



'''simple docstring'''





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_UpperCamelCase											=		False
_UpperCamelCase											=		True
_UpperCamelCase											=		False


if __name__ == "__main__":
   _UpperCamelCase											=		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											=		parser.parse_args()

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

   _UpperCamelCase											=		'' 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											=		reader.read()
      _UpperCamelCase											=		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											=		UNetaDModel(**config)
   else:
      _UpperCamelCase											=		UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
      _UpperCamelCase											=		class_name(**config)

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

   _UpperCamelCase											=		dict(model.config)

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

      _UpperCamelCase											=		[k.replace('UNetRes', '') for k in config['down_block_types']]
      _UpperCamelCase											=		[k.replace('UNetRes', '') for k in config['up_block_types']]

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

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

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











'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""pegasus"""
  a_				   =["""past_key_values"""]
  a_				   ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




  def __init__(					self :      Any     ,      _a :      int=5_0265     ,      _a :      Tuple=1024     ,      _a :      Optional[Any]=12     ,      _a :      int=4096     ,      _a :      str=16     ,      _a :      Union[str, Any]=12     ,      _a :      Optional[Any]=4096     ,      _a :      Optional[int]=16     ,      _a :      Union[str, Any]=0.0     ,      _a :      str=0.0     ,      _a :      List[Any]=True     ,      _a :      Optional[int]=True     ,      _a :      Tuple="gelu"     ,      _a :      Dict=1024     ,      _a :      int=0.1     ,      _a :      Any=0.0     ,      _a :      Union[str, Any]=0.0     ,      _a :      Any=0.02     ,      _a :      Optional[Any]=0     ,      _a :      str=False     ,      _a :      Tuple=0     ,      _a :      Optional[int]=1     ,      _a :      Union[str, Any]=1     ,      **_a :      Optional[int]     ,      )   ->   Any:
    __lowerCamelCase			:			Union[str, Any]       			=					vocab_size
    __lowerCamelCase			:			int       			=					max_position_embeddings
    __lowerCamelCase			:			Union[str, Any]       			=					d_model
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_ffn_dim
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_layers
    __lowerCamelCase			:			List[Any]       			=					encoder_attention_heads
    __lowerCamelCase			:			Optional[int]       			=					decoder_ffn_dim
    __lowerCamelCase			:			int       			=					decoder_layers
    __lowerCamelCase			:			Tuple       			=					decoder_attention_heads
    __lowerCamelCase			:			Optional[Any]       			=					dropout
    __lowerCamelCase			:			Tuple       			=					attention_dropout
    __lowerCamelCase			:			int       			=					activation_dropout
    __lowerCamelCase			:			Dict       			=					activation_function
    __lowerCamelCase			:			List[Any]       			=					init_std
    __lowerCamelCase			:			List[str]       			=					encoder_layerdrop
    __lowerCamelCase			:			Union[str, Any]       			=					decoder_layerdrop
    __lowerCamelCase			:			Union[str, Any]       			=					use_cache
    __lowerCamelCase			:			Tuple       			=					encoder_layers
    __lowerCamelCase			:			int       			=					scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=_a     ,      eos_token_id=_a     ,      is_encoder_decoder=_a     ,      decoder_start_token_id=_a     ,      forced_eos_token_id=_a     ,      **_a     ,      )




  @property
  def 		_lowercase	(					self :      Optional[int]  )   ->   int:
    return self.encoder_attention_heads




  @property
  def 		_lowercase	(					self :      Any  )   ->   int:
    return self.d_model






