
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__		:     int     			=      False
UpperCAmelCase__		:     Dict     			=      True
UpperCAmelCase__		:     List[str]     			=      False


if __name__ == "__main__":
   UpperCAmelCase__		:     Any     			=      argparse.ArgumentParser()

   parser.add_argument(
       '--repo_path',
       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.')

   UpperCAmelCase__		:     List[str]     			=      parser.parse_args()

   UpperCAmelCase__		:     Union[str, Any]     			=      {
       'image_size': 'sample_size',
       'num_res_blocks': 'layers_per_block',
       'block_channels': 'block_out_channels',
       'down_blocks': 'down_block_types',
       'up_blocks': 'up_block_types',
       'downscale_freq_shift': 'freq_shift',
       'resnet_num_groups': 'norm_num_groups',
       'resnet_act_fn': 'act_fn',
       'resnet_eps': 'norm_eps',
       'num_head_channels': 'attention_head_dim',
   }

   UpperCAmelCase__		:     Any     			=      {
       'time_steps': 'time_proj',
       'mid': 'mid_block',
       'downsample_blocks': 'down_blocks',
       'upsample_blocks': 'up_blocks',
   }

   UpperCAmelCase__		:     Union[str, Any]     			=      '' if has_file(args.repo_path, 'config.json') else 'unet'

   with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
      UpperCAmelCase__		:     Union[str, Any]     			=      reader.read()
      UpperCAmelCase__		:     Optional[Any]     			=      json.loads(text)

   if do_only_config:
      for key in config_parameters_to_change.keys():
         config.pop(key, None)

   if has_file(args.repo_path, 'config.json'):
      UpperCAmelCase__		:     Optional[int]     			=      UNetaDModel(**config)
   else:
      UpperCAmelCase__		:     List[Any]     			=      UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
      UpperCAmelCase__		:     Union[str, Any]     			=      class_name(**config)

   if do_only_config:
      model.save_config(os.path.join(args.repo_path, subfolder))

   UpperCAmelCase__		:     str     			=      dict(model.config)

   if do_only_renaming:
      for key, value in config_parameters_to_change.items():
         if key in config:
            UpperCAmelCase__		:     List[str]     			=      config[key]
            del config[key]

      UpperCAmelCase__		:     Optional[Any]     			=      [k.replace('UNetRes', '') for k in config['down_block_types']]
      UpperCAmelCase__		:     Union[str, Any]     			=      [k.replace('UNetRes', '') for k in config['up_block_types']]

   if do_only_weights:
      UpperCAmelCase__		:     Any     			=      torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

      UpperCAmelCase__		:     Union[str, Any]     			=      {}
      for param_key, param_value in state_dict.items():
         if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
            continue
         UpperCAmelCase__		:     List[str]     			=      False
         for key, new_key in key_parameters_to_change.items():
            if not has_changed and param_key.split('.')[0] == key:
               UpperCAmelCase__		:     Tuple     			=      param_value
               UpperCAmelCase__		:     str     			=      True
         if not has_changed:
            UpperCAmelCase__		:     Union[str, Any]     			=      param_value

      model.load_state_dict(new_state_dict)
      model.save_pretrained(os.path.join(args.repo_path, subfolder))





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase__		:     List[Any]     			=      logging.get_logger(__name__)
logging.set_verbosity_info()






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if "xprophetnet" in prophetnet_checkpoint_path:
      _A:  List[Any]					=       XLMProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Union[str, Any]					=       XLMProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )
   else:
      _A:  Dict					=       ProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Tuple					=       ProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )

   _A:  Optional[int]					=       ['''key_proj''', '''value_proj''', '''query_proj''']

   _A:  List[Any]					=       {
       '''self_attn''': '''ngram_self_attn''',
       '''cross_attn''': '''encoder_attn''',
       '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
       '''feed_forward_layer_norm''': '''final_layer_norm''',
       '''feed_forward''': '''''',
       '''intermediate''': '''fc1''',
       '''output''': '''fc2''',
       '''key_proj''': '''k_proj''',
       '''query_proj''': '''q_proj''',
       '''value_proj''': '''v_proj''',
       '''word_embeddings''': '''embed_tokens''',
       '''embeddings_layer_norm''': '''emb_layer_norm''',
       '''relative_pos_embeddings''': '''relative_linear''',
       '''ngram_embeddings''': '''ngram_input_embed''',
       '''position_embeddings''': '''embed_positions''',
   }

   for key in loading_info["missing_keys"]:
      _A:  List[str]					=       key.split('''.'''      )

      if attributes[0] == "lm_head":
         _A:  Optional[int]					=       prophet
         _A:  Tuple					=       prophet_old
      else:
         _A:  Tuple					=       prophet.prophetnet
         _A:  Any					=       prophet_old.model

      _A:  int					=       False
      for attribute in attributes:
         if attribute in mapping:
            _A:  Optional[int]					=       mapping[attribute]
            if not hasattr(a		,		a      ) and len(a      ) > 0:
               _A:  int					=       attribute
         elif hasattr(a		,		a      ):
            _A:  Tuple					=       attribute

         if attribute == "weight":
            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
            _A:  Union[str, Any]					=       old_model.weight
            logger.info(f"""{attribute} is initialized."""      )
            _A:  Any					=       True
            break
         elif attribute == "bias":
            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
            _A:  str					=       old_model.bias
            logger.info(f"""{attribute} is initialized"""      )
            _A:  Dict					=       True
            break
         elif attribute in special_keys and hasattr(a		,		'''in_proj_weight'''      ):
            _A:  Optional[int]					=       old_model.in_proj_weight.shape[0] // 3
            _A:  Tuple					=       getattr(a		,		a      )
            param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
            param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
            if attribute == "query_proj":
               _A:  List[str]					=       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]      )
               _A:  List[Any]					=       nn.Parameter(old_model.in_proj_bias[:embed_dim]      )

            elif attribute == "key_proj":
               _A:  int					=       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
               _A:  Optional[int]					=       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
            elif attribute == "value_proj":
               _A:  List[Any]					=       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
               _A:  int					=       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
            _A:  Tuple					=       True
            break
         elif attribute == "position_embeddings":
            assert (
                model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
            ), "Hidden size has to match"
            assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
            _A:  Union[str, Any]					=       nn.Parameter(old_model.embed_positions.weight[:5_12, :]      )
            _A:  List[Any]					=       True
            break

         if attribute.isdigit():
            _A:  Tuple					=       model[int(a      )]
            _A:  int					=       old_model[int(a      )]
         else:
            _A:  Union[str, Any]					=       getattr(a		,		a      )

            if old_attribute == "":
               _A:  Union[str, Any]					=       old_model
            else:
               if not hasattr(a		,		a      ):
                  raise ValueError(f"""{old_model} does not have {old_attribute}"""      )
               _A:  List[Any]					=       getattr(a		,		a      )

      if not is_key_init:
         raise ValueError(f"""{key} was not correctly initialized!"""      )

   print(f"""Saving model to {pytorch_dump_folder_path}"""      )
   prophet.save_pretrained(a      )


if __name__ == "__main__":
   UpperCAmelCase__		:     Dict     			=      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   UpperCAmelCase__		:     Tuple     			=      parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



