


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,
)


UpperCAmelCase__   	=							{
    '''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 				UpperCAmelCase_					(							__snake_case						)     ->			int:







 """simple docstring"""


 _lowercase							      ={}
 state_dict.pop('''pixel_mean'''     ,						__snake_case						)
 state_dict.pop('''pixel_std'''     ,						__snake_case						)

 _lowercase							      =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:
    _lowercase							      =key.replace(__snake_case     ,						__snake_case						)

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

  _lowercase							      =value

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

 return model_state_dict
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case="ybelkada/segment-anything"						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =hf_hub_download(__snake_case     ,						F"checkpoints/{model_name}.pth"						)

 if "sam_vit_b" in model_name:
  _lowercase							      =SamConfig()
 elif "sam_vit_l" in model_name:
  _lowercase							      =SamVisionConfig(
      hidden_size=1024     ,						num_hidden_layers=24     ,						num_attention_heads=16     ,						global_attn_indexes=[5, 11, 17, 23]     ,						)

  _lowercase							      =SamConfig(
      vision_config=__snake_case     ,						)
 elif "sam_vit_h" in model_name:
  _lowercase							      =SamVisionConfig(
      hidden_size=1280     ,						num_hidden_layers=32     ,						num_attention_heads=16     ,						global_attn_indexes=[7, 15, 23, 31]     ,						)

  _lowercase							      =SamConfig(
      vision_config=__snake_case     ,						)

 _lowercase							      =torch.load(__snake_case     ,						map_location='''cpu'''						)
 _lowercase							      =replace_keys(__snake_case						)

 _lowercase							      =SamImageProcessor()

 _lowercase							      =SamProcessor(image_processor=__snake_case						)
 _lowercase							      =SamModel(__snake_case						)

 hf_model.load_state_dict(__snake_case						)
 _lowercase							      =hf_model.to('''cuda'''						)

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

 _lowercase							      =[[[400, 650]]]
 _lowercase							      =[[1]]

 _lowercase							      =processor(images=np.array(__snake_case						)     ,						return_tensors='''pt'''						).to('''cuda'''						)

 with torch.no_grad():
  _lowercase							      =hf_model(**__snake_case						)
 _lowercase							      =output.iou_scores.squeeze()

 if model_name == "sam_vit_h_4b8939":
  assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

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

  with torch.no_grad():
   _lowercase							      =hf_model(**__snake_case						)
  _lowercase							      =output.iou_scores.squeeze()

  assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

  _lowercase							      =((75, 275, 1725, 850),)

  _lowercase							      =processor(images=np.array(__snake_case						)     ,						input_boxes=__snake_case     ,						return_tensors='''pt'''						).to('''cuda'''						)

  with torch.no_grad():
   _lowercase							      =hf_model(**__snake_case						)
  _lowercase							      =output.iou_scores.squeeze()

  assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

  # Test with 2 points and 1 image.
  _lowercase							      =[[[400, 650], [800, 650]]]
  _lowercase							      =[[1, 1]]

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

  with torch.no_grad():
   _lowercase							      =hf_model(**__snake_case						)
  _lowercase							      =output.iou_scores.squeeze()

  assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
  UpperCAmelCase__   	=							argparse.ArgumentParser()
  UpperCAmelCase__   	=							['''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''',
  )

  UpperCAmelCase__   	=							parser.parse_args()

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





import functools
import operator

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


UpperCAmelCase__   	=							logging.get_logger(__name__)

UpperCAmelCase__   	=							{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''wavlm'''





     def __init__(self		,     UpperCAmelCase=3_2		,     UpperCAmelCase=7_6_8		,     UpperCAmelCase=1_2		,     UpperCAmelCase=1_2		,     UpperCAmelCase=3_0_7_2		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-5		,     UpperCAmelCase="group"		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,     UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,     UpperCAmelCase=(1_0, 3, 3, 3, 3, 2, 2)		,     UpperCAmelCase=False		,     UpperCAmelCase=1_2_8		,     UpperCAmelCase=1_6		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=8_0_0		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=0.05		,     UpperCAmelCase=1_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.0		,     UpperCAmelCase=1_0		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0_0		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=0.1		,     UpperCAmelCase="mean"		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)		,     UpperCAmelCase=(5, 3, 3, 1, 1)		,     UpperCAmelCase=(1, 2, 3, 1, 1)		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=8_0		,     UpperCAmelCase=0		,     UpperCAmelCase=1		,     UpperCAmelCase=2		,     UpperCAmelCase=False		,     UpperCAmelCase=3		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Optional[Any]:
      super().__init__(**UpperCAmelCase		,     pad_token_id=UpperCAmelCase		,     bos_token_id=UpperCAmelCase		,     eos_token_id=UpperCAmelCase       )
      _lowercase							      =hidden_size
      _lowercase							      =feat_extract_norm
      _lowercase							      =feat_extract_activation
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =conv_bias
      _lowercase							      =num_buckets
      _lowercase							      =max_bucket_distance
      _lowercase							      =num_conv_pos_embeddings
      _lowercase							      =num_conv_pos_embedding_groups
      _lowercase							      =len(self.conv_dim       )
      _lowercase							      =num_hidden_layers
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =num_attention_heads
      _lowercase							      =hidden_dropout
      _lowercase							      =attention_dropout
      _lowercase							      =activation_dropout
      _lowercase							      =feat_proj_dropout
      _lowercase							      =final_dropout
      _lowercase							      =layerdrop
      _lowercase							      =layer_norm_eps
      _lowercase							      =initializer_range
      _lowercase							      =num_ctc_classes
      _lowercase							      =vocab_size
      _lowercase							      =do_stable_layer_norm
      _lowercase							      =use_weighted_layer_sum
      _lowercase							      =classifier_proj_size

      if (
          (len(self.conv_stride       ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
          or (len(self.conv_dim       ) != self.num_feat_extract_layers)
      ):
       raise ValueError(
           '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
           ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
           f" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"
           f" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."       )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _lowercase							      =apply_spec_augment
      _lowercase							      =mask_time_prob
      _lowercase							      =mask_time_length
      _lowercase							      =mask_time_min_masks
      _lowercase							      =mask_feature_prob
      _lowercase							      =mask_feature_length

      # parameters for pretraining with codevector quantized representations
      _lowercase							      =num_codevectors_per_group
      _lowercase							      =num_codevector_groups
      _lowercase							      =contrastive_logits_temperature
      _lowercase							      =num_negatives
      _lowercase							      =codevector_dim
      _lowercase							      =proj_codevector_dim
      _lowercase							      =diversity_loss_weight

      # ctc loss
      _lowercase							      =ctc_loss_reduction
      _lowercase							      =ctc_zero_infinity

      # adapter
      _lowercase							      =add_adapter
      _lowercase							      =adapter_kernel_size
      _lowercase							      =adapter_stride
      _lowercase							      =num_adapter_layers
      _lowercase							      =output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      _lowercase							      =classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =xvector_output_dim





     @property
     def      __A					(self       )     ->   int:
      return functools.reduce(operator.mul		,     self.conv_stride		,     1       )

