





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[Any]    	=							{
    "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__							(   SCREAMING_SNAKE_CASE__)       ->   int:
  __snake_case:						int			  =						{}
  state_dict.pop("""pixel_mean""" , SCREAMING_SNAKE_CASE__)
  state_dict.pop("""pixel_std""" , SCREAMING_SNAKE_CASE__)

  __snake_case:						Union[str, Any]			  =						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:
        __snake_case:						List[str]			  =						key.replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

    if re.match(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
      __snake_case:						Optional[int]			  =						int(re.match(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__).group(2))
      if layer_nb == 0:
        __snake_case:						Tuple			  =						key.replace("""layers.0""" , """proj_in""")
      elif layer_nb == 1:
        __snake_case:						Union[str, Any]			  =						key.replace("""layers.1""" , """layers.0""")
      elif layer_nb == 2:
        __snake_case:						Optional[Any]			  =						key.replace("""layers.2""" , """proj_out""")

    __snake_case:						Union[str, Any]			  =						value

  __snake_case:						Tuple			  =						model_state_dict[
      """prompt_encoder.shared_embedding.positional_embedding"""
  ]

  return model_state_dict







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="ybelkada/segment-anything")       ->   Optional[int]:
  __snake_case:						List[Any]			  =						hf_hub_download(SCREAMING_SNAKE_CASE__ , F'''checkpoints/{model_name}.pth''')

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

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

    __snake_case:						int			  =						SamConfig(
        vision_config=SCREAMING_SNAKE_CASE__ , )

  __snake_case:						List[str]			  =						torch.load(SCREAMING_SNAKE_CASE__ , map_location="""cpu""")
  __snake_case:						int			  =						replace_keys(SCREAMING_SNAKE_CASE__)

  __snake_case:						List[str]			  =						SamImageProcessor()

  __snake_case:						Optional[Any]			  =						SamProcessor(image_processor=SCREAMING_SNAKE_CASE__)
  __snake_case:						str			  =						SamModel(SCREAMING_SNAKE_CASE__)

  hf_model.load_state_dict(SCREAMING_SNAKE_CASE__)
  __snake_case:						List[Any]			  =						hf_model.to("""cuda""")

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

  __snake_case:						Union[str, Any]			  =						[[[400, 650]]]
  __snake_case:						int			  =						[[1]]

  __snake_case:						List[str]			  =						processor(images=np.array(SCREAMING_SNAKE_CASE__) , return_tensors="""pt""").to("""cuda""")

  with torch.no_grad():
    __snake_case:						Union[str, Any]			  =						hf_model(**SCREAMING_SNAKE_CASE__)
  __snake_case:						List[str]			  =						output.iou_scores.squeeze()

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

    __snake_case:						List[str]			  =						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():
      __snake_case:						Optional[int]			  =						hf_model(**SCREAMING_SNAKE_CASE__)
    __snake_case:						Tuple			  =						output.iou_scores.squeeze()

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

    __snake_case:						Optional[int]			  =						((75, 275, 1725, 850),)

    __snake_case:						Union[str, Any]			  =						processor(images=np.array(SCREAMING_SNAKE_CASE__) , input_boxes=SCREAMING_SNAKE_CASE__ , return_tensors="""pt""").to("""cuda""")

    with torch.no_grad():
      __snake_case:						List[str]			  =						hf_model(**SCREAMING_SNAKE_CASE__)
    __snake_case:						Any			  =						output.iou_scores.squeeze()

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

    # Test with 2 points and 1 image.
    __snake_case:						Dict			  =						[[[400, 650], [800, 650]]]
    __snake_case:						Any			  =						[[1, 1]]

    __snake_case:						List[Any]			  =						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():
      __snake_case:						List[str]			  =						hf_model(**SCREAMING_SNAKE_CASE__)
    __snake_case:						str			  =						output.iou_scores.squeeze()

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


if __name__ == "__main__":
      __UpperCAmelCase	:       Optional[Any]    	=							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	:       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,
)


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

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






