





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_   :      List[Any]			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      List[Any]			=   {'''vocab_file''': '''sentencepiece.bpe.model'''}

SCREAMING_SNAKE_CASE_   :      str			=   {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   {
    '''camembert-base''': 512,
}

SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   '''▁'''

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		VOCAB_FILES_NAMES
	__UpperCamelCase   =		PRETRAINED_VOCAB_FILES_MAP
	__UpperCamelCase   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__UpperCamelCase   =		['''input_ids''', '''attention_mask''']




	def __init__(						self							:     List[Any]					,      __lowerCamelCase							:     List[Any]					,      __lowerCamelCase							:     str="<s>"					,      __lowerCamelCase							:     List[str]="</s>"					,      __lowerCamelCase							:     List[Any]="</s>"					,      __lowerCamelCase							:     Union[str, Any]="<s>"					,      __lowerCamelCase							:     Dict="<unk>"					,      __lowerCamelCase							:     Dict="<pad>"					,      __lowerCamelCase							:     str="<mask>"					,      __lowerCamelCase							:     Tuple=["<s>NOTUSED", "</s>NOTUSED"]					,      __lowerCamelCase							:     Optional[Dict[str, Any]] = None					,      **__lowerCamelCase							:     Optional[Any]					,      )       -> None:

			'''simple docstring'''




			__lowercase											=     AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase					,      __lowerCamelCase   ) else mask_token

			__lowercase											=     {} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=__lowerCamelCase					,      eos_token=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      sep_token=__lowerCamelCase					,      cls_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      mask_token=__lowerCamelCase					,      additional_special_tokens=__lowerCamelCase					,      sp_model_kwargs=self.sp_model_kwargs					,      **__lowerCamelCase					,      )
			__lowercase											=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(str(__lowerCamelCase   )   )
			__lowercase											=     vocab_file
			# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
			# sentencepiece vocabulary (this is the case for <s> and </s>
			__lowercase											=     {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
			__lowercase											=     len(self.fairseq_tokens_to_ids   )
			__lowercase											=     len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
			__lowercase											=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     List[int]					,      __lowerCamelCase							:     Optional[List[int]] = None   )       -> List[int]:

			'''simple docstring'''




			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			__lowercase											=     [self.cls_token_id]
			__lowercase											=     [self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     List[int]					,      __lowerCamelCase							:     Optional[List[int]] = None					,      __lowerCamelCase							:     bool = False   )       -> List[int]:

			'''simple docstring'''




			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=__lowerCamelCase					,      token_ids_a=__lowerCamelCase					,      already_has_special_tokens=__lowerCamelCase   )

			if token_ids_a is None:
					return [1] + ([0] * len(__lowerCamelCase   )) + [1]
			return [1] + ([0] * len(__lowerCamelCase   )) + [1, 1] + ([0] * len(__lowerCamelCase   )) + [1]




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     List[int]					,      __lowerCamelCase							:     Optional[List[int]] = None   )       -> List[int]:

			'''simple docstring'''




			__lowercase											=     [self.sep_token_id]
			__lowercase											=     [self.cls_token_id]

			if token_ids_a is None:
					return len(cls + token_ids_a + sep   ) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]




	@property
	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Union[str, Any]:

			'''simple docstring'''




			return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )




	def    UpperCAmelCase			(						self							:     List[str]   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     {self.convert_ids_to_tokens(__lowerCamelCase   ): i for i in range(self.vocab_size   )}
			vocab.update(self.added_tokens_encoder   )
			return vocab




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     str   )       -> List[str]:

			'''simple docstring'''




			return self.sp_model.encode(__lowerCamelCase					,      out_type=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     Optional[Any]   )       -> Tuple:

			'''simple docstring'''




			if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
			elif self.sp_model.PieceToId(__lowerCamelCase   ) == 0:
					# Convert sentence piece unk token to fairseq unk token index
					return self.unk_token_id
			return self.fairseq_offset + self.sp_model.PieceToId(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Dict   )       -> Optional[Any]:

			'''simple docstring'''




			if index in self.fairseq_ids_to_tokens:
					return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset   )




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     []
			__lowercase											=     ''
			__lowercase											=     False
			for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							if not prev_is_special:
									out_string += " "
							out_string += self.sp_model.decode(__lowerCamelCase   ) + token
							__lowercase											=     True
							__lowercase											=     []
					else:
							current_sub_tokens.append(__lowerCamelCase   )
							__lowercase											=     False
			out_string += self.sp_model.decode(__lowerCamelCase   )
			return out_string.strip()




	def __getstate__(						self							:     Any   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     self.__dict__.copy()
			__lowercase											=     None
			return state




	def __setstate__(						self							:     Optional[Any]					,      __lowerCamelCase							:     Tuple   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     d

			# for backward compatibility
			if not hasattr(self					,      'sp_model_kwargs'   ):
					__lowercase											=     {}

			__lowercase											=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(self.vocab_file   )






	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     Optional[str] = None   )       -> Tuple[str]:

			'''simple docstring'''




			if not os.path.isdir(__lowerCamelCase   ):
					logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
					return
			__lowercase											=     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   ) and os.path.isfile(self.vocab_file   ):
					copyfile(self.vocab_file					,      __lowerCamelCase   )
			elif not os.path.isfile(self.vocab_file   ):
					with open(__lowerCamelCase					,      'wb'   ) as fi:
							__lowercase											=     self.sp_model.serialized_model_proto()
							fi.write(__lowerCamelCase   )

			return (out_vocab_file,)











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,
)


SCREAMING_SNAKE_CASE_   :      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  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     {}
		state_dict.pop('pixel_mean'						,   snake_case							)
		state_dict.pop('pixel_std'						,   snake_case							)

		__lowercase											=     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:
								__lowercase											=     key.replace(snake_case						,   snake_case							)

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

				__lowercase											=     value

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

		return model_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case="ybelkada/segment-anything"							)		->    int:
		__lowercase											=     hf_hub_download(snake_case						,   F"checkpoints/{model_name}.pth"							)

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

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

				__lowercase											=     SamConfig(
				    vision_config=snake_case						,   )

		__lowercase											=     torch.load(snake_case						,   map_location='cpu'							)
		__lowercase											=     replace_keys(snake_case							)

		__lowercase											=     SamImageProcessor()

		__lowercase											=     SamProcessor(image_processor=snake_case							)
		__lowercase											=     SamModel(snake_case							)

		hf_model.load_state_dict(snake_case							)
		__lowercase											=     hf_model.to('cuda'							)

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

		__lowercase											=     [[[400, 650]]]
		__lowercase											=     [[1]]

		__lowercase											=     processor(images=np.array(snake_case							)						,   return_tensors='pt'							).to('cuda'							)

		with torch.no_grad():
				__lowercase											=     hf_model(**snake_case							)
		__lowercase											=     output.iou_scores.squeeze()

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

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

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

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

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

				__lowercase											=     processor(images=np.array(snake_case							)						,   input_boxes=snake_case						,   return_tensors='pt'							).to('cuda'							)

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

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

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

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

				with torch.no_grad():
						__lowercase											=     hf_model(**snake_case							)
				__lowercase											=     output.iou_scores.squeeze()

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


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

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

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




