
'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 		A_       (	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




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

      return name







def 		A_       (	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            _lowerCamelCase				:   Optional[int]								=   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
                  _lowerCamelCase				:   Any								=   key.split("."							)
                  _lowerCamelCase				:   List[str]								=   int(key_split[2]							), int(key_split[4]							)
                  _lowerCamelCase				:   List[Any]								=   config.vision_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   int								=   val[dim : dim * 2, :]
                        _lowerCamelCase				:   str								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   Optional[int]								=   val[:dim]
                        _lowerCamelCase				:   str								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Tuple								=   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[Any]								=   key.split("."							)
                  _lowerCamelCase				:   int								=   int(key_split[3]							)
                  _lowerCamelCase				:   Tuple								=   config.text_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   str								=   val[
                            dim : dim * 2, :
                        ]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   str								=   val[:dim]
                        _lowerCamelCase				:   Optional[Any]								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:]
            else:
                  _lowerCamelCase				:   int								=   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
                  ):
                        _lowerCamelCase				:   Any								=   val.squeeze_()
                  else:
                        _lowerCamelCase				:   Union[str, Any]								=   val

      return orig_state_dict







def 		A_       (	):



      """simple docstring"""




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







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    str="groupvit-gcc-yfcc"				,	_lowerCAmelCase   :    List[str]=False							):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   GroupViTConfig()
      _lowerCamelCase				:   Union[str, Any]								=   GroupViTModel(_lowerCAmelCase							).eval()

      _lowerCamelCase				:   Any								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]
      _lowerCamelCase				:   Union[str, Any]								=   convert_state_dict(_lowerCAmelCase				,	_lowerCAmelCase							)
      _lowerCamelCase				:   Tuple								=   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
      _lowerCamelCase				:   List[Any]								=   CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"							)
      _lowerCamelCase				:   List[str]								=   prepare_img()
      _lowerCamelCase				:   Any								=   processor(text=["a photo of a cat", "a photo of a dog"]				,	images=_lowerCAmelCase				,	padding=_lowerCAmelCase				,	return_tensors="pt"							)

      with torch.no_grad():
            _lowerCamelCase				:   List[str]								=   model(**_lowerCAmelCase							)

      if model_name == "groupvit-gcc-yfcc":
            _lowerCamelCase				:   List[Any]								=   torch.tensor([[13.3523, 6.3_6_2_9]]							)
      elif model_name == "groupvit-gcc-redcaps":
            _lowerCamelCase				:   Optional[Any]								=   torch.tensor([[16.1873, 8.6_2_3_0]]							)
      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__":
 UpperCAmelCase_       :   Optional[int]   =       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`.',
 )
 UpperCAmelCase_       :   Union[str, Any]   =       parser.parse_args()

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

'''simple docstring'''






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)


UpperCAmelCase_       :   Any   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_       :   List[str]   =       {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

UpperCAmelCase_       :   List[Any]   =       {'facebook/blenderbot-3B': 128}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		['input_ids', 'attention_mask']
    lowerCAmelCase_				    =		BlenderbotTokenizer



    def __init__(	self      :       Dict,__A      :       Optional[Any]=None,__A      :       List[str]=None,__A      :       Optional[Any]=None,__A      :       List[Any]="replace",__A      :       List[Any]="<s>",__A      :       str="</s>",__A      :       List[str]="</s>",__A      :       List[Any]="<s>",__A      :       Union[str, Any]="<unk>",__A      :       Optional[Any]="<pad>",__A      :       Dict="<mask>",__A      :       Any=False,__A      :       Tuple=True,**__A      :       Dict,):
          super().__init__(
              __A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)

          _lowerCamelCase				:   Tuple								=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
          if pre_tok_state.get("add_prefix_space",__A     ) != add_prefix_space:
                _lowerCamelCase				:   Optional[Any]								=   getattr(__A,pre_tok_state.pop("type"     )     )
                _lowerCamelCase				:   List[Any]								=   add_prefix_space
                _lowerCamelCase				:   Optional[Any]								=   pre_tok_class(**__A     )

          _lowerCamelCase				:   Any								=   add_prefix_space

          _lowerCamelCase				:   Any								=   "post_processor"
          _lowerCamelCase				:   Optional[Any]								=   getattr(self.backend_tokenizer,__A,__A     )
          if tokenizer_component_instance:
                _lowerCamelCase				:   Tuple								=   json.loads(tokenizer_component_instance.__getstate__()     )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                      _lowerCamelCase				:   List[Any]								=   tuple(state["sep"]     )
                if "cls" in state:
                      _lowerCamelCase				:   Optional[int]								=   tuple(state["cls"]     )

                _lowerCamelCase				:   List[str]								=   False

                if state.get("add_prefix_space",__A     ) != add_prefix_space:
                      _lowerCamelCase				:   List[str]								=   add_prefix_space
                      _lowerCamelCase				:   int								=   True

                if state.get("trim_offsets",__A     ) != trim_offsets:
                      _lowerCamelCase				:   List[str]								=   trim_offsets
                      _lowerCamelCase				:   Dict								=   True

                if changes_to_apply:
                      _lowerCamelCase				:   Tuple								=   getattr(__A,state.pop("type"     )     )
                      _lowerCamelCase				:   Union[str, Any]								=   component_class(**__A     )
                      setattr(self.backend_tokenizer,__A,__A     )



    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          if self._mask_token is None:
                if self.verbose:
                      logger.error("Using mask_token, but it is not set yet."     )
                return None
          return str(self._mask_token     )



    @mask_token.setter
    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       List[Any]     ):
          _lowerCamelCase				:   Any								=   AddedToken(__A,lstrip=__A,rstrip=__A     ) if isinstance(__A,__A     ) else value
          _lowerCamelCase				:   List[str]								=   value



    def   lowerCamelCase_  (	self      :       Optional[int],*__A      :       Optional[Any],**__A      :       List[str]     ):
          _lowerCamelCase				:   int								=   kwargs.get("is_split_into_words",__A     )
          assert self.add_prefix_space or not is_split_into_words, (
              f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       str,*__A      :       Union[str, Any],**__A      :       List[Any]     ):
          _lowerCamelCase				:   Optional[int]								=   kwargs.get("is_split_into_words",__A     )

          assert self.add_prefix_space or not is_split_into_words, (
              f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       str,__A      :       str,__A      :       Optional[str] = None     ):
          _lowerCamelCase				:   Optional[int]								=   self._tokenizer.model.save(__A,name=__A     )
          return tuple(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   int								=   [self.sep_token_id]
          _lowerCamelCase				:   str								=   [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]



    def   lowerCamelCase_  (	self      :       Dict,__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          return token_ids_a + [self.eos_token_id]



    def   lowerCamelCase_  (	self      :       List[str],__A      :       "Conversation"     ):
          _lowerCamelCase				:   Dict								=   []
          for is_user, text in conversation.iter_texts():
                if is_user:
                      # We need to space prefix as it's being done within blenderbot
                      inputs.append(" " + text     )
                else:
                      # Generated responses should contain them already.
                      inputs.append(__A     )

          _lowerCamelCase				:   List[Any]								=   "  ".join(__A     )
          _lowerCamelCase				:   List[str]								=   self.encode(__A     )
          if len(__A     ) > self.model_max_length:
                _lowerCamelCase				:   Tuple								=   input_ids[-self.model_max_length :]
                logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'     )
          return input_ids