



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_								=       logging.get_logger(__name__)

a_								=       {
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Any			,_UpperCamelCase      :			int				):
   for attribute in key.split('''.'''				):
      __lowerCamelCase    				=     getattr(_UpperCamelCase			,_UpperCamelCase				)

   if weight_type is not None:
      __lowerCamelCase    				=     getattr(_UpperCamelCase			,_UpperCamelCase				).shape
   else:
      __lowerCamelCase    				=     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":
      __lowerCamelCase    				=     value
   elif weight_type == "weight_g":
      __lowerCamelCase    				=     value
   elif weight_type == "weight_v":
      __lowerCamelCase    				=     value
   elif weight_type == "bias":
      __lowerCamelCase    				=     value
   else:
      __lowerCamelCase    				=     value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""				)





def 		a__      (		_UpperCamelCase      :			int			,_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Optional[int]				):
   __lowerCamelCase    				=     []
   __lowerCamelCase    				=     fairseq_model.state_dict()

   __lowerCamelCase    				=     hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __lowerCamelCase    				=     False
      if "conv_layers" in name:
         load_conv_layer(
             _UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,hf_model.config.feat_extract_norm == '''group'''			,)
         __lowerCamelCase    				=     True
      else:
         for key, mapped_key in MAPPING.items():
            __lowerCamelCase    				=     '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

            if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
               __lowerCamelCase    				=     True
               if "*" in mapped_key:
                  __lowerCamelCase    				=     name.split(_UpperCamelCase				)[0].split('''.'''				)[-2]
                  __lowerCamelCase    				=     mapped_key.replace('''*'''			,_UpperCamelCase				)
               if "weight_g" in name:
                  __lowerCamelCase    				=     '''weight_g'''
               elif "weight_v" in name:
                  __lowerCamelCase    				=     '''weight_v'''
               elif "weight" in name:
                  __lowerCamelCase    				=     '''weight'''
               elif "bias" in name:
                  __lowerCamelCase    				=     '''bias'''
               else:
                  __lowerCamelCase    				=     None
               set_recursively(_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase				)
            continue
      if not is_used:
         unused_weights.append(_UpperCamelCase				)

   logger.warning(F"""Unused weights: {unused_weights}"""				)





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			List[str]			,_UpperCamelCase      :			int			,_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			int				):
   __lowerCamelCase    				=     full_name.split('''conv_layers.'''				)[-1]
   __lowerCamelCase    				=     name.split('''.'''				)
   __lowerCamelCase    				=     int(items[0]				)
   __lowerCamelCase    				=     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."""
         )
         __lowerCamelCase    				=     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."""
         )
         __lowerCamelCase    				=     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."
         )
         __lowerCamelCase    				=     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."""
         )
         __lowerCamelCase    				=     value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
   else:
      unused_weights.append(_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			Optional[int]			,_UpperCamelCase      :			Dict				):
   __lowerCamelCase    				=     SEWConfig()
   if is_finetuned:
      __lowerCamelCase    				=     model.wav_encoder.wav_model.cfg
   else:
      __lowerCamelCase    				=     model.cfg

   __lowerCamelCase    				=     fs_config.conv_bias
   __lowerCamelCase    				=     eval(fs_config.conv_feature_layers				)
   __lowerCamelCase    				=     [x[0] for x in conv_layers]
   __lowerCamelCase    				=     [x[1] for x in conv_layers]
   __lowerCamelCase    				=     [x[2] for x in conv_layers]
   __lowerCamelCase    				=     '''gelu'''
   __lowerCamelCase    				=     '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   __lowerCamelCase    				=     0.0
   __lowerCamelCase    				=     fs_config.activation_fn.name
   __lowerCamelCase    				=     fs_config.encoder_embed_dim
   __lowerCamelCase    				=     0.02
   __lowerCamelCase    				=     fs_config.encoder_ffn_embed_dim
   __lowerCamelCase    				=     1e-5
   __lowerCamelCase    				=     fs_config.encoder_layerdrop
   __lowerCamelCase    				=     fs_config.encoder_attention_heads
   __lowerCamelCase    				=     fs_config.conv_pos_groups
   __lowerCamelCase    				=     fs_config.conv_pos
   __lowerCamelCase    				=     len(_UpperCamelCase				)
   __lowerCamelCase    				=     fs_config.encoder_layers
   __lowerCamelCase    				=     fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      __lowerCamelCase    				=     model.cfg
      __lowerCamelCase    				=     fs_config.final_dropout
      __lowerCamelCase    				=     fs_config.layerdrop
   __lowerCamelCase    				=     fs_config.activation_dropout
   __lowerCamelCase    				=     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   __lowerCamelCase    				=     fs_config.attention_dropout
   __lowerCamelCase    				=     fs_config.dropout_input
   __lowerCamelCase    				=     fs_config.dropout
   __lowerCamelCase    				=     fs_config.mask_channel_length
   __lowerCamelCase    				=     fs_config.mask_channel_prob
   __lowerCamelCase    				=     fs_config.mask_length
   __lowerCamelCase    				=     fs_config.mask_prob

   __lowerCamelCase    				=     '''Wav2Vec2FeatureExtractor'''
   __lowerCamelCase    				=     '''Wav2Vec2CTCTokenizer'''

   return config





@torch.no_grad()
def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			Any			,_UpperCamelCase      :			Tuple=None			,_UpperCamelCase      :			List[str]=None			,_UpperCamelCase      :			Union[str, Any]=True				):

   if is_finetuned:
      __lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase    				=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]			,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				)}				)
   else:
      __lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase    				=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

   if config_path is not None:
      __lowerCamelCase    				=     SEWConfig.from_pretrained(_UpperCamelCase				)
   else:
      __lowerCamelCase    				=     convert_config(model[0]			,_UpperCamelCase				)
   __lowerCamelCase    				=     model[0].eval()

   __lowerCamelCase    				=     True if config.feat_extract_norm == '''layer''' else False
   __lowerCamelCase    				=     WavaVecaFeatureExtractor(
       feature_size=1			,sampling_rate=1_60_00			,padding_value=0			,do_normalize=_UpperCamelCase			,return_attention_mask=_UpperCamelCase			,)

   if is_finetuned:
      if dict_path:
         __lowerCamelCase    				=     Dictionary.load(_UpperCamelCase				)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __lowerCamelCase    				=     target_dict.pad_index
         __lowerCamelCase    				=     target_dict.bos_index
         __lowerCamelCase    				=     target_dict.pad_index
         __lowerCamelCase    				=     target_dict.bos_index
         __lowerCamelCase    				=     target_dict.eos_index
         __lowerCamelCase    				=     len(target_dict.symbols				)
         __lowerCamelCase    				=     os.path.join(_UpperCamelCase			,'''vocab.json'''				)
         if not os.path.isdir(_UpperCamelCase				):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCamelCase				)				)
            return
         os.makedirs(_UpperCamelCase			,exist_ok=_UpperCamelCase				)
         with open(_UpperCamelCase			,'''w'''			,encoding='''utf-8'''				) as vocab_handle:
            json.dump(target_dict.indices			,_UpperCamelCase				)
         __lowerCamelCase    				=     WavaVecaCTCTokenizer(
             _UpperCamelCase			,unk_token=target_dict.unk_word			,pad_token=target_dict.pad_word			,bos_token=target_dict.bos_word			,eos_token=target_dict.eos_word			,word_delimiter_token='''|'''			,do_lower_case=_UpperCamelCase			,)
         __lowerCamelCase    				=     WavaVecaProcessor(feature_extractor=_UpperCamelCase			,tokenizer=_UpperCamelCase				)
         processor.save_pretrained(_UpperCamelCase				)

      __lowerCamelCase    				=     SEWForCTC(_UpperCamelCase				)
   else:
      __lowerCamelCase    				=     SEWModel(_UpperCamelCase				)
      feature_extractor.save_pretrained(_UpperCamelCase				)

   recursively_load_weights(_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase				)

   hf_model.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
  parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument(
      """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
  )
  a_								=       parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )











import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


a_								=       {
    """iou_prediction_head.layers.0""": """iou_prediction_head.proj_in""",
    """iou_prediction_head.layers.1""": """iou_prediction_head.layers.0""",
    """iou_prediction_head.layers.2""": """iou_prediction_head.proj_out""",
    """mask_decoder.output_upscaling.0""": """mask_decoder.upscale_conv1""",
    """mask_decoder.output_upscaling.1""": """mask_decoder.upscale_layer_norm""",
    """mask_decoder.output_upscaling.3""": """mask_decoder.upscale_conv2""",
    """mask_downscaling.0""": """mask_embed.conv1""",
    """mask_downscaling.1""": """mask_embed.layer_norm1""",
    """mask_downscaling.3""": """mask_embed.conv2""",
    """mask_downscaling.4""": """mask_embed.layer_norm2""",
    """mask_downscaling.6""": """mask_embed.conv3""",
    """point_embeddings""": """point_embed""",
    """pe_layer.positional_encoding_gaussian_matrix""": """shared_embedding.positional_embedding""",
    """image_encoder""": """vision_encoder""",
    """neck.0""": """neck.conv1""",
    """neck.1""": """neck.layer_norm1""",
    """neck.2""": """neck.conv2""",
    """neck.3""": """neck.layer_norm2""",
    """patch_embed.proj""": """patch_embed.projection""",
    """.norm""": """.layer_norm""",
    """blocks""": """layers""",
}





def 		a__      (		_UpperCamelCase      :			Optional[Any]				):
   __lowerCamelCase    				=     {}
   state_dict.pop('''pixel_mean'''			,_UpperCamelCase				)
   state_dict.pop('''pixel_std'''			,_UpperCamelCase				)

   __lowerCamelCase    				=     R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

   for key, value in state_dict.items():
      for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
         if key_to_modify in key:
            __lowerCamelCase    				=     key.replace(_UpperCamelCase			,_UpperCamelCase				)

      if re.match(_UpperCamelCase			,_UpperCamelCase				):
         __lowerCamelCase    				=     int(re.match(_UpperCamelCase			,_UpperCamelCase				).group(2				)				)
         if layer_nb == 0:
            __lowerCamelCase    				=     key.replace('''layers.0'''			,'''proj_in'''				)
         elif layer_nb == 1:
            __lowerCamelCase    				=     key.replace('''layers.1'''			,'''layers.0'''				)
         elif layer_nb == 2:
            __lowerCamelCase    				=     key.replace('''layers.2'''			,'''proj_out'''				)

      __lowerCamelCase    				=     value

   __lowerCamelCase    				=     model_state_dict[
       '''prompt_encoder.shared_embedding.positional_embedding'''
   ]

   return model_state_dict





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any			,_UpperCamelCase      :			Optional[int]="ybelkada/segment-anything"				):
   __lowerCamelCase    				=     hf_hub_download(_UpperCamelCase			,F"""checkpoints/{model_name}.pth"""				)

   if "sam_vit_b" in model_name:
      __lowerCamelCase    				=     SamConfig()
   elif "sam_vit_l" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=10_24			,num_hidden_layers=24			,num_attention_heads=16			,global_attn_indexes=[5, 11, 17, 23]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)
   elif "sam_vit_h" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=12_80			,num_hidden_layers=32			,num_attention_heads=16			,global_attn_indexes=[7, 15, 23, 31]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)

   __lowerCamelCase    				=     torch.load(_UpperCamelCase			,map_location='''cpu'''				)
   __lowerCamelCase    				=     replace_keys(_UpperCamelCase				)

   __lowerCamelCase    				=     SamImageProcessor()

   __lowerCamelCase    				=     SamProcessor(image_processor=_UpperCamelCase				)
   __lowerCamelCase    				=     SamModel(_UpperCamelCase				)

   hf_model.load_state_dict(_UpperCamelCase				)
   __lowerCamelCase    				=     hf_model.to('''cuda'''				)

   __lowerCamelCase    				=     '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
   __lowerCamelCase    				=     Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase				).raw				).convert('''RGB'''				)

   __lowerCamelCase    				=     [[[4_00, 6_50]]]
   __lowerCamelCase    				=     [[1]]

   __lowerCamelCase    				=     processor(images=np.array(_UpperCamelCase				)			,return_tensors='''pt'''				).to('''cuda'''				)

   with torch.no_grad():
      __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
   __lowerCamelCase    				=     output.iou_scores.squeeze()

   if model_name == "sam_vit_h_4b8939":
      assert scores[-1].item() == 0.579_890_251_159_668

      __lowerCamelCase    				=     processor(
          images=np.array(_UpperCamelCase				)			,input_points=_UpperCamelCase			,input_labels=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_712_603_092_193_604

      __lowerCamelCase    				=     ((75, 2_75, 17_25, 8_50),)

      __lowerCamelCase    				=     processor(images=np.array(_UpperCamelCase				)			,input_boxes=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.8_686_015_605_926_514

      # Test with 2 points and 1 image.
      __lowerCamelCase    				=     [[[4_00, 6_50], [8_00, 6_50]]]
      __lowerCamelCase    				=     [[1, 1]]

      __lowerCamelCase    				=     processor(
          images=np.array(_UpperCamelCase				)			,input_points=_UpperCamelCase			,input_labels=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  a_								=       ["""sam_vit_b_01ec64""", """sam_vit_h_4b8939""", """sam_vit_l_0b3195"""]
  parser.add_argument(
      """--model_name""",
      default="""sam_vit_h_4b8939""",
      choices=choices,
      type=str,
      help="""Path to hf config.json of model to convert""",
  )
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
      help="""Whether to push the model and processor to the hub after converting""",
  )
  parser.add_argument(
      """--model_hub_id""",
      default="""ybelkada/segment-anything""",
      choices=choices,
      type=str,
      help="""Path to hf config.json of model to convert""",
  )

  a_								=       parser.parse_args()

  convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)






