





import os
import unicodedata
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


_lowerCamelCase      							=   logging.get_logger(__name__)
_lowerCamelCase      							=   {'vocab_file': 'spiece.model'}

_lowerCamelCase      							=   {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

_lowerCamelCase      							=   {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

_lowerCamelCase      							=   '▁'







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   VOCAB_FILES_NAMES
      UpperCAmelCase__							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


      def __init__(		self   ,				a__   ,				a__=True   ,				a__=True   ,				a__=False   ,				a__="[CLS]"   ,				a__="[SEP]"   ,				a__="<unk>"   ,				a__="[SEP]"   ,				a__="<pad>"   ,				a__="[CLS]"   ,				a__="[MASK]"   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		(
                 AddedToken(a__   ,				lstrip=a__   ,				rstrip=a__   ,				normalized=a__)
                 if isinstance(a__   ,				a__)
                 else mask_token
             )

             _lowerCamelCase				:  Optional[int]						=		{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 do_lower_case=a__   ,				remove_space=a__   ,				keep_accents=a__   ,				bos_token=a__   ,				eos_token=a__   ,				unk_token=a__   ,				sep_token=a__   ,				pad_token=a__   ,				cls_token=a__   ,				mask_token=a__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**a__   ,				)

             _lowerCamelCase				:  Optional[Any]						=		do_lower_case
             _lowerCamelCase				:  str						=		remove_space
             _lowerCamelCase				:  Any						=		keep_accents
             _lowerCamelCase				:  List[Any]						=		vocab_file

             _lowerCamelCase				:  Union[str, Any]						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
             self.sp_model.Load(a__)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return len(self.sp_model)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		{self.convert_ids_to_tokens(a__): i for i in range(self.vocab_size)}
             vocab.update(self.added_tokens_encoder)
             return vocab


      def __getstate__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		self.__dict__.copy()
             _lowerCamelCase				:  Union[str, Any]						=		None
             return state


      def __setstate__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		d

             # for backward compatibility
             if not hasattr(self   ,				'''sp_model_kwargs'''):
                    _lowerCamelCase				:  Optional[int]						=		{}

             _lowerCamelCase				:  List[str]						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
             self.sp_model.Load(self.vocab_file)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if self.remove_space:
                    _lowerCamelCase				:  Dict						=		''' '''.join(inputs.strip().split())
             else:
                    _lowerCamelCase				:  Optional[Any]						=		inputs
             _lowerCamelCase				:  Union[str, Any]						=		outputs.replace('''``'''   ,				'''"''').replace('''\'\''''   ,				'''"''')

             if not self.keep_accents:
                    _lowerCamelCase				:  Union[str, Any]						=		unicodedata.normalize('''NFKD'''   ,				a__)
                    _lowerCamelCase				:  List[str]						=		''''''.join([c for c in outputs if not unicodedata.combining(a__)])
             if self.do_lower_case:
                    _lowerCamelCase				:  int						=		outputs.lower()

             return outputs


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.preprocess_text(a__)
             _lowerCamelCase				:  List[Any]						=		self.sp_model.encode(a__   ,				out_type=a__)
             _lowerCamelCase				:  List[str]						=		[]
             for piece in pieces:
                    if len(a__) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
                           _lowerCamelCase				:  Optional[Any]						=		self.sp_model.EncodeAsPieces(piece[:-1].replace(a__   ,				''''''))
                           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                  if len(cur_pieces[0]) == 1:
                                         _lowerCamelCase				:  Union[str, Any]						=		cur_pieces[1:]
                                  else:
                                         _lowerCamelCase				:  Union[str, Any]						=		cur_pieces[0][1:]
                           cur_pieces.append(piece[-1])
                           new_pieces.extend(a__)
                    else:
                           new_pieces.append(a__)

             return new_pieces


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.sp_model.PieceToId(a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.sp_model.IdToPiece(a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  str						=		[]
             _lowerCamelCase				:  Tuple						=		''''''
             _lowerCamelCase				:  Optional[int]						=		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(a__) + token
                           _lowerCamelCase				:  int						=		True
                           _lowerCamelCase				:  int						=		[]
                    else:
                           current_sub_tokens.append(a__)
                           _lowerCamelCase				:  Dict						=		False
             out_string += self.sp_model.decode(a__)
             return out_string.strip()


      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				a__   ,				a__ = None   ,				a__ = False):



             """simple docstring"""





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

             if token_ids_a is not None:
                    return [1] + ([0] * len(a__)) + [1] + ([0] * len(a__)) + [1]
             return [1] + ([0] * len(a__)) + [1]


      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





             _lowerCamelCase				:  str						=		[self.sep_token_id]
             _lowerCamelCase				:  List[Any]						=		[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) * [0] + len(token_ids_a + sep) * [1]



      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





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

             if os.path.abspath(self.vocab_file) != os.path.abspath(a__) and os.path.isfile(self.vocab_file):
                    copyfile(self.vocab_file   ,				a__)
             elif not os.path.isfile(self.vocab_file):
                    with open(a__   ,				'''wb''') as fi:
                           _lowerCamelCase				:  Dict						=		self.sp_model.serialized_model_proto()
                           fi.write(a__)

             return (out_vocab_file,)








import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def  __UpperCAmelCase(   lowercase_   ):
       # vision encoder
       if "img_encoder.pos_embed" in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''img_encoder.pos_embed'''			,     '''vision_model.embeddings.position_embeddings'''   )
       if "img_encoder.patch_embed.proj" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''img_encoder.patch_embed.proj'''			,     '''vision_model.embeddings.patch_embeddings.projection'''   )
       if "img_encoder.patch_embed.norm" in name:
              _lowerCamelCase				:  Dict						=		name.replace('''img_encoder.patch_embed.norm'''			,     '''vision_model.embeddings.layernorm'''   )
       if "img_encoder.layers" in name:
              _lowerCamelCase				:  Dict						=		name.replace('''img_encoder.layers'''			,     '''vision_model.encoder.stages'''   )
       if "blocks" in name and "res" not in name:
              _lowerCamelCase				:  Optional[Any]						=		name.replace('''blocks'''			,     '''layers'''   )
       if "attn" in name and "pre_assign" not in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''attn'''			,     '''self_attn'''   )
       if "proj" in name and "self_attn" in name and "text" not in name:
              _lowerCamelCase				:  Optional[int]						=		name.replace('''proj'''			,     '''out_proj'''   )
       if "pre_assign_attn.attn.proj" in name:
              _lowerCamelCase				:  List[Any]						=		name.replace('''pre_assign_attn.attn.proj'''			,     '''pre_assign_attn.attn.out_proj'''   )
       if "norm1" in name:
              _lowerCamelCase				:  int						=		name.replace('''norm1'''			,     '''layer_norm1'''   )
       if "norm2" in name and "pre_assign" not in name:
              _lowerCamelCase				:  Optional[int]						=		name.replace('''norm2'''			,     '''layer_norm2'''   )
       if "img_encoder.norm" in name:
              _lowerCamelCase				:  Dict						=		name.replace('''img_encoder.norm'''			,     '''vision_model.layernorm'''   )
       # text encoder
       if "text_encoder.token_embedding" in name:
              _lowerCamelCase				:  List[Any]						=		name.replace('''text_encoder.token_embedding'''			,     '''text_model.embeddings.token_embedding'''   )
       if "text_encoder.positional_embedding" in name:
              _lowerCamelCase				:  Optional[Any]						=		name.replace('''text_encoder.positional_embedding'''			,     '''text_model.embeddings.position_embedding.weight'''   )
       if "text_encoder.transformer.resblocks." in name:
              _lowerCamelCase				:  Dict						=		name.replace('''text_encoder.transformer.resblocks.'''			,     '''text_model.encoder.layers.'''   )
       if "ln_1" in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''ln_1'''			,     '''layer_norm1'''   )
       if "ln_2" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''ln_2'''			,     '''layer_norm2'''   )
       if "c_fc" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''c_fc'''			,     '''fc1'''   )
       if "c_proj" in name:
              _lowerCamelCase				:  str						=		name.replace('''c_proj'''			,     '''fc2'''   )
       if "text_encoder" in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''text_encoder'''			,     '''text_model'''   )
       if "ln_final" in name:
              _lowerCamelCase				:  str						=		name.replace('''ln_final'''			,     '''final_layer_norm'''   )
       # projection layers
       if "img_projector.linear_hidden." in name:
              _lowerCamelCase				:  str						=		name.replace('''img_projector.linear_hidden.'''			,     '''visual_projection.'''   )
       if "img_projector.linear_out." in name:
              _lowerCamelCase				:  Union[str, Any]						=		name.replace('''img_projector.linear_out.'''			,     '''visual_projection.3.'''   )
       if "text_projector.linear_hidden" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''text_projector.linear_hidden'''			,     '''text_projection'''   )
       if "text_projector.linear_out" in name:
              _lowerCamelCase				:  Any						=		name.replace('''text_projector.linear_out'''			,     '''text_projection.3'''   )

       return name




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       for key in orig_state_dict.copy().keys():
              _lowerCamelCase				:  Optional[int]						=		orig_state_dict.pop(lowercase_   )

              if "qkv" in key:
                     # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     _lowerCamelCase				:  Tuple						=		key.split('''.'''   )
                     _lowerCamelCase,	_lowerCamelCase				:  Dict						=		int(key_split[2]   ), int(key_split[4]   )
                     _lowerCamelCase				:  Optional[int]						=		config.vision_config.hidden_size
                     if "weight" in key:
                            _lowerCamelCase				:  List[Any]						=		val[:dim, :]
                            _lowerCamelCase				:  List[Any]						=		val[dim : dim * 2, :]
                            _lowerCamelCase				:  Optional[Any]						=		val[-dim:, :]
                     else:
                            _lowerCamelCase				:  Union[str, Any]						=		val[:dim]
                            _lowerCamelCase				:  Optional[Any]						=		val[dim : dim * 2]
                            _lowerCamelCase				:  Any						=		val[-dim:]
              elif "in_proj" in key:
                     # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     _lowerCamelCase				:  Optional[int]						=		key.split('''.'''   )
                     _lowerCamelCase				:  Optional[Any]						=		int(key_split[3]   )
                     _lowerCamelCase				:  Union[str, Any]						=		config.text_config.hidden_size
                     if "weight" in key:
                            _lowerCamelCase				:  Any						=		val[:dim, :]
                            _lowerCamelCase				:  Optional[Any]						=		val[
                                dim : dim * 2, :
                            ]
                            _lowerCamelCase				:  Dict						=		val[-dim:, :]
                     else:
                            _lowerCamelCase				:  List[Any]						=		val[:dim]
                            _lowerCamelCase				:  Tuple						=		val[dim : dim * 2]
                            _lowerCamelCase				:  str						=		val[-dim:]
              else:
                     _lowerCamelCase				:  Optional[Any]						=		rename_key(lowercase_   )
                     # squeeze if necessary
                     if (
                         "text_projection.0" in new_name
                         or "text_projection.3" in new_name
                         or "visual_projection.0" in new_name
                         or "visual_projection.3" in new_name
                     ):
                            _lowerCamelCase				:  List[str]						=		val.squeeze_()
                     else:
                            _lowerCamelCase				:  Tuple						=		val

       return orig_state_dict




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  Union[str, Any]						=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase				:  Any						=		Image.open(requests.get(lowercase_			,     stream=lowercase_   ).raw   )
       return im




@torch.no_grad()
def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_="groupvit-gcc-yfcc"			,     lowercase_=False   ):
       _lowerCamelCase				:  Optional[Any]						=		GroupViTConfig()
       _lowerCamelCase				:  Any						=		GroupViTModel(lowercase_   ).eval()

       _lowerCamelCase				:  Optional[Any]						=		torch.load(lowercase_			,     map_location='''cpu'''   )['''model''']
       _lowerCamelCase				:  List[str]						=		convert_state_dict(lowercase_			,     lowercase_   )
       _lowerCamelCase,	_lowerCamelCase				:  List[str]						=		model.load_state_dict(lowercase_			,     strict=lowercase_   )
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase_   ) == 0)

       # verify result
       _lowerCamelCase				:  Optional[Any]						=		CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''   )
       _lowerCamelCase				:  Dict						=		prepare_img()
       _lowerCamelCase				:  str						=		processor(text=['''a photo of a cat''', '''a photo of a dog''']			,     images=lowercase_			,     padding=lowercase_			,     return_tensors='''pt'''   )

       with torch.no_grad():
              _lowerCamelCase				:  Optional[int]						=		model(**lowercase_   )

       if model_name == "groupvit-gcc-yfcc":
              _lowerCamelCase				:  Dict						=		torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]   )
       elif model_name == "groupvit-gcc-redcaps":
              _lowerCamelCase				:  Tuple						=		torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]   )
       else:
              raise ValueError(F"""Model name {model_name} not supported."""   )
       assert torch.allclose(outputs.logits_per_image			,     lowercase_			,     atol=1e-3   )

       processor.save_pretrained(lowercase_   )
       model.save_pretrained(lowercase_   )
       print('''Successfully saved processor and model to'''			,     lowercase_   )

       if push_to_hub:
              print('''Pushing to the hub...'''   )
              processor.push_to_hub(lowercase_			,     organization='''nielsr'''   )
              model.push_to_hub(lowercase_			,     organization='''nielsr'''   )


if __name__ == "__main__":
       _lowerCamelCase      							=   argparse.ArgumentParser()
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
       )
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
       parser.add_argument(
           '--model_name',
           default='groupvit-gccy-fcc',
           type=str,
           help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
       )
       parser.add_argument(
           '--push_to_hub',
           action='store_true',
           help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
       )
       _lowerCamelCase      							=   parser.parse_args()

       convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

