

import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel





def  _snake_case		(snake_case_					:     Any    ,       snake_case_					:     Dict    ,       snake_case_					:     str  )							->		Union[str, Any]:
  __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Tuple		=     pt_weights[F"{prefix}.in_proj_weight"].chunk(3    ,       dim=0  )
  __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     pt_weights[F"{prefix}.in_proj_bias"].chunk(3    ,       dim=0  )

  __UpperCAmelCase	:  Any		=     pt_weights[F"{prefix}.out_proj.weight"]
  __UpperCAmelCase	:  int		=     pt_weights[F"{prefix}.out_proj.bias"]

  __UpperCAmelCase	:  Dict		=     q_proj
  __UpperCAmelCase	:  Optional[Any]		=     q_proj_bias

  __UpperCAmelCase	:  List[Any]		=     k_proj
  __UpperCAmelCase	:  str		=     k_proj_bias

  __UpperCAmelCase	:  Dict		=     v_proj
  __UpperCAmelCase	:  Any		=     v_proj_bias

  __UpperCAmelCase	:  Tuple		=     out_proj_weights
  __UpperCAmelCase	:  Optional[int]		=     out_proj_bias





def  _snake_case		(snake_case_					:     Tuple    ,       snake_case_					:     List[str]    ,       snake_case_					:     Any  )							->		str:
  copy_linear(hf_mlp.fca    ,       snake_case_    ,       F"{prefix}.c_fc"  )
  copy_linear(hf_mlp.fca    ,       snake_case_    ,       F"{prefix}.c_proj"  )





def  _snake_case		(snake_case_					:     Any    ,       snake_case_					:     Union[str, Any]    ,       snake_case_					:     int  )							->		Tuple:
  __UpperCAmelCase	:  Optional[Any]		=     pt_weights[F"{prefix}.weight"].data
  __UpperCAmelCase	:  Tuple		=     pt_weights[F"{prefix}.bias"].data





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     Dict    ,       snake_case_					:     List[str]  )							->		Any:
  # copy layer norms
  copy_linear(hf_layer.layer_norma    ,       snake_case_    ,       F"{prefix}.ln_1"  )
  copy_linear(hf_layer.layer_norma    ,       snake_case_    ,       F"{prefix}.ln_2"  )

  # copy MLP
  copy_mlp(hf_layer.mlp    ,       snake_case_    ,       F"{prefix}.mlp"  )

  # copy attn
  copy_attn_layer(hf_layer.self_attn    ,       snake_case_    ,       F"{prefix}.attn"  )





def  _snake_case		(snake_case_					:     Tuple    ,       snake_case_					:     Dict    ,       snake_case_					:     Tuple  )							->		Optional[Any]:
  for layer_id, hf_layer in enumerate(snake_case_  ):
    copy_layer(snake_case_    ,       snake_case_    ,       F"{prefix}.{layer_id}"  )





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     Dict  )							->		str:
  # copy projection
  __UpperCAmelCase	:  str		=     pt_weights['text_projection'].data.T

  # copy text encoder
  for name, param in hf_model.text_model.named_parameters():
    __UpperCAmelCase	:  Dict		=     pt_weights[F"bert.{name}"].data





def  _snake_case		(snake_case_					:     Optional[Any]    ,       snake_case_					:     int  )							->		int:
  # copy projection
  __UpperCAmelCase	:  Union[str, Any]		=     pt_weights['visual.proj'].data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm    ,       snake_case_    ,       'visual.ln_pre'  )
  copy_linear(hf_model.vision_model.post_layernorm    ,       snake_case_    ,       'visual.ln_post'  )

  # copy embeddings
  __UpperCAmelCase	:  Dict		=     pt_weights['visual.conv1.weight'].data
  __UpperCAmelCase	:  int		=     pt_weights['visual.class_embedding'].data
  __UpperCAmelCase	:  Optional[int]		=     pt_weights['visual.positional_embedding'].data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers    ,       snake_case_    ,       'visual.transformer.resblocks'  )





@torch.no_grad()
def  _snake_case		(snake_case_					:     Dict    ,       snake_case_					:     Union[str, Any]    ,       snake_case_					:     List[str]=None  )							->		Optional[int]:
  assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
  __UpperCAmelCase	:  str		=     ChineseCLIPConfig.from_pretrained(snake_case_  )

  __UpperCAmelCase	:  List[str]		=     ChineseCLIPModel(snake_case_  ).eval()

  __UpperCAmelCase	:  Optional[int]		=     torch.load(snake_case_    ,       map_location='cpu'  )['state_dict']
  __UpperCAmelCase	:  str		=     {(name[7:] if name.startswith('module.'  ) else name): value for name, value in pt_weights.items()}

  copy_text_model_and_projection(snake_case_    ,       snake_case_  )
  copy_vision_model_and_projection(snake_case_    ,       snake_case_  )
  __UpperCAmelCase	:  int		=     pt_weights['logit_scale'].data

  hf_model.save_pretrained(snake_case_  )


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE							 =			argparse.ArgumentParser()
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default=None,
          type=str,
          help='''Path to the output folder storing converted hf PyTorch model.''',
      )
      parser.add_argument(
          '''--checkpoint_path''', default=None, type=str, help='''Path to original github format ChineseCLIP checkpoint.'''
      )
      parser.add_argument(
          '''--config_path''', default=None, required=True, type=str, help='''Path to hf config.json of model to convert.'''
      )
      _SCREAMING_SNAKE_CASE							 =			parser.parse_args()

      convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
      print('''The conversion is finished!''')







import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
__snake_case					:							Optional[int]           =							False
__snake_case					:							Union[str, Any]           =							Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__snake_case					:							Optional[int]           =							datasets.Features(
    OrderedDict(
        {
            'attr_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'attr_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'boxes': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='float32'),
            'img_id': datasets.Value('int32'),
            'obj_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'obj_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'roi_features': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)


class 	UpperCamelCase__       :

						'''simple docstring'''



						def __init__(  self  , A=sys.argv[1:]	)  ->Optional[Any]:
									UpperCAmelCase__		:Optional[Any] 		=    None
									UpperCAmelCase__		:str 		=    None
									UpperCAmelCase__		:Optional[Any] 		=    None
									UpperCAmelCase__		:List[Any] 		=    1
									UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    getopt.getopt(A  , 'i:o:b:s'  , ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']	)
									for opt, arg in opts:
												if opt in ("-i", "--inputdir"):
															UpperCAmelCase__		:Optional[int] 		=    arg
												elif opt in ("-o", "--outfile"):
															UpperCAmelCase__		:str 		=    arg
												elif opt in ("-b", "--batch_size"):
															UpperCAmelCase__		:List[Any] 		=    int(A	)
												elif opt in ("-s", "--subset_list"):
															UpperCAmelCase__		:str 		=    arg

									assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
									assert outputfile is not None and not os.path.isfile(A	), F"""{outputfile}"""
									if subset_list is not None:
												with open(os.path.realpath(A	)	) as f:
															UpperCAmelCase__		:Any 		=    {self._vqa_file_split()[0] for x in tryload(A	)}
									else:
												UpperCAmelCase__		:Any 		=    None

									UpperCAmelCase__		:List[str] 		=    CONFIG
									if torch.cuda.is_available():
												UpperCAmelCase__		:Optional[Any] 		=    'cuda'
									UpperCAmelCase__		:Dict 		=    os.path.realpath(A	)
									UpperCAmelCase__		:List[Any] 		=    os.path.realpath(A	)
									UpperCAmelCase__		:List[Any] 		=    Preprocess(self.config	)
									UpperCAmelCase__		:Union[str, Any] 		=    GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'  , config=self.config	)
									UpperCAmelCase__		:Tuple 		=    batch_size if batch_size != 0 else 1
									UpperCAmelCase__		:List[str] 		=    DEFAULT_SCHEMA


						def    A__       (  self  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:List[str] 		=    int(file.split('.'	)[0].split('_'	)[-1]	)
									UpperCAmelCase__		:Any 		=    os.path.join(self.inputdir  , A	)
									return (img_id, filepath)


						@property
						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:str 		=    []
									for i, file in enumerate(os.listdir(self.inputdir	)	):
												if self.subset_list is not None and i not in self.subset_list:
															continue
												batch.append(self._vqa_file_split(A	)	)
												if len(A	) == self.batch:
															UpperCAmelCase__		:Optional[Any] 		=    batch
															UpperCAmelCase__		:Tuple 		=    []
															yield list(map(A  , zip(*A	)	)	)

									for i in range(1	):
												yield list(map(A  , zip(*A	)	)	)


						def __call__(  self	)  ->List[Any]:
									# make writer
									if not TEST:
												UpperCAmelCase__		:List[Any] 		=    datasets.ArrowWriter(features=self.schema  , path=self.outputfile	)
									# do file generator
									for i, (img_ids, filepaths) in enumerate(self.file_generator	):
												UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__		:Any 		=    self.preprocess(A	)
												UpperCAmelCase__		:Tuple 		=    self.model(
												    A  , A  , scales_yx=A  , padding='max_detections'  , max_detections=self.config.MAX_DETECTIONS  , pad_value=0  , return_tensors='np'  , location='cpu'  , )
												UpperCAmelCase__		:Dict 		=    output_dict.pop('normalized_boxes'	)
												if not TEST:
															UpperCAmelCase__		:Union[str, Any] 		=    np.array(A	)
															UpperCAmelCase__		:Any 		=    self.schema.encode_batch(A	)
															writer.write_batch(A	)
												if TEST:
															break
												# finalizer the writer
									if not TEST:
												UpperCAmelCase__		,       UpperCAmelCase__		:Optional[Any] 		=    writer.finalize()
												print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""	)




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			try:
						UpperCAmelCase__		:List[Any] 		=    json.load(SCREAMING_SNAKE_CASE							)
						try:
									UpperCAmelCase__		:Optional[Any] 		=    list(data.keys()							)
						except Exception:
									UpperCAmelCase__		:List[str] 		=    [d['img_id'] for d in data]
			except Exception:
						try:
									UpperCAmelCase__		:str 		=    eval(stream.read()							)
						except Exception:
									UpperCAmelCase__		:Optional[Any] 		=    stream.read().split('\n'							)
			return data


if __name__ == "__main__":
						__snake_case					:							Union[str, Any]           =							Extract(sys.argv[1:])
						extract()
						if not TEST:
												__snake_case					:							Optional[Any]           =							datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)





