






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





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

							return name





def 		__UpperCAmelCase     (	a_				,	a_):
							for key in orig_state_dict.copy().keys():
														snake_case_           =				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
																					snake_case_           =				key.split('.')
																					snake_case_     ,						snake_case_           =				int(key_split[2]), int(key_split[4])
																					snake_case_           =				config.vision_config.hidden_size
																					if "weight" in key:
																												snake_case_           =				val[:dim, :]
																												snake_case_           =				val[dim : dim * 2, :]
																												snake_case_           =				val[-dim:, :]
																					else:
																												snake_case_           =				val[:dim]
																												snake_case_           =				val[dim : dim * 2]
																												snake_case_           =				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
																					snake_case_           =				key.split('.')
																					snake_case_           =				int(key_split[3])
																					snake_case_           =				config.text_config.hidden_size
																					if "weight" in key:
																												snake_case_           =				val[:dim, :]
																												snake_case_           =				val[
																												    dim : dim * 2, :
																												]
																												snake_case_           =				val[-dim:, :]
																					else:
																												snake_case_           =				val[:dim]
																												snake_case_           =				val[dim : dim * 2]
																												snake_case_           =				val[-dim:]
														else:
																					snake_case_           =				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
																					):
																												snake_case_           =				val.squeeze_()
																					else:
																												snake_case_           =				val

							return orig_state_dict





def 		__UpperCAmelCase     (	):
							snake_case_           =				'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_           =				Image.open(requests.get(a_				,	stream=a_).raw)
							return im





@torch.no_grad()
def 		__UpperCAmelCase     (	a_				,	a_				,	a_="groupvit-gcc-yfcc"				,	a_=False):
							snake_case_           =				GroupViTConfig()
							snake_case_           =				GroupViTModel(a_).eval()

							snake_case_           =				torch.load(a_				,	map_location='cpu')['model']
							snake_case_           =				convert_state_dict(a_				,	a_)
							snake_case_     ,						snake_case_           =				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
							snake_case_           =				CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')
							snake_case_           =				prepare_img()
							snake_case_           =				processor(text=['a photo of a cat', 'a photo of a dog']				,	images=a_				,	padding=a_				,	return_tensors='pt')

							with torch.no_grad():
														snake_case_           =				model(**a_)

							if model_name == "groupvit-gcc-yfcc":
														snake_case_           =				torch.tensor([[13.35_23, 6.36_29]])
							elif model_name == "groupvit-gcc-redcaps":
														snake_case_           =				torch.tensor([[16.18_73, 8.62_30]])
							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__":
	lowercase    				=   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`.",
	)
	lowercase    				=   parser.parse_args()

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













import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_rembert import RemBertTokenizer
else:
	lowercase    				=   None

lowercase    				=   logging.get_logger(__name__)
lowercase    				=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

lowercase    				=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

lowercase    				=   {
    "google/rembert": 256,
}

lowercase    				=   "▁"




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					VOCAB_FILES_NAMES
	lowerCAmelCase						=					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase						=					RemBertTokenizer




	def __init__(			self			,     a=None			,     a=None			,     a=True			,     a=True			,     a=False			,     a="[CLS]"			,     a="[SEP]"			,     a="<unk>"			,     a="[SEP]"			,     a="<pad>"			,     a="[CLS]"			,     a="[MASK]"			,     **a			,     )			->       Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								snake_case_           =				AddedToken(a			,     lstrip=a			,     rstrip=a   ) if isinstance(a			,     a   ) else mask_token

								super().__init__(
								    a			,     tokenizer_file=a			,     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			,     **a			,     )

								snake_case_           =				do_lower_case
								snake_case_           =				remove_space
								snake_case_           =				keep_accents
								snake_case_           =				vocab_file
								snake_case_           =				False if not self.vocab_file else True




	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       List[int]:
								snake_case_           =				[self.sep_token_id]
								snake_case_           =				[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 				_UpperCamelCase     (			self			,     a			,     a = None			,     a = False   )			->       List[int]:

								if already_has_special_tokens:
															if token_ids_a is not None:
																						raise ValueError(
																						    'You should not supply a second sequence if the provided sequence of '
																						    'ids is already formatted with special tokens for the model.'   )
															return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_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 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       List[int]:
								snake_case_           =				[self.sep_token_id]
								snake_case_           =				[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 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       Tuple[str]:
								if not os.path.isdir(a   ):
															logger.error('Vocabulary path ({}) should be a directory'.format(a   )   )
															return
								snake_case_           =				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   ):
															copyfile(self.vocab_file			,     a   )

								return (out_vocab_file,)





