
'''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,
)


A__   :		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        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       List[str]:





  """simple docstring"""

  __A		:     Optional[Any]												=		{}
  state_dict.pop("""pixel_mean"""			,     _lowerCamelCase )
  state_dict.pop("""pixel_std"""			,     _lowerCamelCase )

  __A		:     Optional[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:
        __A		:     Optional[int]												=		key.replace(_lowerCamelCase			,     _lowerCamelCase )

    if re.match(_lowerCamelCase			,     _lowerCamelCase ):
      __A		:     Optional[int]												=		int(re.match(_lowerCamelCase			,     _lowerCamelCase ).group(2 ) )
      if layer_nb == 0:
        __A		:     List[str]												=		key.replace("""layers.0"""			,     """proj_in""" )
      elif layer_nb == 1:
        __A		:     List[str]												=		key.replace("""layers.1"""			,     """layers.0""" )
      elif layer_nb == 2:
        __A		:     Dict												=		key.replace("""layers.2"""			,     """proj_out""" )

    __A		:     Tuple												=		value

  __A		:     int												=		model_state_dict[
      'prompt_encoder.shared_embedding.positional_embedding'
  ]

  return model_state_dict







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  List[Any]="ybelkada/segment-anything" )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		hf_hub_download(_lowerCamelCase			,     F"checkpoints/{model_name}.pth" )

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

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

    __A		:     str												=		SamConfig(
        vision_config=_lowerCamelCase			,     )

  __A		:     Tuple												=		torch.load(_lowerCamelCase			,     map_location="""cpu""" )
  __A		:     Optional[Any]												=		replace_keys(_lowerCamelCase )

  __A		:     Dict												=		SamImageProcessor()

  __A		:     Optional[Any]												=		SamProcessor(image_processor=_lowerCamelCase )
  __A		:     List[str]												=		SamModel(_lowerCamelCase )

  hf_model.load_state_dict(_lowerCamelCase )
  __A		:     Union[str, Any]												=		hf_model.to("""cuda""" )

  __A		:     List[Any]												=		'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
  __A		:     Any												=		Image.open(requests.get(_lowerCamelCase			,     stream=_lowerCamelCase ).raw ).convert("""RGB""" )

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

  __A		:     Optional[int]												=		processor(images=np.array(_lowerCamelCase )			,     return_tensors="""pt""" ).to("""cuda""" )

  with torch.no_grad():
    __A		:     Union[str, Any]												=		hf_model(**_lowerCamelCase )
  __A		:     Tuple												=		output.iou_scores.squeeze()

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

    __A		:     List[Any]												=		processor(
        images=np.array(_lowerCamelCase )			,     input_points=_lowerCamelCase			,     input_labels=_lowerCamelCase			,     return_tensors="""pt""" ).to("""cuda""" )

    with torch.no_grad():
      __A		:     Optional[Any]												=		hf_model(**_lowerCamelCase )
    __A		:     int												=		output.iou_scores.squeeze()

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

    __A		:     List[str]												=		((75, 275, 1725, 850),)

    __A		:     List[Any]												=		processor(images=np.array(_lowerCamelCase )			,     input_boxes=_lowerCamelCase			,     return_tensors="""pt""" ).to("""cuda""" )

    with torch.no_grad():
      __A		:     Tuple												=		hf_model(**_lowerCamelCase )
    __A		:     List[str]												=		output.iou_scores.squeeze()

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

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

    __A		:     str												=		processor(
        images=np.array(_lowerCamelCase )			,     input_points=_lowerCamelCase			,     input_labels=_lowerCamelCase			,     return_tensors="""pt""" ).to("""cuda""" )

    with torch.no_grad():
      __A		:     str												=		hf_model(**_lowerCamelCase )
    __A		:     Dict												=		output.iou_scores.squeeze()

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


if __name__ == "__main__":
     A__   :		Union[str, Any] 		=argparse.ArgumentParser()
     A__   :		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',
     )

     A__   :		List[Any] 		=parser.parse_args()

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

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__   :		int 		=logging.get_logger(__name__)

A__   :		Union[str, Any] 		={
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''dpr'''
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    Tuple=3_05_22				,   lowerCamelCase			:    List[str]=7_68				,   lowerCamelCase			:    Union[str, Any]=12				,   lowerCamelCase			:    Tuple=12				,   lowerCamelCase			:    Dict=30_72				,   lowerCamelCase			:    List[Any]="gelu"				,   lowerCamelCase			:    List[Any]=0.1				,   lowerCamelCase			:    Union[str, Any]=0.1				,   lowerCamelCase			:    Optional[Any]=5_12				,   lowerCamelCase			:    List[Any]=2				,   lowerCamelCase			:    int=0.02				,   lowerCamelCase			:    Union[str, Any]=1e-1_2				,   lowerCamelCase			:    int=0				,   lowerCamelCase			:    List[str]="absolute"				,   lowerCamelCase			:    int = 0				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   super().__init__(pad_token_id=lowerCamelCase				,   **lowerCamelCase       )

   __A		:     Dict												=		vocab_size
   __A		:     int												=		hidden_size
   __A		:     str												=		num_hidden_layers
   __A		:     Union[str, Any]												=		num_attention_heads
   __A		:     Union[str, Any]												=		hidden_act
   __A		:     Optional[Any]												=		intermediate_size
   __A		:     str												=		hidden_dropout_prob
   __A		:     int												=		attention_probs_dropout_prob
   __A		:     int												=		max_position_embeddings
   __A		:     Dict												=		type_vocab_size
   __A		:     Dict												=		initializer_range
   __A		:     Union[str, Any]												=		layer_norm_eps
   __A		:     int												=		projection_dim
   __A		:     List[Any]												=		position_embedding_type



