



'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

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

		return name
def    lowerCamelCase__ (		_A				,    _A       ):
		for key in orig_state_dict.copy().keys():
				a		:      str       =  orig_state_dict.pop(_A       )

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

		return orig_state_dict
def    lowerCamelCase__ (		):
		a		:      int       =  'http://images.cocodataset.org/val2017/000000039769.jpg'
		a		:      str       =  Image.open(requests.get(_A				,    stream=_A       ).raw       )
		return im
@torch.no_grad()
def    lowerCamelCase__ (		_A				,    _A				,    _A="groupvit-gcc-yfcc"				,    _A=False       ):
		a		:      List[Any]       =  GroupViTConfig()
		a		:      List[str]       =  GroupViTModel(_A       ).eval()

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

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

		with torch.no_grad():
				a		:      str       =  model(**_A       )

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

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

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


if __name__ == "__main__":
					lowerCAmelCase:      List[Any]		 =		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:      Optional[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 os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

lowerCAmelCase:      Dict		 =		{
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

lowerCAmelCase:      List[Any]		 =		{
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

lowerCAmelCase:      Optional[Any]		 =		{
    'vinai/phobert-base': 2_5_6,
    'vinai/phobert-large': 2_5_6,
}
def    lowerCamelCase__ (		_A       ):
  a		:      List[Any]       =  set()
  a		:      Union[str, Any]       =  word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)       )
    a		:      Tuple       =  char

  a		:      Any       =  set(_A       )
  return pairs

class 							a__(      lowerCamelCase__			):
    lowercase__				=					VOCAB_FILES_NAMES
    lowercase__				=					PRETRAINED_VOCAB_FILES_MAP
    lowercase__				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







    def __init__(   self				:						Optional[Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						int		,    __snake_case				:						Any="<s>"		,    __snake_case				:						List[Any]="</s>"		,    __snake_case				:						Union[str, Any]="</s>"		,    __snake_case				:						List[Any]="<s>"		,    __snake_case				:						Optional[Any]="<unk>"		,    __snake_case				:						Union[str, Any]="<pad>"		,    __snake_case				:						List[Any]="<mask>"		,    **__snake_case				:						int		,    ):
      super().__init__(
          bos_token=__snake_case		,    eos_token=__snake_case		,    unk_token=__snake_case		,    sep_token=__snake_case		,    cls_token=__snake_case		,    pad_token=__snake_case		,    mask_token=__snake_case		,    **__snake_case		,    )

      a		:      Optional[int]       =  vocab_file
      a		:      Optional[Any]       =  merges_file

      a		:      int       =  {}
      a		:      List[str]       =  0
      a		:      Union[str, Any]       =  1
      a		:      Optional[Any]       =  2
      a		:      List[Any]       =  3

      self.add_from_file(__snake_case			)

      a		:      Dict       =  {v: k for k, v in self.encoder.items()}

      with open(__snake_case		,    encoding='utf-8'			) as merges_handle:
        a		:      Optional[int]       =  merges_handle.read().split('\n'			)[:-1]
      a		:      Any       =  [tuple(merge.split()[:-1]			) for merge in merges]
      a		:      List[Any]       =  dict(zip(__snake_case		,    range(len(__snake_case			)			)			)			)
      a		:      List[str]       =  {}







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None			):

      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      a		:      Union[str, Any]       =  [self.cls_token_id]
      a		:      Any       =  [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None		,    __snake_case				:						bool = False			):

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

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







    def     lowercase_ (   self				:						int		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None			):
      a		:      Dict       =  [self.sep_token_id]
      a		:      Optional[int]       =  [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     lowercase_ (   self				:						Optional[int]			):
      return len(self.encoder			)







    def     lowercase_ (   self				:						Dict			):
      return dict(self.encoder		,    **self.added_tokens_encoder			)







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						Optional[Any]			):
      if token in self.cache:
        return self.cache[token]
      a		:      int       =  tuple(__snake_case			)
      a		:      int       =  tuple(list(word[:-1]			) + [word[-1] + '</w>']			)
      a		:      Dict       =  get_pairs(__snake_case			)

      if not pairs:
        return token

      while True:
        a		:      str       =  min(__snake_case		,    key=lambda __snake_case			: self.bpe_ranks.get(__snake_case		,    float('inf'			)			)			)
        if bigram not in self.bpe_ranks:
          break
        a     ,			a		:      Tuple       =  bigram
        a		:      int       =  []
        a		:      Optional[int]       =  0
        while i < len(__snake_case			):
          try:
            a		:      List[str]       =  word.index(__snake_case		,    __snake_case			)
          except ValueError:
            new_word.extend(word[i:]			)
            break
          else:
            new_word.extend(word[i:j]			)
            a		:      Dict       =  j

          if word[i] == first and i < len(__snake_case			) - 1 and word[i + 1] == second:
            new_word.append(first + second			)
            i += 2
          else:
            new_word.append(word[i]			)
            i += 1
        a		:      Optional[Any]       =  tuple(__snake_case			)
        a		:      Tuple       =  new_word
        if len(__snake_case			) == 1:
          break
        else:
          a		:      Union[str, Any]       =  get_pairs(__snake_case			)
      a		:      List[Any]       =  '@@ '.join(__snake_case			)
      a		:      int       =  word[:-4]
      a		:      Optional[int]       =  word
      return word







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						List[str]			):
      a		:      Optional[int]       =  []

      a		:      Optional[Any]       =  re.findall(r'\S+\n?'		,    __snake_case			)

      for token in words:
        split_tokens.extend(list(self.bpe(__snake_case			).split(' '			)			)			)
      return split_tokens







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Dict			):
      return self.encoder.get(__snake_case		,    self.encoder.get(self.unk_token			)			)







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Dict			):
      return self.decoder.get(__snake_case		,    self.unk_token			)







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Any			):
      a		:      Union[str, Any]       =  ' '.join(__snake_case			).replace('@@ '		,    ''			).strip()
      return out_string







    def     lowercase_ (   self				:						str		,    __snake_case				:						str		,    __snake_case				:						Optional[str] = None			):
      if not os.path.isdir(__snake_case			):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
        return
      a		:      Dict       =  os.path.join(
          __snake_case		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
      a		:      int       =  os.path.join(
          __snake_case		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(__snake_case			):
        copyfile(self.vocab_file		,    __snake_case			)

      if os.path.abspath(self.merges_file			) != os.path.abspath(__snake_case			):
        copyfile(self.merges_file		,    __snake_case			)

      return out_vocab_file, out_merge_file







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						List[str]			):
      if isinstance(__snake_case		,    __snake_case			):
        try:
          with open(__snake_case		,    'r'		,    encoding='utf-8'			) as fd:
            self.add_from_file(__snake_case			)
        except FileNotFoundError as fnfe:
          raise fnfe
        except UnicodeError:
          raise Exception(F"""Incorrect encoding detected in {f}, please rebuild the dataset"""			)
        return

      a		:      List[str]       =  f.readlines()
      for lineTmp in lines:
        a		:      Any       =  lineTmp.strip()
        a		:      Dict       =  line.rfind(' '			)
        if idx == -1:
          raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\''			)
        a		:      str       =  line[:idx]
        a		:      Optional[int]       =  len(self.encoder			)