






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
       import timm


if is_torch_available():
       from torch import Tensor





class 					A__     (     __snake_case   ,    __snake_case ):

     '''simple docstring'''






     snake_case__					=					"""pixel_values"""
     snake_case__					=					False
     snake_case__					=					TimmBackboneConfig







     def __init__( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Tuple   ,						**_SCREAMING_SNAKE_CASE      :    Dict ):






           """simple docstring"""




           requires_backends(self   ,						'timm' )
           super().__init__(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  config

           if config.backbone is None:
                 raise ValueError('backbone is not set in the config. Please set it to a timm model name.' )

           if config.backbone not in timm.list_models():
                 raise ValueError(f'backbone {config.backbone} is not supported by timm.' )

           if hasattr(_SCREAMING_SNAKE_CASE   ,						'out_features' ) and config.out_features is not None:
                 raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.' )

           UpperCamelCase					       =  getattr(_SCREAMING_SNAKE_CASE   ,						'use_pretrained_backbone'   ,						_SCREAMING_SNAKE_CASE )
           if pretrained is None:
                 raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.' )

           # We just take the final layer by default. This matches the default for the transformers models.
           UpperCamelCase					       =  config.out_indices if getattr(_SCREAMING_SNAKE_CASE   ,						'out_indices'   ,						_SCREAMING_SNAKE_CASE ) is not None else (-1,)

           UpperCamelCase					       =  timm.create_model(
               config.backbone   ,						pretrained=_SCREAMING_SNAKE_CASE   ,						features_only=config.features_only   ,						in_chans=config.num_channels   ,						out_indices=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE   ,						)
           # These are used to control the output of the model when called. If output_hidden_states is True, then
           # return_layers is modified to include all layers.
           UpperCamelCase					       =  self._backbone.return_layers
           UpperCamelCase					       =  {layer['module']: str(_SCREAMING_SNAKE_CASE ) for i, layer in enumerate(self._backbone.feature_info.info )}
           super()._init_backbone(_SCREAMING_SNAKE_CASE )







     @classmethod
     def   _SCREAMING_SNAKE_CASE     ( cls      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]   ,						*_SCREAMING_SNAKE_CASE      :    Tuple   ,						**_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




           requires_backends(cls   ,						['vision', 'timm'] )
           from ...models.timm_backbone import TimmBackboneConfig

           UpperCamelCase					       =  kwargs.pop('config'   ,						TimmBackboneConfig() )

           UpperCamelCase					       =  kwargs.pop('use_timm_backbone'   ,						_SCREAMING_SNAKE_CASE )
           if not use_timm:
                 raise ValueError('use_timm_backbone must be True for timm backbones' )

           UpperCamelCase					       =  kwargs.pop('num_channels'   ,						config.num_channels )
           UpperCamelCase					       =  kwargs.pop('features_only'   ,						config.features_only )
           UpperCamelCase					       =  kwargs.pop('use_pretrained_backbone'   ,						config.use_pretrained_backbone )
           UpperCamelCase					       =  kwargs.pop('out_indices'   ,						config.out_indices )
           UpperCamelCase					       =  TimmBackboneConfig(
               backbone=_SCREAMING_SNAKE_CASE   ,						num_channels=_SCREAMING_SNAKE_CASE   ,						features_only=_SCREAMING_SNAKE_CASE   ,						use_pretrained_backbone=_SCREAMING_SNAKE_CASE   ,						out_indices=_SCREAMING_SNAKE_CASE   ,						)
           return super()._from_config(_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Any   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=None   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=None   ,						**_SCREAMING_SNAKE_CASE      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  return_dict if return_dict is not None else self.config.use_return_dict
           UpperCamelCase					       =  (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           UpperCamelCase					       =  output_attentions if output_attentions is not None else self.config.output_attentions

           if output_attentions:
                 raise ValueError('Cannot output attentions for timm backbones at the moment' )

           if output_hidden_states:
                 # We modify the return layers to include all the stages of the backbone
                 UpperCamelCase					       =  self._all_layers
                 UpperCamelCase					       =  self._backbone(_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  self._return_layers
                 UpperCamelCase					       =  tuple(hidden_states[i] for i in self.out_indices )
           else:
                 UpperCamelCase					       =  self._backbone(_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  None

           UpperCamelCase					       =  tuple(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  tuple(_SCREAMING_SNAKE_CASE ) if hidden_states is not None else None

           if not return_dict:
                 UpperCamelCase					       =  (feature_maps,)
                 if output_hidden_states:
                       UpperCamelCase					       =  output + (hidden_states,)
                 return output

           return BackboneOutput(feature_maps=_SCREAMING_SNAKE_CASE   ,						hidden_states=_SCREAMING_SNAKE_CASE   ,						attentions=_SCREAMING_SNAKE_CASE )











import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          'decoder.project_in_dim.weight': 'decoder.project_in.weight',
          'decoder.project_out_dim.weight': 'decoder.project_out.weight',
          'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
          'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  list(sd.keys())
      for key in keys:
            if ".qkv_proj." in key:
                  UpperCamelCase					       =  sd[key]
                  # We split QKV in separate Q,K,V

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

                  UpperCamelCase					       =  q
                  UpperCamelCase					       =  k
                  UpperCamelCase					       =  v
                  del sd[key]

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

      # Check results
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      model.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  Any        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--fairseq_path''',
           type=str,
           help=(
               '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
               ''' https://huggingface.co/models?other=opt_metasq'''
           ),
       )
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
       __magic_name__   :  str        =							parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



