"""simple docstring"""




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


_a :						Dict			       =  logging.get_logger(__name__)
_a :						Optional[int]			       =  {'vocab_file': 'spiece.model'}

_a :						List[str]			       =  {
    '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',
    }
}

_a :						List[Any]			       =  {
    '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,
}

_a :						Union[str, Any]			       =  '▁'
class 		__A				(					lowerCamelCase__						):
     _UpperCamelCase				:     str						   =						VOCAB_FILES_NAMES
     _UpperCamelCase				:     Union[str, Any]						   =						PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase				:     List[str]						   =						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__							,	):
       _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        __A    (						self    ):
       return len(self.sp_model    )






     def        __A    (						self    ):
       _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    ):
       _lowerCAmelCase		:			Tuple         =	self.__dict__.copy()
       _lowerCAmelCase		:			Union[str, Any]         =	None
       return state






     def __setstate__(						self							,	a__    ):
       _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        __A    (						self							,	a__    ):





       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        __A    (						self							,	a__    ):
       _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        __A    (						self							,	a__    ):
       return self.sp_model.PieceToId(a__    )






     def        __A    (						self							,	a__    ):
       return self.sp_model.IdToPiece(a__    )






     def        __A    (						self							,	a__    ):
       _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        __A    (						self							,	a__							,	a__ = None    ):
       _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        __A    (						self							,	a__							,	a__ = None							,	a__ = False    ):





       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        __A    (						self							,	a__							,	a__ = None    ):
       _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        __A    (						self							,	a__							,	a__ = None    ):





       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)

