

'''simple docstring'''
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  :		List[str]  =		{
    '''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   (       lowercase_						:    Any	)		->	int:




  '''simple docstring'''



  lowercase									={}
  state_dict.pop('''pixel_mean'''     ,					lowercase_	)
  state_dict.pop('''pixel_std'''     ,					lowercase_	)

  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(lowercase_     ,					lowercase_	)

    if re.match(lowercase_     ,					lowercase_	):
      lowercase									=int(re.match(lowercase_     ,					lowercase_	).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   (       lowercase_						:    List[Any]     ,					lowercase_						:    int     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Any="ybelkada/segment-anything"	)		->	Tuple:




  '''simple docstring'''



  lowercase									=hf_hub_download(lowercase_     ,					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=1_0_2_4     ,					num_hidden_layers=2_4     ,					num_attention_heads=1_6     ,					global_attn_indexes=[5, 1_1, 1_7, 2_3]     ,					)

    lowercase									=SamConfig(
        vision_config=lowercase_     ,					)
  elif "sam_vit_h" in model_name:
    lowercase									=SamVisionConfig(
        hidden_size=1_2_8_0     ,					num_hidden_layers=3_2     ,					num_attention_heads=1_6     ,					global_attn_indexes=[7, 1_5, 2_3, 3_1]     ,					)

    lowercase									=SamConfig(
        vision_config=lowercase_     ,					)

  lowercase									=torch.load(lowercase_     ,					map_location='''cpu'''	)
  lowercase									=replace_keys(lowercase_	)

  lowercase									=SamImageProcessor()

  lowercase									=SamProcessor(image_processor=lowercase_	)
  lowercase									=SamModel(lowercase_	)

  hf_model.load_state_dict(lowercase_	)
  lowercase									=hf_model.to('''cuda'''	)

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

  lowercase									=[[[4_0_0, 6_5_0]]]
  lowercase									=[[1]]

  lowercase									=processor(images=np.array(lowercase_	)     ,					return_tensors='''pt'''	).to('''cuda'''	)

  with torch.no_grad():
    lowercase									=hf_model(**lowercase_	)
  lowercase									=output.iou_scores.squeeze()

  if model_name == "sam_vit_h_4b8939":
    assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

    with torch.no_grad():
      lowercase									=hf_model(**lowercase_	)
    lowercase									=output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

    lowercase									=((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

    lowercase									=processor(images=np.array(lowercase_	)     ,					input_boxes=lowercase_     ,					return_tensors='''pt'''	).to('''cuda'''	)

    with torch.no_grad():
      lowercase									=hf_model(**lowercase_	)
    lowercase									=output.iou_scores.squeeze()

    assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

    # Test with 2 points and 1 image.
    lowercase									=[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
    lowercase									=[[1, 1]]

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

    with torch.no_grad():
      lowercase									=hf_model(**lowercase_	)
    lowercase									=output.iou_scores.squeeze()

    assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
     _UpperCAmelCase  :		Tuple  =		argparse.ArgumentParser()
     _UpperCAmelCase  :		Optional[Any]  =		['''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  :		List[str]  =		parser.parse_args()

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







'''simple docstring'''


import math

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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[Any]								=       "data2vec-audio"




 def __init__(						self      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)      ,  _lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)      ,  _lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=19      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=0.05      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase="sum"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=256      ,  _lowerCAmelCase=(512, 512, 512, 512, 1500)      ,  _lowerCAmelCase=(5, 3, 3, 1, 1)      ,  _lowerCAmelCase=(1, 2, 3, 1, 1)      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase      ,  pad_token_id=_lowerCAmelCase      ,  bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase		)
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   feat_extract_activation
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   conv_bias
     _lowerCAmelCase						      =   num_conv_pos_embeddings
     _lowerCAmelCase						      =   num_conv_pos_embedding_groups
     _lowerCAmelCase						      =   conv_pos_kernel_size
     _lowerCAmelCase						      =   len(self.conv_dim		)
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   hidden_dropout
     _lowerCAmelCase						      =   attention_dropout
     _lowerCAmelCase						      =   activation_dropout
     _lowerCAmelCase						      =   feat_proj_dropout
     _lowerCAmelCase						      =   final_dropout
     _lowerCAmelCase						      =   layerdrop
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   use_weighted_layer_sum

     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
     _lowerCAmelCase						      =   mask_time_prob
     _lowerCAmelCase						      =   mask_time_length
     _lowerCAmelCase						      =   mask_time_min_masks
     _lowerCAmelCase						      =   mask_feature_prob
     _lowerCAmelCase						      =   mask_feature_length
     _lowerCAmelCase						      =   mask_feature_min_masks

     # ctc loss
     _lowerCAmelCase						      =   ctc_loss_reduction
     _lowerCAmelCase						      =   ctc_zero_infinity

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

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

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





 @property
 def 				_snake_case (						self		)     ->				str:
     return math.prod(self.conv_stride		)


