'''simple docstring'''

import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__  :						int					=      logging.get_logger(__name__)

UpperCamelCase__  :						int					=      {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Any   				= '''owlvit_text_model'''




      def __init__(					self       ,lowerCamelCase_=49408       ,lowerCamelCase_=512       ,lowerCamelCase_=2048       ,lowerCamelCase_=12       ,lowerCamelCase_=8       ,lowerCamelCase_=16       ,lowerCamelCase_="quick_gelu"       ,lowerCamelCase_=1e-5       ,lowerCamelCase_=0.0       ,lowerCamelCase_=0.02       ,lowerCamelCase_=1.0       ,lowerCamelCase_=0       ,lowerCamelCase_=49406       ,lowerCamelCase_=49407       ,**lowerCamelCase_       ,)      ->      str:



         '''simple docstring'''



         super().__init__(pad_token_id=lowerCamelCase_       ,bos_token_id=lowerCamelCase_       ,eos_token_id=lowerCamelCase_       ,**lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[int] 					=	vocab_size
         UpperCAmelCase__				:					Dict 					=	hidden_size
         UpperCAmelCase__				:					Optional[Any] 					=	intermediate_size
         UpperCAmelCase__				:					Union[str, Any] 					=	num_hidden_layers
         UpperCAmelCase__				:					int 					=	num_attention_heads
         UpperCAmelCase__				:					List[str] 					=	max_position_embeddings
         UpperCAmelCase__				:					Any 					=	hidden_act
         UpperCAmelCase__				:					int 					=	layer_norm_eps
         UpperCAmelCase__				:					List[Any] 					=	attention_dropout
         UpperCAmelCase__				:					List[str] 					=	initializer_range
         UpperCAmelCase__				:					Tuple 					=	initializer_factor




      @classmethod
      def 		lowerCAmelCase__       (					cls       ,lowerCamelCase_       ,**lowerCamelCase_				)      ->      "PretrainedConfig":



         '''simple docstring'''



         cls._set_token_in_kwargs(lowerCamelCase_				)

         UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	cls.get_config_dict(lowerCamelCase_       ,**lowerCamelCase_				)

         # get the text config dict if we are loading from OwlViTConfig
         if config_dict.get('''model_type'''				) == "owlvit":
            UpperCAmelCase__				:					Optional[Any] 					=	config_dict['''text_config''']

         if "model_type" in config_dict and hasattr(cls       ,'''model_type'''				) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''				)

         return cls.from_dict(lowerCamelCase_       ,**lowerCamelCase_				)




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Union[str, Any]   				= '''owlvit_vision_model'''




      def __init__(					self       ,lowerCamelCase_=768       ,lowerCamelCase_=3072       ,lowerCamelCase_=12       ,lowerCamelCase_=12       ,lowerCamelCase_=3       ,lowerCamelCase_=768       ,lowerCamelCase_=32       ,lowerCamelCase_="quick_gelu"       ,lowerCamelCase_=1e-5       ,lowerCamelCase_=0.0       ,lowerCamelCase_=0.02       ,lowerCamelCase_=1.0       ,**lowerCamelCase_       ,)      ->      List[str]:



         '''simple docstring'''



         super().__init__(**lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[int] 					=	hidden_size
         UpperCAmelCase__				:					Union[str, Any] 					=	intermediate_size
         UpperCAmelCase__				:					Dict 					=	num_hidden_layers
         UpperCAmelCase__				:					Tuple 					=	num_attention_heads
         UpperCAmelCase__				:					str 					=	num_channels
         UpperCAmelCase__				:					Optional[int] 					=	image_size
         UpperCAmelCase__				:					Dict 					=	patch_size
         UpperCAmelCase__				:					str 					=	hidden_act
         UpperCAmelCase__				:					int 					=	layer_norm_eps
         UpperCAmelCase__				:					Optional[Any] 					=	attention_dropout
         UpperCAmelCase__				:					List[Any] 					=	initializer_range
         UpperCAmelCase__				:					Optional[int] 					=	initializer_factor




      @classmethod
      def 		lowerCAmelCase__       (					cls       ,lowerCamelCase_       ,**lowerCamelCase_				)      ->      "PretrainedConfig":



         '''simple docstring'''



         cls._set_token_in_kwargs(lowerCamelCase_				)

         UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	cls.get_config_dict(lowerCamelCase_       ,**lowerCamelCase_				)

         # get the vision config dict if we are loading from OwlViTConfig
         if config_dict.get('''model_type'''				) == "owlvit":
            UpperCAmelCase__				:					List[str] 					=	config_dict['''vision_config''']

         if "model_type" in config_dict and hasattr(cls       ,'''model_type'''				) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''				)

         return cls.from_dict(lowerCamelCase_       ,**lowerCamelCase_				)




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= '''owlvit'''
      UpperCAmelCase_   :   List[str]   				= True




      def __init__(					self       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=512       ,lowerCamelCase_=2.6592       ,lowerCamelCase_=True       ,**lowerCamelCase_       ,)      ->      Union[str, Any]:



         '''simple docstring'''



         super().__init__(**lowerCamelCase_				)

         if text_config is None:
            UpperCAmelCase__				:					Any 					=	{}
            logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.'''				)

         if vision_config is None:
            UpperCAmelCase__				:					List[Any] 					=	{}
            logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.'''				)

         UpperCAmelCase__				:					List[Any] 					=	OwlViTTextConfig(**lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	OwlViTVisionConfig(**lowerCamelCase_				)

         UpperCAmelCase__				:					Dict 					=	projection_dim
         UpperCAmelCase__				:					Dict 					=	logit_scale_init_value
         UpperCAmelCase__				:					str 					=	return_dict
         UpperCAmelCase__				:					List[Any] 					=	1.0




      @classmethod
      def 		lowerCAmelCase__       (					cls       ,lowerCamelCase_       ,**lowerCamelCase_				)      ->      "PretrainedConfig":



         '''simple docstring'''



         cls._set_token_in_kwargs(lowerCamelCase_				)

         UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	cls.get_config_dict(lowerCamelCase_       ,**lowerCamelCase_				)

         if "model_type" in config_dict and hasattr(cls       ,'''model_type'''				) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''				)

         return cls.from_dict(lowerCamelCase_       ,**lowerCamelCase_				)




      @classmethod
      def 		lowerCAmelCase__       (					cls       ,lowerCamelCase_       ,lowerCamelCase_       ,**lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	{}
         UpperCAmelCase__				:					Any 					=	text_config
         UpperCAmelCase__				:					str 					=	vision_config

         return cls.from_dict(lowerCamelCase_       ,**lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	copy.deepcopy(self.__dict__				)
         UpperCAmelCase__				:					Optional[Any] 					=	self.text_config.to_dict()
         UpperCAmelCase__				:					Any 					=	self.vision_config.to_dict()
         UpperCAmelCase__				:					int 					=	self.__class__.model_type
         return output




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      @property
      def 		lowerCAmelCase__       (					self				)      ->      Mapping[str, Mapping[int, str]]:



         '''simple docstring'''



         return OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
             ]				)




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Mapping[str, Mapping[int, str]]:



         '''simple docstring'''



         return OrderedDict(
             [
                 ('''logits_per_image''', {0: '''batch'''}),
                 ('''logits_per_text''', {0: '''batch'''}),
                 ('''text_embeds''', {0: '''batch'''}),
                 ('''image_embeds''', {0: '''batch'''}),
             ]				)




      @property
      def 		lowerCAmelCase__       (					self				)      ->      float:



         '''simple docstring'''



         return 1e-4




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = -1       ,lowerCamelCase_ = -1       ,lowerCamelCase_ = None       ,)      ->      Mapping[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	super().generate_dummy_inputs(
             processor.tokenizer       ,batch_size=lowerCamelCase_       ,seq_length=lowerCamelCase_       ,framework=lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	super().generate_dummy_inputs(
             processor.image_processor       ,batch_size=lowerCamelCase_       ,framework=lowerCamelCase_				)
         return {**text_input_dict, **image_input_dict}




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         return 14





'''simple docstring'''

# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCamelCase__  :						Optional[Any]					=      2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
UpperCamelCase__  :						str					=      {
    # fairseq:
    'wmt19-ru-en': {'length_penalty': 1.1},
    'wmt19-en-ru': {'length_penalty': 1.15},
    'wmt19-en-de': {'length_penalty': 1.0},
    'wmt19-de-en': {'length_penalty': 1.1},
    # allenai:
    'wmt16-en-de-dist-12-1': {'length_penalty': 0.6},
    'wmt16-en-de-dist-6-1': {'length_penalty': 0.6},
    'wmt16-en-de-12-1': {'length_penalty': 0.8},
    'wmt19-de-en-6-6-base': {'length_penalty': 0.6},
    'wmt19-de-en-6-6-big': {'length_penalty': 0.6},
}

# this remaps the different models to their organization names
UpperCamelCase__  :						Tuple					=      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  UpperCamelCase__  :						Tuple					=      'facebook'
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
  UpperCamelCase__  :						Tuple					=      'allenai'







def __UpperCamelCase( _A       :					int     ):
   '''simple docstring'''
   # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
   # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
   UpperCAmelCase__				:					int 					=	dict((re.sub(R'''@@$'''	,      ''''''	,      _A     ), v) if k.endswith('''@@'''     ) else (re.sub(R'''$'''	,      '''</w>'''	,      _A     ), v) for k, v in d.items()     )
   UpperCAmelCase__				:					List[str] 					=	'''<s> <pad> </s> <unk>'''.split()
   # restore the special tokens
   for k in keep_keys:
      del da[F'''{k}</w>''']
      UpperCAmelCase__				:					Optional[Any] 					=	d[k]  # restore
   return da







def __UpperCamelCase( _A       :					Tuple	,      _A       :					List[Any]     ):
   '''simple docstring'''
   # prep
   assert os.path.exists(_A     )
   os.makedirs(_A	,      exist_ok=_A     )
   print(F'''Writing results to {pytorch_dump_folder_path}'''     )

   # handle various types of models

   UpperCAmelCase__				:					Dict 					=	basename(_A     )
   UpperCAmelCase__				:					List[str] 					=	dirname(_A     )

   UpperCAmelCase__				:					Union[str, Any] 					=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
   UpperCAmelCase__				:					Dict 					=	cls.hub_models()
   UpperCAmelCase__				:					Dict 					=	{'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
   UpperCAmelCase__				:					int 					=	'''.'''
   # note: since the model dump is old, fairseq has upgraded its model some
   # time later, and it does a whole lot of rewrites and splits on the saved
   # weights, therefore we can't use torch.load() directly on the model file.
   # see: upgrade_state_dict(state_dict) in fairseq_model.py
   print(F'''using checkpoint {checkpoint_file}'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	hub_utils.from_pretrained(
       _A	,      _A	,      _A	,      archive_map=_A	,      **_A     )

   UpperCAmelCase__				:					List[Any] 					=	vars(chkpt['''args''']['''model''']     )

   UpperCAmelCase__				:					str 					=	args['''source_lang''']
   UpperCAmelCase__				:					Tuple 					=	args['''target_lang''']

   UpperCAmelCase__				:					Optional[int] 					=	dirname(_A     )
   UpperCAmelCase__				:					int 					=	basename(_A     )

   # dicts
   UpperCAmelCase__				:					List[Any] 					=	os.path.join(_A	,      F'''dict.{src_lang}.txt'''     )
   UpperCAmelCase__				:					Tuple 					=	os.path.join(_A	,      F'''dict.{tgt_lang}.txt'''     )

   UpperCAmelCase__				:					int 					=	Dictionary.load(_A     )
   UpperCAmelCase__				:					Optional[int] 					=	rewrite_dict_keys(src_dict.indices     )
   UpperCAmelCase__				:					List[Any] 					=	len(_A     )
   UpperCAmelCase__				:					str 					=	os.path.join(_A	,      '''vocab-src.json'''     )
   print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''     )
   with open(_A	,      '''w'''	,      encoding='''utf-8'''     ) as f:
      f.write(json.dumps(_A	,      ensure_ascii=_A	,      indent=_A     )     )

   # detect whether this is a do_lower_case situation, which can be derived by checking whether we
   # have at least one uppercase letter in the source vocab
   UpperCAmelCase__				:					Tuple 					=	True
   for k in src_vocab.keys():
      if not k.islower():
         UpperCAmelCase__				:					int 					=	False
         break

   UpperCAmelCase__				:					str 					=	Dictionary.load(_A     )
   UpperCAmelCase__				:					Tuple 					=	rewrite_dict_keys(tgt_dict.indices     )
   UpperCAmelCase__				:					List[str] 					=	len(_A     )
   UpperCAmelCase__				:					Dict 					=	os.path.join(_A	,      '''vocab-tgt.json'''     )
   print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''     )
   with open(_A	,      '''w'''	,      encoding='''utf-8'''     ) as f:
      f.write(json.dumps(_A	,      ensure_ascii=_A	,      indent=_A     )     )

   # merges_file (bpecodes)
   UpperCAmelCase__				:					Tuple 					=	os.path.join(_A	,      VOCAB_FILES_NAMES['''merges_file''']     )
   for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
      UpperCAmelCase__				:					Dict 					=	os.path.join(_A	,      _A     )
      if os.path.exists(_A     ):
         break
   with open(_A	,      encoding='''utf-8'''     ) as fin:
      UpperCAmelCase__				:					Optional[Any] 					=	fin.read()
   UpperCAmelCase__				:					List[str] 					=	re.sub(R''' \d+$'''	,      ''''''	,      _A	,      0	,      re.M     )  # remove frequency number
   print(F'''Generating {merges_file}'''     )
   with open(_A	,      '''w'''	,      encoding='''utf-8'''     ) as fout:
      fout.write(_A     )

   # model config
   UpperCAmelCase__				:					Optional[int] 					=	os.path.join(_A	,      '''config.json'''     )

   # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
   # may have to modify the tokenizer if a different type is used by a future model
   assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
   assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''

   UpperCAmelCase__				:					Optional[Any] 					=	{
       '''architectures''': ['''FSMTForConditionalGeneration'''],
       '''model_type''': '''fsmt''',
       '''activation_dropout''': args['''activation_dropout'''],
       '''activation_function''': '''relu''',
       '''attention_dropout''': args['''attention_dropout'''],
       '''d_model''': args['''decoder_embed_dim'''],
       '''dropout''': args['''dropout'''],
       '''init_std''': 0.0_2,
       '''max_position_embeddings''': args['''max_source_positions'''],
       '''num_hidden_layers''': args['''encoder_layers'''],
       '''src_vocab_size''': src_vocab_size,
       '''tgt_vocab_size''': tgt_vocab_size,
       '''langs''': [src_lang, tgt_lang],
       '''encoder_attention_heads''': args['''encoder_attention_heads'''],
       '''encoder_ffn_dim''': args['''encoder_ffn_embed_dim'''],
       '''encoder_layerdrop''': args['''encoder_layerdrop'''],
       '''encoder_layers''': args['''encoder_layers'''],
       '''decoder_attention_heads''': args['''decoder_attention_heads'''],
       '''decoder_ffn_dim''': args['''decoder_ffn_embed_dim'''],
       '''decoder_layerdrop''': args['''decoder_layerdrop'''],
       '''decoder_layers''': args['''decoder_layers'''],
       '''bos_token_id''': 0,
       '''pad_token_id''': 1,
       '''eos_token_id''': 2,
       '''is_encoder_decoder''': True,
       '''scale_embedding''': not args['''no_scale_embedding'''],
       '''tie_word_embeddings''': args['''share_all_embeddings'''],
   }

   # good hparam defaults to start with
   UpperCAmelCase__				:					Optional[int] 					=	5
   UpperCAmelCase__				:					Optional[Any] 					=	False
   if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
      UpperCAmelCase__				:					str 					=	best_score_hparams[model_dir]['''length_penalty''']
   else:
      UpperCAmelCase__				:					Dict 					=	1.0

   print(F'''Generating {fsmt_model_config_file}'''     )
   with open(_A	,      '''w'''	,      encoding='''utf-8'''     ) as f:
      f.write(json.dumps(_A	,      ensure_ascii=_A	,      indent=_A     )     )

   # tokenizer config
   UpperCAmelCase__				:					List[str] 					=	os.path.join(_A	,      _A     )

   UpperCAmelCase__				:					Any 					=	{
       '''langs''': [src_lang, tgt_lang],
       '''model_max_length''': 10_24,
       '''do_lower_case''': do_lower_case,
   }

   print(F'''Generating {fsmt_tokenizer_config_file}'''     )
   with open(_A	,      '''w'''	,      encoding='''utf-8'''     ) as f:
      f.write(json.dumps(_A	,      ensure_ascii=_A	,      indent=_A     )     )

   # model
   UpperCAmelCase__				:					Dict 					=	chkpt['''models'''][0]
   UpperCAmelCase__				:					Any 					=	model.state_dict()

   # rename keys to start with 'model.'
   UpperCAmelCase__				:					Tuple 					=	OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()     )

   # remove unneeded keys
   UpperCAmelCase__				:					Union[str, Any] 					=	[
       '''model.model''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''model.encoder_embed_tokens.weight''',
       '''model.decoder_embed_tokens.weight''',
       '''model.encoder.embed_positions._float_tensor''',
       '''model.decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      model_state_dict.pop(_A	,      _A     )

   UpperCAmelCase__				:					List[str] 					=	FSMTConfig.from_pretrained(_A     )
   UpperCAmelCase__				:					int 					=	FSMTForConditionalGeneration(_A     )

   # check that it loads ok
   model_new.load_state_dict(_A	,      strict=_A     )

   # save
   UpperCAmelCase__				:					List[str] 					=	os.path.join(_A	,      _A     )
   print(F'''Generating {pytorch_weights_dump_path}'''     )
   torch.save(_A	,      _A     )

   print('''Conversion is done!'''     )
   print('''\nLast step is to upload the files to s3'''     )
   print(F'''cd {data_root}'''     )
   print(F'''transformers-cli upload {model_dir}'''     )


if __name__ == "__main__":
  UpperCamelCase__  :						int					=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--fsmt_checkpoint_path',
      default=None,
      type=str,
      required=True,
      help=(
          'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
          ' bpecodes, etc.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  UpperCamelCase__  :						List[str]					=      parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




