
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,
)


__magic_name__            =  {
    '''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__    (      __lowerCAmelCase							):
    snake_case__						=				{}
    state_dict.pop("pixel_mean"    ,		__lowerCAmelCase							)
    state_dict.pop("pixel_std"    ,		__lowerCAmelCase							)

    snake_case__						=				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__						=				key.replace(__lowerCAmelCase    ,		__lowerCAmelCase							)

        if re.match(__lowerCAmelCase    ,		__lowerCAmelCase							):
            snake_case__						=				int(re.match(__lowerCAmelCase    ,		__lowerCAmelCase							).group(2							)							)
            if layer_nb == 0:
                snake_case__						=				key.replace("layers.0"    ,		"proj_in"							)
            elif layer_nb == 1:
                snake_case__						=				key.replace("layers.1"    ,		"layers.0"							)
            elif layer_nb == 2:
                snake_case__						=				key.replace("layers.2"    ,		"proj_out"							)

        snake_case__						=				value

    snake_case__						=				model_state_dict[
        "prompt_encoder.shared_embedding.positional_embedding"
    ]

    return model_state_dict





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase="ybelkada/segment-anything"							):
    snake_case__						=				hf_hub_download(__lowerCAmelCase    ,		F"""checkpoints/{model_name}.pth"""							)

    if "sam_vit_b" in model_name:
        snake_case__						=				SamConfig()
    elif "sam_vit_l" in model_name:
        snake_case__						=				SamVisionConfig(
            hidden_size=1_024    ,		num_hidden_layers=24    ,		num_attention_heads=16    ,		global_attn_indexes=[5, 11, 17, 23]    ,		)

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

        snake_case__						=				SamConfig(
            vision_config=__lowerCAmelCase    ,		)

    snake_case__						=				torch.load(__lowerCAmelCase    ,		map_location="cpu"							)
    snake_case__						=				replace_keys(__lowerCAmelCase							)

    snake_case__						=				SamImageProcessor()

    snake_case__						=				SamProcessor(image_processor=__lowerCAmelCase							)
    snake_case__						=				SamModel(__lowerCAmelCase							)

    hf_model.load_state_dict(__lowerCAmelCase							)
    snake_case__						=				hf_model.to("cuda"							)

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

    snake_case__						=				[[[400, 650]]]
    snake_case__						=				[[1]]

    snake_case__						=				processor(images=np.array(__lowerCAmelCase							)    ,		return_tensors="pt"							).to("cuda"							)

    with torch.no_grad():
        snake_case__						=				hf_model(**__lowerCAmelCase							)
    snake_case__						=				output.iou_scores.squeeze()

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

        snake_case__						=				processor(
            images=np.array(__lowerCAmelCase							)    ,		input_points=__lowerCAmelCase    ,		input_labels=__lowerCAmelCase    ,		return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            snake_case__						=				hf_model(**__lowerCAmelCase							)
        snake_case__						=				output.iou_scores.squeeze()

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

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

        snake_case__						=				processor(images=np.array(__lowerCAmelCase							)    ,		input_boxes=__lowerCAmelCase    ,		return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            snake_case__						=				hf_model(**__lowerCAmelCase							)
        snake_case__						=				output.iou_scores.squeeze()

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

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

        snake_case__						=				processor(
            images=np.array(__lowerCAmelCase							)    ,		input_points=__lowerCAmelCase    ,		input_labels=__lowerCAmelCase    ,		return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            snake_case__						=				hf_model(**__lowerCAmelCase							)
        snake_case__						=				output.iou_scores.squeeze()

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


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()
     __magic_name__            =  ['''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''',
     )

     __magic_name__            =  parser.parse_args()

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





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     __magic_name__            =  None


__magic_name__            =  logging.get_logger(__name__)


__magic_name__            =  '''▁'''

__magic_name__            =  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__            =  {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

__magic_name__            =  {
    '''google/pegasus-xsum''': 512,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		int    	=     VOCAB_FILES_NAMES
      _A						:		Dict    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A						:		Tuple    	=     PegasusTokenizer
      _A						:		str    	=     ['input_ids', 'attention_mask']
      def __init__(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase="<pad>"		,	lowerCamelCase="</s>"		,	lowerCamelCase="<unk>"		,	lowerCamelCase="<mask_2>"		,	lowerCamelCase="<mask_1>"		,	lowerCamelCase=None		,	lowerCamelCase=1_03		,	**lowerCamelCase		,	):
          snake_case__						=				offset

          if additional_special_tokens is not None:
              if not isinstance(lowerCamelCase		,	lowerCamelCase ):
                  raise TypeError(
                      F"""additional_special_tokens should be of type {type(lowerCamelCase )}, but is"""
                      F""" {type(lowerCamelCase )}""" )

              snake_case__						=				(
                  ([mask_token_sent] + additional_special_tokens)
                  if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                  else additional_special_tokens
              )
              # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
              additional_special_tokens_extended += [
                  F"""<unk_{i}>""" for i in range(len(lowerCamelCase )		,	self.offset - 1 )
              ]

              if len(set(lowerCamelCase ) ) != len(lowerCamelCase ):
                  raise ValueError(
                      "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                      F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
              snake_case__						=				additional_special_tokens_extended
          else:
              snake_case__						=				[mask_token_sent] if mask_token_sent is not None else []
              additional_special_tokens += [F"""<unk_{i}>""" for i in range(2		,	self.offset )]

          super().__init__(
              lowerCamelCase		,	tokenizer_file=lowerCamelCase		,	pad_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	mask_token_sent=lowerCamelCase		,	offset=lowerCamelCase		,	additional_special_tokens=lowerCamelCase		,	**lowerCamelCase		,	)
          snake_case__						=				vocab_file
          snake_case__						=				False if not self.vocab_file else True
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				set(self.all_special_ids )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
              raise ValueError(
                  "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                  F""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )

          return [1 if x in all_special_ids else 0 for x in seq]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):
          if already_has_special_tokens:
              return self._special_token_mask(lowerCamelCase )
          elif token_ids_a is None:
              return self._special_token_mask(lowerCamelCase ) + [1]
          else:
              return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=None ):
          if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer." )

          if not os.path.isdir(lowerCamelCase ):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)



