



'''simple docstring'''





import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
_lowerCAmelCase      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

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

    lowerCAmelCase__ :  List[str]							      =					{
        """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:
            lowerCAmelCase__ :  Any							      =					sd.pop(UpperCamelCase			)

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

            lowerCAmelCase__ :  Optional[int]							      =					key.replace(""".qkv_proj."""							,    """.q_proj."""			)
            lowerCAmelCase__ :  List[str]							      =					key.replace(""".qkv_proj."""							,    """.k_proj."""			)
            lowerCAmelCase__ :  Optional[Any]							      =					key.replace(""".qkv_proj."""							,    """.v_proj."""			)

            lowerCAmelCase__ :  Union[str, Any]							      =					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
            lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  int							      =					torch.split(UpperCamelCase							,    depth // 3							,    dim=0			)

            lowerCAmelCase__ :  Dict							      =					q
            lowerCAmelCase__ :  Dict							      =					k
            lowerCAmelCase__ :  Any							      =					v
            del sd[key]

    return sd
@torch.no_grad()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					load_checkpoint(UpperCamelCase			)

    if config is not None:
        lowerCAmelCase__ :  Tuple							      =					OPTConfig.from_pretrained(UpperCamelCase			)
    else:
        lowerCAmelCase__ :  List[str]							      =					OPTConfig()

    lowerCAmelCase__ :  Optional[int]							      =					OPTModel(UpperCamelCase			).half().eval()
    model.load_state_dict(UpperCamelCase			)

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


if __name__ == "__main__":
 _lowerCAmelCase      						=	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.''')
 _lowerCAmelCase      						=	parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)







'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Optional[Any]				=      '''focalnet'''




  def __init__(    self  ,__UpperCAmelCase=224  ,__UpperCAmelCase=4  ,__UpperCAmelCase=3  ,__UpperCAmelCase=96  ,__UpperCAmelCase=False  ,__UpperCAmelCase=[192, 384, 768, 768]  ,__UpperCAmelCase=[2, 2, 6, 2]  ,__UpperCAmelCase=[2, 2, 2, 2]  ,__UpperCAmelCase=[3, 3, 3, 3]  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=4.0  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=False  ,__UpperCAmelCase=1E-4  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-5  ,__UpperCAmelCase=32  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		Optional[Any]:
      super().__init__(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					image_size
      lowerCAmelCase__ :  int							      =					patch_size
      lowerCAmelCase__ :  str							      =					num_channels
      lowerCAmelCase__ :  Dict							      =					embed_dim
      lowerCAmelCase__ :  List[str]							      =					use_conv_embed
      lowerCAmelCase__ :  List[Any]							      =					hidden_sizes
      lowerCAmelCase__ :  Dict							      =					depths
      lowerCAmelCase__ :  List[str]							      =					focal_levels
      lowerCAmelCase__ :  List[str]							      =					focal_windows
      lowerCAmelCase__ :  Dict							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					mlp_ratio
      lowerCAmelCase__ :  Tuple							      =					hidden_dropout_prob
      lowerCAmelCase__ :  Tuple							      =					drop_path_rate
      lowerCAmelCase__ :  Dict							      =					use_layerscale
      lowerCAmelCase__ :  Optional[Any]							      =					layerscale_value
      lowerCAmelCase__ :  str							      =					use_post_layernorm
      lowerCAmelCase__ :  Union[str, Any]							      =					use_post_layernorm_in_modulation
      lowerCAmelCase__ :  int							      =					normalize_modulator
      lowerCAmelCase__ :  Optional[Any]							      =					initializer_range
      lowerCAmelCase__ :  List[str]							      =					layer_norm_eps
      lowerCAmelCase__ :  List[Any]							      =					encoder_stride
      lowerCAmelCase__ :  Dict							      =					["""stem"""] + [F"""stage{idx}""" for idx in range(1  ,len(self.depths      ) + 1      )]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase  ,out_indices=__UpperCAmelCase  ,stage_names=self.stage_names      )


