

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,
)


lowerCamelCase           =					{
    '''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__				(			_A      : Any							):





  '''simple docstring'''







  a__  ={}
  state_dict.pop('''pixel_mean'''	,      _A							)
  state_dict.pop('''pixel_std'''	,      _A							)

  a__  =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:
        a__  =key.replace(_A	,      _A							)

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

    a__  =value

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

  return model_state_dict




def 			UpperCAmelCase__				(			_A      : Tuple	,      _A      : Tuple	,      _A      : Any	,      _A      : Tuple="ybelkada/segment-anything"							):





  '''simple docstring'''







  a__  =hf_hub_download(_A	,      F"""checkpoints/{model_name}.pth"""							)

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

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

    a__  =SamConfig(
        vision_config=_A	,      )

  a__  =torch.load(_A	,      map_location='''cpu'''							)
  a__  =replace_keys(_A							)

  a__  =SamImageProcessor()

  a__  =SamProcessor(image_processor=_A							)
  a__  =SamModel(_A							)

  hf_model.load_state_dict(_A							)
  a__  =hf_model.to('''cuda'''							)

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

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

  a__  =processor(images=np.array(_A							)	,      return_tensors='''pt'''							).to('''cuda'''							)

  with torch.no_grad():
    a__  =hf_model(**_A							)
  a__  =output.iou_scores.squeeze()

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

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

    with torch.no_grad():
      a__  =hf_model(**_A							)
    a__  =output.iou_scores.squeeze()

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

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

    a__  =processor(images=np.array(_A							)	,      input_boxes=_A	,      return_tensors='''pt'''							).to('''cuda'''							)

    with torch.no_grad():
      a__  =hf_model(**_A							)
    a__  =output.iou_scores.squeeze()

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

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

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

    with torch.no_grad():
      a__  =hf_model(**_A							)
    a__  =output.iou_scores.squeeze()

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


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

       lowerCamelCase           =					parser.parse_args()

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''MvpTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''MvpForCausalLM''',
           '''MvpForConditionalGeneration''',
           '''MvpForQuestionAnswering''',
           '''MvpForSequenceClassification''',
           '''MvpModel''',
           '''MvpPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
