




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_   		=     {
    "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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						{}
    state_dict.pop('''pixel_mean'''					,     lowercase_      )
    state_dict.pop('''pixel_std'''					,     lowercase_      )

    __UpperCamelCase  		=						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  		=						key.replace(lowercase_					,     lowercase_      )

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

        __UpperCamelCase  		=						value

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

    return model_state_dict




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_="ybelkada/segment-anything"      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						hf_hub_download(lowercase_					,     F"checkpoints/{model_name}.pth"      )

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

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

        __UpperCamelCase  		=						SamConfig(
            vision_config=lowercase_					,     )

    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    __UpperCamelCase  		=						replace_keys(lowercase_      )

    __UpperCamelCase  		=						SamImageProcessor()

    __UpperCamelCase  		=						SamProcessor(image_processor=lowercase_      )
    __UpperCamelCase  		=						SamModel(lowercase_      )

    hf_model.load_state_dict(lowercase_      )
    __UpperCamelCase  		=						hf_model.to('''cuda'''      )

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

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

    __UpperCamelCase  		=						processor(images=np.array(lowercase_      )					,     return_tensors='''pt'''      ).to('''cuda'''      )

    with torch.no_grad():
        __UpperCamelCase  		=						hf_model(**lowercase_      )
    __UpperCamelCase  		=						output.iou_scores.squeeze()

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

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

        with torch.no_grad():
            __UpperCamelCase  		=						hf_model(**lowercase_      )
        __UpperCamelCase  		=						output.iou_scores.squeeze()

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

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

        __UpperCamelCase  		=						processor(images=np.array(lowercase_      )					,     input_boxes=lowercase_					,     return_tensors='''pt'''      ).to('''cuda'''      )

        with torch.no_grad():
            __UpperCamelCase  		=						hf_model(**lowercase_      )
        __UpperCamelCase  		=						output.iou_scores.squeeze()

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

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

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

        with torch.no_grad():
            __UpperCamelCase  		=						hf_model(**lowercase_      )
        __UpperCamelCase  		=						output.iou_scores.squeeze()

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


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 a_   		=     ["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_   		=     parser.parse_args()

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












from ....configuration_utils import PretrainedConfig
from ....utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					"mctct"







      def __init__(     self					: List[Any] ,       snake_case					: Optional[Any]=8065 ,       snake_case					: Optional[int]=1536 ,       snake_case					: Any=36 ,       snake_case					: List[str]=6144 ,       snake_case					: Dict=4 ,       snake_case					: str=384 ,       snake_case					: List[str]=920 ,       snake_case					: Dict=1E-5 ,       snake_case					: Union[str, Any]=0.3 ,       snake_case					: Optional[Any]="relu" ,       snake_case					: str=0.02 ,       snake_case					: Optional[int]=0.3 ,       snake_case					: int=0.3 ,       snake_case					: Any=1 ,       snake_case					: int=0 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: List[Any]=1 ,       snake_case					: Dict=0.3 ,       snake_case					: int=1 ,       snake_case					: Optional[int]=(7,) ,       snake_case					: List[Any]=(3,) ,       snake_case					: Optional[int]=80 ,       snake_case					: List[str]=1 ,       snake_case					: int=None ,       snake_case					: List[str]="sum" ,       snake_case					: Tuple=False ,       **snake_case					: List[str] ,       ):
          super().__init__(**snake_case ,       pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case				)
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						attention_head_dim
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						layerdrop
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						pad_token_id
          __UpperCamelCase  		=						bos_token_id
          __UpperCamelCase  		=						eos_token_id
          __UpperCamelCase  		=						conv_glu_dim
          __UpperCamelCase  		=						conv_dropout
          __UpperCamelCase  		=						num_conv_layers
          __UpperCamelCase  		=						input_feat_per_channel
          __UpperCamelCase  		=						input_channels
          __UpperCamelCase  		=						conv_channels
          __UpperCamelCase  		=						ctc_loss_reduction
          __UpperCamelCase  		=						ctc_zero_infinity

          # prevents config testing fail with exporting to json
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						list(snake_case				)

          if len(self.conv_kernel				) != self.num_conv_layers:
              raise ValueError(
                  '''Configuration for convolutional module is incorrect. '''
                  '''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
                  F"but is `len(config.conv_kernel) = {len(self.conv_kernel				)}`, "
                  F"`config.num_conv_layers = {self.num_conv_layers}`."				)






