



'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A			:   Dict			=      logging.get_logger(__name__)

A			:   str			=      {
    """layoutlmv2-base-uncased""": """https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json""",
    """layoutlmv2-large-uncased""": """https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json""",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
						import detectrona





class 							lowerCAmelCase_		(	a_						):
				__UpperCAmelCase							=	'layoutlmv2'





				def __init__(    self    :    int,    _snake_case    :    List[Any]=30_522,    _snake_case    :    Union[str, Any]=768,    _snake_case    :    Union[str, Any]=12,    _snake_case    :    Union[str, Any]=12,    _snake_case    :    int=3_072,    _snake_case    :    List[str]="gelu",    _snake_case    :    Optional[Any]=0.1,    _snake_case    :    Optional[int]=0.1,    _snake_case    :    str=512,    _snake_case    :    List[Any]=2,    _snake_case    :    Dict=0.02,    _snake_case    :    Optional[int]=1E-12,    _snake_case    :    Dict=0,    _snake_case    :    List[Any]=1_024,    _snake_case    :    str=128,    _snake_case    :    List[Any]=32,    _snake_case    :    List[Any]=True,    _snake_case    :    int=256,    _snake_case    :    Union[str, Any]=64,    _snake_case    :    int=True,    _snake_case    :    Tuple=[7, 7, 256],    _snake_case    :    Optional[int]=128,    _snake_case    :    Any=128,    _snake_case    :    Optional[Any]=True,    _snake_case    :    Union[str, Any]=True,    _snake_case    :    Dict=False,    _snake_case    :    Union[str, Any]=None,    **_snake_case    :    Any,    ):



							'''simple docstring'''







							super().__init__(
							    vocab_size=_snake_case,    hidden_size=_snake_case,    num_hidden_layers=_snake_case,    num_attention_heads=_snake_case,    intermediate_size=_snake_case,    hidden_act=_snake_case,    hidden_dropout_prob=_snake_case,    attention_probs_dropout_prob=_snake_case,    max_position_embeddings=_snake_case,    type_vocab_size=_snake_case,    initializer_range=_snake_case,    layer_norm_eps=_snake_case,    pad_token_id=_snake_case,    **_snake_case,    )
							snake_case   :							str						  =max_ad_position_embeddings
							snake_case   :							Dict						  =max_rel_pos
							snake_case   :							Any						  =rel_pos_bins
							snake_case   :							int						  =fast_qkv
							snake_case   :							Union[str, Any]						  =max_rel_ad_pos
							snake_case   :							Optional[int]						  =rel_ad_pos_bins
							snake_case   :							Optional[int]						  =convert_sync_batchnorm
							snake_case   :							Optional[int]						  =image_feature_pool_shape
							snake_case   :							Optional[int]						  =coordinate_size
							snake_case   :							Optional[int]						  =shape_size
							snake_case   :							Optional[Any]						  =has_relative_attention_bias
							snake_case   :							Optional[int]						  =has_spatial_attention_bias
							snake_case   :							List[str]						  =has_visual_segment_embedding
							snake_case   :							int						  =(
							    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
							)





				@classmethod
				def 					__snake_case       (    self    :    Any     ):



							'''simple docstring'''







							return {
							    "MODEL.MASK_ON": True,
							    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
							    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
							    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
							    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
							    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
							    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
							    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
							    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
							    "MODEL.POST_NMS_TOPK_TEST": 1_000,
							    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
							    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
							    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
							    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
							    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
							    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
							    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
							    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
							    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
							    "MODEL.RESNETS.DEPTH": 101,
							    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
							    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
							    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
							    "MODEL.RESNETS.NUM_GROUPS": 32,
							    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
							    "MODEL.RESNETS.STRIDE_IN_1X1": False,
							}






				def 					__snake_case       (    self    :    List[str]     ):



							'''simple docstring'''







							snake_case   :							List[str]						  =detectrona.config.get_cfg()
							for k, v in self.detectrona_config_args.items():
										snake_case   :							Any						  =k.split('''.'''     )
										snake_case   :							Optional[Any]						  =detectrona_config
										for attribute in attributes[:-1]:
													snake_case   :							Dict						  =getattr(_snake_case,    _snake_case     )
										setattr(_snake_case,    attributes[-1],    _snake_case     )

							return detectrona_config










'''simple docstring'''





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A			:   Dict			=      logging.get_logger(__name__)

A			:   Tuple			=      {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """mask_emb""": """masked_spec_embed""",
}
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   for attribute in key.split('''.'''							):
      snake_case   :							Optional[Any]						  =getattr(lowerCamelCase_					,						lowerCamelCase_							)

   if weight_type is not None:
      snake_case   :							int						  =getattr(lowerCamelCase_					,						lowerCamelCase_							).shape
   else:
      snake_case   :							Any						  =hf_pointer.shape

   assert hf_shape == value.shape, (
       F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
       F''' {value.shape} for {full_name}'''
   )

   if weight_type == "weight":
      snake_case   :							Dict						  =value
   elif weight_type == "weight_g":
      snake_case   :							Any						  =value
   elif weight_type == "weight_v":
      snake_case   :							int						  =value
   elif weight_type == "bias":
      snake_case   :							int						  =value
   else:
      snake_case   :							str						  =value

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							str						  =[]
   snake_case   :							Tuple						  =fairseq_model.state_dict()

   snake_case   :							Union[str, Any]						  =hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      snake_case   :							Optional[int]						  =False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						hf_model.config.feat_extract_norm == '''group'''					,						)
         snake_case   :							int						  =True
      else:
         for key, mapped_key in MAPPING.items():
            snake_case   :							Dict						  =mapped_key

            if key in name:
               snake_case   :							str						  =True
               if "*" in mapped_key:
                  snake_case   :							Tuple						  =name.split(lowerCamelCase_							)[0].split('''.'''							)[-2]
                  snake_case   :							Optional[int]						  =mapped_key.replace('''*'''					,						lowerCamelCase_							)
               if "weight_g" in name:
                  snake_case   :							int						  ='''weight_g'''
               elif "weight_v" in name:
                  snake_case   :							List[str]						  ='''weight_v'''
               elif "weight" in name:
                  snake_case   :							Any						  ='''weight'''
               elif "bias" in name:
                  snake_case   :							List[str]						  ='''bias'''
               else:
                  snake_case   :							Tuple						  =None
               set_recursively(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							)
            continue
      if not is_used:
         unused_weights.append(lowerCamelCase_							)

   logger.warning(F'''Unused weights: {unused_weights}'''							)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							List[str]						  =full_name.split('''conv_layers.'''							)[-1]
   snake_case   :							Any						  =name.split('''.'''							)
   snake_case   :							Tuple						  =int(items[0]							)
   snake_case   :							int						  =int(items[1]							)

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
         )
         snake_case   :							Optional[int]						  =value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
         )
         snake_case   :							str						  =value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
             " found."
         )
         snake_case   :							Optional[int]						  =value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
         )
         snake_case   :							Union[str, Any]						  =value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
   else:
      unused_weights.append(lowerCamelCase_							)
def 		_a   (  lowerCamelCase_							):
   snake_case   :							Any						  =HubertConfig()
   snake_case   :							List[str]						  =model.config

   snake_case   :							List[str]						  =fs_config.activation_dropout
   snake_case   :							List[Any]						  =False
   snake_case   :							Optional[Any]						  =fs_config.attention_dropout
   snake_case   :							Optional[int]						  =False
   snake_case   :							Any						  =eval(fs_config.extractor_conv_feature_layers							)
   snake_case   :							Optional[int]						  =[x[0] for x in conv_layers]
   snake_case   :							Any						  =[x[1] for x in conv_layers]
   snake_case   :							Optional[Any]						  =[x[2] for x in conv_layers]
   snake_case   :							Optional[Any]						  ='''gelu'''
   snake_case   :							Optional[Any]						  ='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   snake_case   :							Optional[int]						  =False
   snake_case   :							Optional[Any]						  =0.0
   snake_case   :							Optional[int]						  =0.0
   snake_case   :							Dict						  =fs_config.activation_fn
   snake_case   :							Any						  =fs_config.dropout
   snake_case   :							Optional[int]						  =fs_config.encoder_embed_dim
   snake_case   :							Dict						  =0.02
   snake_case   :							Dict						  =fs_config.encoder_ffn_embed_dim
   snake_case   :							str						  =1e-5
   snake_case   :							List[Any]						  =0.0
   snake_case   :							Dict						  =fs_config.encoder_attention_heads
   snake_case   :							Union[str, Any]						  =fs_config.conv_pos_groups
   snake_case   :							Union[str, Any]						  =fs_config.conv_pos
   snake_case   :							Dict						  =len(lowerCamelCase_							)
   snake_case   :							str						  =fs_config.encoder_layers

   return config
@torch.no_grad()
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_=None							):
   snake_case   :							Union[str, Any]						  =distilhubert().model.model

   if config_path is not None:
      snake_case   :							int						  =HubertConfig.from_pretrained(lowerCamelCase_							)
   else:
      snake_case   :							Optional[Any]						  =convert_config(lowerCamelCase_							)
   snake_case   :							Tuple						  =model.eval()

   snake_case   :							Optional[Any]						  =WavaVecaFeatureExtractor(
       feature_size=1					,						sampling_rate=1_60_00					,						padding_value=0					,						do_normalize=lowerCamelCase_					,						return_attention_mask=lowerCamelCase_					,						)
   snake_case   :							Any						  =HubertModel(lowerCamelCase_							)

   recursively_load_weights(lowerCamelCase_					,						lowerCamelCase_							)

   feature_extractor.save_pretrained(lowerCamelCase_							)
   hf_model.save_pretrained(lowerCamelCase_							)


if __name__ == "__main__":
      A			:   Tuple			=      argparse.ArgumentParser()
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      A			:   int			=      parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





