





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
			'''simple docstring'''


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

			return name



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
			'''simple docstring'''


			for key in orig_state_dict.copy().keys():
						A_     :				Dict			      =	orig_state_dict.pop(_lowerCAmelCase     )

						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
									A_     :				int			      =	key.split("""."""     )
									A_     :				List[Any]			      =	int(key_split[2]     ), int(key_split[4]     )
									A_     :				Any			      =	config.vision_config.hidden_size
									if "weight" in key:
												A_     :				Optional[Any]			      =	val[:dim, :]
												A_     :				Union[str, Any]			      =	val[dim : dim * 2, :]
												A_     :				List[str]			      =	val[-dim:, :]
									else:
												A_     :				Tuple			      =	val[:dim]
												A_     :				int			      =	val[dim : dim * 2]
												A_     :				Dict			      =	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
									A_     :				int			      =	key.split("""."""     )
									A_     :				Optional[int]			      =	int(key_split[3]     )
									A_     :				Dict			      =	config.text_config.hidden_size
									if "weight" in key:
												A_     :				str			      =	val[:dim, :]
												A_     :				Optional[int]			      =	val[
												    dim : dim * 2, :
												]
												A_     :				int			      =	val[-dim:, :]
									else:
												A_     :				List[Any]			      =	val[:dim]
												A_     :				Tuple			      =	val[dim : dim * 2]
												A_     :				Any			      =	val[-dim:]
						else:
									A_     :				str			      =	rename_key(_lowerCAmelCase     )
									# 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
									):
												A_     :				str			      =	val.squeeze_()
									else:
												A_     :				Tuple			      =	val

			return orig_state_dict



def     _lowerCAmelCase		(  ):
			'''simple docstring'''


			A_     :				List[Any]			      =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			A_     :				Union[str, Any]			      =	Image.open(requests.get(_lowerCAmelCase  ,stream=_lowerCAmelCase     ).raw     )
			return im



@torch.no_grad()
def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase="groupvit-gcc-yfcc"  ,_lowerCAmelCase=False     ):
			'''simple docstring'''


			A_     :				int			      =	GroupViTConfig()
			A_     :				Tuple			      =	GroupViTModel(_lowerCAmelCase     ).eval()

			A_     :				List[str]			      =	torch.load(_lowerCAmelCase  ,map_location="""cpu"""     )["""model"""]
			A_     :				Dict			      =	convert_state_dict(_lowerCAmelCase  ,_lowerCAmelCase     )
			A_     :				Optional[int]			      =	model.load_state_dict(_lowerCAmelCase  ,strict=_lowerCAmelCase     )
			assert missing_keys == ["text_model.embeddings.position_ids"]
			assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowerCAmelCase     ) == 0)

			# verify result
			A_     :				int			      =	CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
			A_     :				str			      =	prepare_img()
			A_     :				Dict			      =	processor(text=["""a photo of a cat""", """a photo of a dog"""]  ,images=_lowerCAmelCase  ,padding=_lowerCAmelCase  ,return_tensors="""pt"""     )

			with torch.no_grad():
						A_     :				Union[str, Any]			      =	model(**_lowerCAmelCase     )

			if model_name == "groupvit-gcc-yfcc":
						A_     :				List[str]			      =	torch.tensor([[13.3523, 6.3629]]     )
			elif model_name == "groupvit-gcc-redcaps":
						A_     :				Dict			      =	torch.tensor([[16.1873, 8.6230]]     )
			else:
						raise ValueError(f"""Model name {model_name} not supported."""     )
			assert torch.allclose(outputs.logits_per_image  ,_lowerCAmelCase  ,atol=1e-3     )

			processor.save_pretrained(_lowerCAmelCase     )
			model.save_pretrained(_lowerCAmelCase     )
			print("""Successfully saved processor and model to"""  ,_lowerCAmelCase     )

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













import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     """▁"""

_lowerCAmelCase 				=     {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_lowerCAmelCase 				=     {
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

_lowerCAmelCase 				=     {
    """facebook/m2m100_418M""": 1_024,
}

# fmt: off
_lowerCAmelCase 				=     {
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							PRETRAINED_VOCAB_FILES_MAP
      a						       =							['''input_ids''', '''attention_mask''']

      a						       =							[]
      a						       =							[]
      def __init__(		self      ,       a__      ,       a__      ,       a__=None      ,       a__=None      ,       a__="<s>"      ,       a__="</s>"      ,       a__="</s>"      ,       a__="<pad>"      ,       a__="<unk>"      ,       a__="m2m100"      ,       a__ = None      ,       a__=8      ,       **a__      ,       ):
         A_     :				Any			      =	{} if sp_model_kwargs is None else sp_model_kwargs

         A_     :				Dict			      =	language_codes
         A_     :				Union[str, Any]			      =	FAIRSEQ_LANGUAGE_CODES[language_codes]
         A_     :				int			      =	{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

         A_     :				Union[str, Any]			      =	kwargs.get("""additional_special_tokens"""      ,       []       )
         kwargs["additional_special_tokens"] += [
             self.get_lang_token(a__       )
             for lang_code in fairseq_language_code
             if self.get_lang_token(a__       ) not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=a__      ,       tgt_lang=a__      ,       bos_token=a__      ,       eos_token=a__      ,       sep_token=a__      ,       unk_token=a__      ,       pad_token=a__      ,       language_codes=a__      ,       sp_model_kwargs=self.sp_model_kwargs      ,       num_madeup_words=a__      ,       **a__      ,       )

         A_     :				List[str]			      =	vocab_file
         A_     :				Dict			      =	load_json(a__       )
         A_     :				str			      =	{v: k for k, v in self.encoder.items()}
         A_     :				Optional[Any]			      =	spm_file
         A_     :				Any			      =	load_spm(a__      ,       self.sp_model_kwargs       )

         A_     :				Union[str, Any]			      =	len(self.encoder       )

         A_     :				Dict			      =	{
             self.get_lang_token(a__       ): self.encoder_size + i for i, lang_code in enumerate(a__       )
         }
         A_     :				Optional[Any]			      =	{lang_code: self.encoder_size + i for i, lang_code in enumerate(a__       )}
         A_     :				Any			      =	{v: k for k, v in self.lang_token_to_id.items()}

         A_     :				Tuple			      =	src_lang if src_lang is not None else """en"""
         A_     :				str			      =	tgt_lang
         A_     :				Optional[int]			      =	self.get_lang_id(self._src_lang       )
         self.set_src_lang_special_tokens(self._src_lang       )

         A_     :				Optional[int]			      =	num_madeup_words
      @property
      def 		_lowerCamelCase      (		self       ):
         return len(self.encoder       ) + len(self.lang_token_to_id       )
      @property
      def 		_lowerCamelCase      (		self       ):
         return self._src_lang
      @src_lang.setter
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Any			      =	new_src_lang
         self.set_src_lang_special_tokens(self._src_lang       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.sp_model.encode(a__      ,       out_type=a__       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         if token in self.lang_token_to_id:
            return self.lang_token_to_id[token]
         return self.encoder.get(a__      ,       self.encoder[self.unk_token]       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         if index in self.id_to_lang_token:
            return self.id_to_lang_token[index]
         return self.decoder.get(a__      ,       self.unk_token       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[str]			      =	[]
         A_     :				List[str]			      =	""""""
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               out_string += self.sp_model.decode(a__       ) + token
               A_     :				int			      =	[]
            else:
               current_sub_tokens.append(a__       )
         out_string += self.sp_model.decode(a__       )
         return out_string.strip()
      def 		_lowerCamelCase      (		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__       )

         A_     :				List[Any]			      =	[1] * len(self.prefix_tokens       )
         A_     :				Dict			      =	[1] * len(self.suffix_tokens       )
         if token_ids_a is None:
            return prefix_ones + ([0] * len(a__       )) + suffix_ones
         return prefix_ones + ([0] * len(a__       )) + ([0] * len(a__       )) + suffix_ones
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	{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       ):
         A_     :				List[str]			      =	self.__dict__.copy()
         A_     :				str			      =	None
         return state
      def __setstate__(		self      ,       a__       ):
         A_     :				Any			      =	d

         # for backward compatibility
         if not hasattr(self      ,       """sp_model_kwargs"""       ):
            A_     :				Dict			      =	{}

         A_     :				int			      =	load_spm(self.spm_file      ,       self.sp_model_kwargs       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         A_     :				Optional[Any]			      =	Path(a__       )
         if not save_dir.is_dir():
            raise OSError(F"""{save_directory} should be a directory"""       )
         A_     :				List[Any]			      =	save_dir / (
             (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
         )
         A_     :				Tuple			      =	save_dir / (
             (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
         )

         save_json(self.encoder      ,       a__       )

         if os.path.abspath(self.spm_file       ) != os.path.abspath(a__       ) and os.path.isfile(self.spm_file       ):
            copyfile(self.spm_file      ,       a__       )
         elif not os.path.isfile(self.spm_file       ):
            with open(a__      ,       """wb"""       ) as fi:
               A_     :				Tuple			      =	self.sp_model.serialized_model_proto()
               fi.write(a__       )

         return (str(a__       ), str(a__       ))
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = "en"      ,       a__ = None      ,       a__ = "ro"      ,       **a__      ,       ):
         A_     :				Optional[int]			      =	src_lang
         A_     :				List[Any]			      =	tgt_lang
         self.set_src_lang_special_tokens(self.src_lang       )
         return super().prepare_seqaseq_batch(a__      ,       a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       **a__       ):
         if src_lang is None or tgt_lang is None:
            raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
         A_     :				List[str]			      =	src_lang
         A_     :				Union[str, Any]			      =	self(a__      ,       add_special_tokens=a__      ,       **a__       )
         A_     :				Any			      =	self.get_lang_id(a__       )
         A_     :				List[Any]			      =	tgt_lang_id
         return inputs
      def 		_lowerCamelCase      (		self       ):
         self.set_src_lang_special_tokens(self.src_lang       )
      def 		_lowerCamelCase      (		self       ):
         self.set_tgt_lang_special_tokens(self.tgt_lang       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Tuple			      =	self.get_lang_token(a__       )
         A_     :				Optional[Any]			      =	self.lang_token_to_id[lang_token]
         A_     :				Optional[Any]			      =	[self.cur_lang_id]
         A_     :				Union[str, Any]			      =	[self.eos_token_id]
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				str			      =	self.get_lang_token(a__       )
         A_     :				Optional[int]			      =	self.lang_token_to_id[lang_token]
         A_     :				str			      =	[self.cur_lang_id]
         A_     :				Optional[int]			      =	[self.eos_token_id]
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.lang_code_to_token[lang]






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Union[str, Any]			      =	self.get_lang_token(a__       )
         return self.lang_token_to_id[lang_token]






def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[str]			      =	sentencepiece.SentencePieceProcessor(**_lowerCAmelCase     )
   spm.Load(str(_lowerCAmelCase     )     )
   return spm



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   with open(_lowerCAmelCase  ,"""r"""     ) as f:
      return json.load(_lowerCAmelCase     )







def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   with open(_lowerCAmelCase  ,"""w"""     ) as f:
      json.dump(_lowerCAmelCase  ,_lowerCAmelCase  ,indent=2     )






