Any]=None			,_UpperCamelCase      :			List[str]=None			,_UpperCamelCase      :			Dict=None				):
   assert isinstance(_UpperCamelCase			,_UpperCamelCase				), "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():
         __lowerCamelCase    				=     old_checkpoint[path]
         __lowerCamelCase    				=     old_tensor.shape[0] // 3

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

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

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

         __lowerCamelCase    				=     query.reshape(_UpperCamelCase				)
         __lowerCamelCase    				=     key.reshape(_UpperCamelCase				)
         __lowerCamelCase    				=     value.reshape(_UpperCamelCase				)

   for path in paths:
      __lowerCamelCase    				=     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
      __lowerCamelCase    				=     new_path.replace('''middle_block.0'''			,'''mid_block.resnets.0'''				)
      __lowerCamelCase    				=     new_path.replace('''middle_block.1'''			,'''mid_block.attentions.0'''				)
      __lowerCamelCase    				=     new_path.replace('''middle_block.2'''			,'''mid_block.resnets.1'''				)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            __lowerCamelCase    				=     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:
         __lowerCamelCase    				=     old_checkpoint[path['''old''']][:, :, 0]
      else:
         __lowerCamelCase    				=     old_checkpoint[path['''old''']]





def 		a__      (		_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			int				):
   __lowerCamelCase    				=     {}

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

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

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

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

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

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

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

      __lowerCamelCase    				=     [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
      __lowerCamelCase    				=     [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:
         __lowerCamelCase    				=     checkpoint[
             F"""input_blocks.{i}.0.op.weight"""
         ]
         __lowerCamelCase    				=     checkpoint[
             F"""input_blocks.{i}.0.op.bias"""
         ]
         continue

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

      if len(_UpperCamelCase				):
         __lowerCamelCase    				=     renew_attention_paths(_UpperCamelCase				)
         __lowerCamelCase    				=     {
             '''old''': F"""input_blocks.{i}.1""",
             '''new''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
         }
         __lowerCamelCase    				=     {
             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(
             _UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,additional_replacements=[meta_path]			,attention_paths_to_split=_UpperCamelCase			,config=_UpperCamelCase			,)

   __lowerCamelCase    				=     middle_blocks[0]
   __lowerCamelCase    				=     middle_blocks[1]
   __lowerCamelCase    				=     middle_blocks[2]

   __lowerCamelCase    				=     renew_resnet_paths(_UpperCamelCase				)
   assign_to_checkpoint(_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,config=_UpperCamelCase				)

   __lowerCamelCase    				=     renew_resnet_paths(_UpperCamelCase				)
   assign_to_checkpoint(_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,config=_UpperCamelCase				)

   __lowerCamelCase    				=     renew_attention_paths(_UpperCamelCase				)
   __lowerCamelCase    				=     {
       '''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(
       _UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,attention_paths_to_split=_UpperCamelCase			,config=_UpperCamelCase				)

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

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

      if len(_UpperCamelCase				) > 1:
         __lowerCamelCase    				=     [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
         __lowerCamelCase    				=     [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

         __lowerCamelCase    				=     renew_resnet_paths(_UpperCamelCase				)
         __lowerCamelCase    				=     renew_resnet_paths(_UpperCamelCase				)

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

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

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

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

            __lowerCamelCase    				=     checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
  a_								=       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.""")

  a_								=       parser.parse_args()

  a_								=       torch.load(args.checkpoint_path)

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

  a_								=       convert_ldm_checkpoint(checkpoint, config)

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

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

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

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











import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	XLMTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase    				=     [
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    __lowerCamelCase    				=     dict(zip(__UpperCAmelCase						,					range(len(__UpperCAmelCase					)					)					)					)
    __lowerCamelCase    				=     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''merges_file''']					)
    with open(self.vocab_file						,					'''w'''					) as fp:
       fp.write(json.dumps(__UpperCAmelCase					)					)
    with open(self.merges_file						,					'''w'''					) as fp:
       fp.write('''\n'''.join(__UpperCAmelCase					)					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''lower newer'''
    __lowerCamelCase    				=     '''lower newer'''
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer(self.vocab_file						,					self.merges_file					)

    __lowerCamelCase    				=     '''lower'''
    __lowerCamelCase    				=     ['''low''', '''er</w>''']
    __lowerCamelCase    				=     tokenizer.tokenize(__UpperCAmelCase					)
    self.assertListEqual(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tokens + ['''<unk>''']
    __lowerCamelCase    				=     [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,					__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''					)

    __lowerCamelCase    				=     tokenizer.encode('''sequence builders'''						,					add_special_tokens=__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.encode('''multi-sequence build'''						,					add_special_tokens=__UpperCAmelCase					)

    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,					__UpperCAmelCase					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]






