






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_						:						Optional[int]						      =					{
    '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     snake_case   (UpperCAmelCase__    )			->							Union[str, Any]:
  UpperCamelCase_:   str        =							{}
  state_dict.pop('pixel_mean'      ,       UpperCAmelCase__    )
  state_dict.pop('pixel_std'      ,       UpperCAmelCase__    )

  UpperCamelCase_:   List[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:
        UpperCamelCase_:   Dict        =							key.replace(UpperCAmelCase__      ,       UpperCAmelCase__    )

    if re.match(UpperCAmelCase__      ,       UpperCAmelCase__    ):
      UpperCamelCase_:   Optional[Any]        =							int(re.match(UpperCAmelCase__      ,       UpperCAmelCase__    ).group(2    )    )
      if layer_nb == 0:
        UpperCamelCase_:   Union[str, Any]        =							key.replace('layers.0'      ,       'proj_in'    )
      elif layer_nb == 1:
        UpperCamelCase_:   Any        =							key.replace('layers.1'      ,       'layers.0'    )
      elif layer_nb == 2:
        UpperCamelCase_:   Optional[int]        =							key.replace('layers.2'      ,       'proj_out'    )

    UpperCamelCase_:   List[Any]        =							value

  UpperCamelCase_:   str        =							model_state_dict[
      'prompt_encoder.shared_embedding.positional_embedding'
  ]

  return model_state_dict

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__="ybelkada/segment-anything"    )			->							Union[str, Any]:
  UpperCamelCase_:   int        =							hf_hub_download(UpperCAmelCase__      ,       F'''checkpoints/{model_name}.pth'''    )

  if "sam_vit_b" in model_name:
    UpperCamelCase_:   Optional[int]        =							SamConfig()
  elif "sam_vit_l" in model_name:
    UpperCamelCase_:   Union[str, Any]        =							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]      ,       )

    UpperCamelCase_:   Dict        =							SamConfig(
        vision_config=UpperCAmelCase__      ,       )
  elif "sam_vit_h" in model_name:
    UpperCamelCase_:   Any        =							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]      ,       )

    UpperCamelCase_:   Optional[Any]        =							SamConfig(
        vision_config=UpperCAmelCase__      ,       )

  UpperCamelCase_:   Optional[int]        =							torch.load(UpperCAmelCase__      ,       map_location='cpu'    )
  UpperCamelCase_:   Dict        =							replace_keys(UpperCAmelCase__    )

  UpperCamelCase_:   Tuple        =							SamImageProcessor()

  UpperCamelCase_:   List[str]        =							SamProcessor(image_processor=UpperCAmelCase__    )
  UpperCamelCase_:   Dict        =							SamModel(UpperCAmelCase__    )

  hf_model.load_state_dict(UpperCAmelCase__    )
  UpperCamelCase_:   List[str]        =							hf_model.to('cuda'    )

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

  UpperCamelCase_:   int        =							[[[4_0_0, 6_5_0]]]
  UpperCamelCase_:   str        =							[[1]]

  UpperCamelCase_:   str        =							processor(images=np.array(UpperCAmelCase__    )      ,       return_tensors='pt'    ).to('cuda'    )

  with torch.no_grad():
    UpperCamelCase_:   List[Any]        =							hf_model(**UpperCAmelCase__    )
  UpperCamelCase_:   Optional[int]        =							output.iou_scores.squeeze()

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

    UpperCamelCase_:   Optional[Any]        =							processor(
        images=np.array(UpperCAmelCase__    )      ,       input_points=UpperCAmelCase__      ,       input_labels=UpperCAmelCase__      ,       return_tensors='pt'    ).to('cuda'    )

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

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

    UpperCamelCase_:   Union[str, Any]        =							((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

    UpperCamelCase_:   List[Any]        =							processor(images=np.array(UpperCAmelCase__    )      ,       input_boxes=UpperCAmelCase__      ,       return_tensors='pt'    ).to('cuda'    )

    with torch.no_grad():
      UpperCamelCase_:   Optional[int]        =							hf_model(**UpperCAmelCase__    )
    UpperCamelCase_:   str        =							output.iou_scores.squeeze()

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

    # Test with 2 points and 1 image.
    UpperCamelCase_:   List[Any]        =							[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
    UpperCamelCase_:   Dict        =							[[1, 1]]

    UpperCamelCase_:   Optional[Any]        =							processor(
        images=np.array(UpperCAmelCase__    )      ,       input_points=UpperCAmelCase__      ,       input_labels=UpperCAmelCase__      ,       return_tensors='pt'    ).to('cuda'    )

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

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


if __name__ == "__main__":
   A_						:						str						      =					argparse.ArgumentParser()
   A_						:						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_						:						Tuple						      =					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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__snake_case				 =	{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

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

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	['''CLIPFeatureExtractor''']
		__snake_case				 =	['''CLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPModel''',
		    '''CLIPPreTrainedModel''',
		    '''CLIPTextModel''',
		    '''CLIPTextModelWithProjection''',
		    '''CLIPVisionModel''',
		    '''CLIPVisionModelWithProjection''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCLIPModel''',
		    '''TFCLIPPreTrainedModel''',
		    '''TFCLIPTextModel''',
		    '''TFCLIPVisionModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''FlaxCLIPModel''',
		    '''FlaxCLIPPreTrainedModel''',
		    '''FlaxCLIPTextModel''',
		    '''FlaxCLIPTextPreTrainedModel''',
		    '''FlaxCLIPVisionModel''',
		    '''FlaxCLIPVisionPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

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