





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							:			Tuple         =			{
    "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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[Any]:




    """simple docstring"""




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

    _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(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

        if re.match(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							int(re.match(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				).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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE="ybelkada/segment-anything"				)							-> Optional[int]:




    """simple docstring"""




    _A     =							hf_hub_download(_SCREAMING_SNAKE_CASE							,     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=1_024							,     num_hidden_layers=24							,     num_attention_heads=16							,     global_attn_indexes=[5, 11, 17, 23]							,     )

        _A     =							SamConfig(
            vision_config=_SCREAMING_SNAKE_CASE							,     )
    elif "sam_vit_h" in model_name:
        _A     =							SamVisionConfig(
            hidden_size=1_280							,     num_hidden_layers=32							,     num_attention_heads=16							,     global_attn_indexes=[7, 15, 23, 31]							,     )

        _A     =							SamConfig(
            vision_config=_SCREAMING_SNAKE_CASE							,     )

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

    _A     =							SamImageProcessor()

    _A     =							SamProcessor(image_processor=_SCREAMING_SNAKE_CASE				)
    _A     =							SamModel(_SCREAMING_SNAKE_CASE				)

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

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

    _A     =							[[[400, 650]]]
    _A     =							[[1]]

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

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

    if model_name == "sam_vit_h_4b8939":
        assert scores[-1].item() == 0.579_8902_5115_9668

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

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

        assert scores[-1].item() == 0.9712_6030_9219_3604

        _A     =							((75, 275, 1_725, 850),)

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

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

        assert scores[-1].item() == 0.8686_0156_0592_6514

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

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

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

        assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
 __A							:			Tuple         =			argparse.ArgumentParser()
 __A							:			List[str]         =			["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							:			Union[str, Any]         =			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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:			Dict         =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			["BlenderbotTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			[
     "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BlenderbotForCausalLM",
     "BlenderbotForConditionalGeneration",
     "BlenderbotModel",
     "BlenderbotPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Union[str, Any]         =			[
     "TFBlenderbotForConditionalGeneration",
     "TFBlenderbotModel",
     "TFBlenderbotPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[Any]         =			[
     "FlaxBlenderbotForConditionalGeneration",
     "FlaxBlenderbotModel",
     "FlaxBlenderbotPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_blenderbot import (
     BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlenderbotConfig,
     BlenderbotOnnxConfig,
 )
 from .tokenization_blenderbot import BlenderbotTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blenderbot import (
      BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlenderbotForCausalLM,
      BlenderbotForConditionalGeneration,
      BlenderbotModel,
      BlenderbotPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_blenderbot import (
      TFBlenderbotForConditionalGeneration,
      TFBlenderbotModel,
      TFBlenderbotPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      FlaxBlenderbotPreTrainedModel,
  )

else:
 import sys

 __A							:			str         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





