



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

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

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

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

            lowercase__     =  shave_segments(A__						,  n_shave_prefix_segments=A__	)

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

      return mapping

def   _lowerCAmelCase   (						A__						,  A__=0	):
      lowercase__     =  []
      for old_item in old_list:
            lowercase__     =  old_item

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

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

            lowercase__     =  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	):
      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():
                  lowercase__     =  old_checkpoint[path]
                  lowercase__     =  old_tensor.shape[0] // 3

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

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

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

                  lowercase__     =  query.reshape(A__	)
                  lowercase__     =  key.reshape(A__	)
                  lowercase__     =  value.reshape(A__	)

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

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

def   _lowerCAmelCase   (						A__						,  A__	):
      lowercase__     =  {}

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

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

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

      # Retrieves the keys for the input blocks only
      lowercase__     =  len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'input_blocks' in layer}	)
      lowercase__     =  {
          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
      lowercase__     =  len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'middle_block' in layer}	)
      lowercase__     =  {
          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
      lowercase__     =  len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'output_blocks' in layer}	)
      lowercase__     =  {
          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__	):
            lowercase__     =  (i - 1) // (config['num_res_blocks'] + 1)
            lowercase__     =  (i - 1) % (config['num_res_blocks'] + 1)

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

            lowercase__     =  renew_resnet_paths(A__	)
            lowercase__     =  {'old': F'''input_blocks.{i}.0''', 'new': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
            lowercase__     =  {'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__	):
                  lowercase__     =  renew_attention_paths(A__	)
                  lowercase__     =  {
                      'old': F'''input_blocks.{i}.1''',
                      'new': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
                  }
                  lowercase__     =  {
                      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__						,  )

      lowercase__     =  middle_blocks[0]
      lowercase__     =  middle_blocks[1]
      lowercase__     =  middle_blocks[2]

      lowercase__     =  renew_resnet_paths(A__	)
      assign_to_checkpoint(A__						,  A__						,  A__						,  config=A__	)

      lowercase__     =  renew_resnet_paths(A__	)
      assign_to_checkpoint(A__						,  A__						,  A__						,  config=A__	)

      lowercase__     =  renew_attention_paths(A__	)
      lowercase__     =  {
          '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__	):
            lowercase__     =  i // (config['num_res_blocks'] + 1)
            lowercase__     =  i % (config['num_res_blocks'] + 1)
            lowercase__     =  [shave_segments(A__						,  2	) for name in output_blocks[i]]
            lowercase__     =  {}

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

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

                  lowercase__     =  renew_resnet_paths(A__	)
                  lowercase__     =  renew_resnet_paths(A__	)

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

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

                  if len(A__	):
                        lowercase__     =  renew_attention_paths(A__	)
                        lowercase__     =  {
                            'old': F'''output_blocks.{i}.1''',
                            'new': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                        }
                        lowercase__     =  {
                            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:
                  lowercase__     =  renew_resnet_paths(A__						,  n_shave_prefix_segments=1	)
                  for path in resnet_0_paths:
                        lowercase__     =  '.'.join(['output_blocks', str(A__	), path['old']]	)
                        lowercase__     =  '.'.join(['up_blocks', str(A__	), 'resnets', str(A__	), path['new']]	)

                        lowercase__     =  checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
     a__      :				Any   	=			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__      :				List[str]   	=			parser.parse_args()

     a__      :				Union[str, Any]   	=			torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          a__      :				Optional[Any]   	=			json.loads(f.read())

     a__      :				List[str]   	=			convert_ldm_checkpoint(checkpoint, config)

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

     a__      :				Union[str, Any]   	=			UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

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

          a__      :				Optional[Any]   	=			LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)





import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


a__      :				int   	=			logging.get_logger(__name__)

a__      :				Tuple   	=			{"vocab_file": "vocab.txt"}

a__      :				int   	=			{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

a__      :				Dict   	=			{
    "facebook/esm2_t6_8M_UR50D": 10_24,
    "facebook/esm2_t12_35M_UR50D": 10_24,
}

def   _lowerCAmelCase   (						A__	):
      with open(A__						,  'r'	) as f:
            lowercase__     =  f.read().splitlines()
            return [l.strip() for l in lines]



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    VOCAB_FILES_NAMES
   A    :  str       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  int       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  List[Any]       				=    ["input_ids", "attention_mask"]


   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Optional[int]="<unk>"			,				lowerCAmelCase  :			Dict="<cls>"			,				lowerCAmelCase  :			List[str]="<pad>"			,				lowerCAmelCase  :			Union[str, Any]="<mask>"			,				lowerCAmelCase  :			Optional[Any]="<eos>"			,				**lowerCAmelCase  :			Any			,				)	->				Optional[Any]:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)
         lowercase__     =  load_vocab_file(lowerCAmelCase)
         lowercase__     =  dict(enumerate(self.all_tokens))
         lowercase__     =  {tok: ind for ind, tok in enumerate(self.all_tokens)}
         lowercase__     =  unk_token
         lowercase__     =  cls_token
         lowercase__     =  pad_token
         lowercase__     =  mask_token
         lowercase__     =  eos_token
         lowercase__     =  self.all_tokens
         self._create_trie(self.unique_no_split_tokens)


   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         return self._id_to_token.get(lowerCAmelCase			,				self.unk_token)


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         return self._token_to_id.get(lowerCAmelCase			,				self._token_to_id.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			str			,				**lowerCAmelCase  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         return text.split()


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Any=False)	->				Union[str, Any]:




         """simple docstring"""

         return len(self._id_to_token)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

         return {token: i for i, token in enumerate(self.all_tokens)}


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         return self._token_to_id.get(lowerCAmelCase			,				self._token_to_id.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         return self._id_to_token.get(lowerCAmelCase			,				self.unk_token)


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			List[int]			,				lowerCAmelCase  :			Optional[List[int]] = None)	->				List[int]:




         """simple docstring"""

         lowercase__     =  [self.cls_token_id]
         lowercase__     =  [self.eos_token_id]  # No sep token in ESM vocabulary
         if token_ids_a is None:
               if self.eos_token_id is None:
                     return cls + token_ids_a
               else:
                     return cls + token_ids_a + sep
         elif self.eos_token_id is None:
               raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!')
         return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			List			,				lowerCAmelCase  :			Optional[List] = None			,				lowerCAmelCase  :			bool = False)	->				List[int]:




         """simple docstring"""

         if already_has_special_tokens:
               if token_ids_a is not None:
                     raise ValueError(
                         'You should not supply a second sequence if the provided sequence of '
                         'ids is already formatted with special tokens for the model.')

               return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
         lowercase__     =  [1] + ([0] * len(lowerCAmelCase)) + [1]
         if token_ids_a is not None:
               mask += [0] * len(lowerCAmelCase) + [1]
         return mask


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  os.path.join(lowerCAmelCase			,				(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt')
         with open(lowerCAmelCase			,				'w') as f:
               f.write('\n'.join(self.all_tokens))
         return (vocab_file,)


   @property
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				int:




         """simple docstring"""

         return self.get_vocab_size(with_added_tokens=lowerCAmelCase)



   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			Union[List[str], List[AddedToken]]			,				lowerCAmelCase  :			bool = False)	->				int:




         """simple docstring"""

         return super()._add_tokens(lowerCAmelCase			,				special_tokens=lowerCAmelCase)
