


'''simple docstring'''







import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
A__:    int					    =       {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Tuple  )  ->			List[Any]:
       # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
       # the original model
       _a    : Any     					=list(s_dict.keys()  )
       for key in keys:
              _a    : int     					=R""".*/layers_(\d+)"""
              _a    : Union[str, Any]     					=key
              if re.match(_UpperCAmelCase    ,_UpperCAmelCase  ):
                     _a    : str     					=re.sub(R"""layers_(\d+)"""    ,R"""block/\1/layer"""    ,_UpperCAmelCase  )

              _a    : str     					=R"""(encoder|decoder)\/"""

              if re.match(_UpperCAmelCase    ,_UpperCAmelCase  ):
                     _a    : int     					=re.match(_UpperCAmelCase    ,_UpperCAmelCase  ).groups()
                     if groups[0] == "encoder":
                            _a    : List[str]     					=re.sub(R"""/mlp/"""    ,R"""/1/mlp/"""    ,_UpperCAmelCase  )
                            _a    : Union[str, Any]     					=re.sub(R"""/pre_mlp_layer_norm/"""    ,R"""/1/layer_norm/"""    ,_UpperCAmelCase  )

                     elif groups[0] == "decoder":
                            _a    : Dict     					=re.sub(R"""/mlp/"""    ,R"""/2/mlp/"""    ,_UpperCAmelCase  )
                            _a    : List[str]     					=re.sub(R"""/pre_mlp_layer_norm/"""    ,R"""/2/layer_norm/"""    ,_UpperCAmelCase  )

        # 2. Convert other classic mappings
              for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                     if old_key in new_key:
                            _a    : int     					=new_key.replace(_UpperCAmelCase    ,_UpperCAmelCase  )

              print(F"{key} -> {new_key}"  )
              _a    : List[Any]     					=s_dict.pop(_UpperCAmelCase  )

       if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
              _a    : Optional[int]     					=s_dict[
                  """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
              ].T
       if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
              _a    : List[Any]     					=s_dict[
                  """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
              ].T

       # 3. Take extra care of the EXPERTS layer
       for key in list(s_dict.keys()  ):
              if "expert" in key:
                     _a    : Optional[int]     					=s_dict[key].shape[0]
                     _a    : Optional[int]     					=s_dict[key]
                     for idx in range(_UpperCAmelCase  ):
                            _a    : Dict     					=expert_weihts[idx]
                            print(F"{key} -> {key.replace('expert/'    ,'nested fstring'  )}"  )

                     s_dict.pop(_UpperCAmelCase  )

       return s_dict


A__:    Any					    =       {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	int  )  ->			str:
       # Convert a google style config to the hugging face fromat
       import regex as re

       with open(_UpperCAmelCase    ,"""r"""  ) as f:
              _a    : Dict     					=f.read()

       _a    : Union[str, Any]     					=re.findall(R"""(.*) = ([0-9.]*)"""    ,_UpperCAmelCase  )
       _a    : Optional[int]     					={}
       for param, value in regex_match:
              if param in GIN_TO_CONFIG_MAPPING and value != "":
                     _a    : Dict     					=float(_UpperCAmelCase  ) if """.""" in value else int(_UpperCAmelCase  )

       _a    : Tuple     					=re.findall(R"""(.*activations) = \(\'(.*)\',\)"""    ,_UpperCAmelCase  )[0]
       _a    : List[str]     					=str(activation[1]  )

       _a    : Any     					=num_experts
       _a    : Dict     					=SwitchTransformersConfig(**_UpperCAmelCase  )
       return config


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	Union[str, Any]=None    ,_UpperCAmelCase	:	List[Any]="./"    ,_UpperCAmelCase	:	Optional[Any]=8  )  ->			List[str]:
       # Initialise PyTorch model

       print(F"Loading flax weights from : {flax_checkpoint_path}"  )
       _a    : Optional[Any]     					=checkpoints.load_tax_checkpoint(_UpperCAmelCase  )

       if gin_file is not None:
              _a    : List[Any]     					=convert_gin_to_config(_UpperCAmelCase    ,_UpperCAmelCase  )
       else:
              _a    : int     					=SwitchTransformersConfig.from_pretrained(_UpperCAmelCase  )

       _a    : List[str]     					=SwitchTransformersForConditionalGeneration(_UpperCAmelCase  )

       _a    : List[str]     					=flax_params["""target"""]
       _a    : Union[str, Any]     					=flatten_dict(_UpperCAmelCase    ,sep="""/"""  )
       _a    : str     					=rename_keys(_UpperCAmelCase  )
       _a    : List[Any]     					=unflatten_dict(_UpperCAmelCase    ,sep="""/"""  )

       # Load the flax params in the PT model
       load_flax_weights_in_pytorch_model(_UpperCAmelCase    ,_UpperCAmelCase  )

       print(F"Save PyTorch model to {pytorch_dump_path}"  )
       pt_model.save_pretrained(_UpperCAmelCase  )


if __name__ == "__main__":
   A__:    Tuple					    =       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--switch_t5x_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
           ''' model architecture. If not provided, a `gin_file` has to be provided.'''
       ),
   )
   parser.add_argument(
       '''--gin_file''',
       default=None,
       type=str,
       required=False,
       help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
   )
   parser.add_argument(
       '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
   )
   parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
   A__:    Optional[int]					    =       parser.parse_args()
   convert_flax_checkpoint_to_pytorch(
       args.switch_tax_checkpoint_path,
       args.config_name,
       args.gin_file,
       args.pytorch_dump_folder_path,
       args.num_experts,
   )






'''simple docstring'''







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


A__:    int					    =       logging.getLogger(__name__)







@dataclass
class A__       :
      __UpperCamelCase       :  str
      __UpperCamelCase       :  List[str]
      __UpperCamelCase       :  Optional[List[str]]







@dataclass
class A__       :
      __UpperCamelCase       :  List[int]
      __UpperCamelCase       :  List[int]
      __UpperCamelCase       :  Optional[List[int]]	       =							None
      __UpperCamelCase       :  Optional[List[int]]	       =							None







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str	       =							"train"
      __UpperCamelCase       :  Tuple	       =							"dev"
      __UpperCamelCase       :  str	       =							"test"







class A__       :






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:Dict     ,						SCREAMING_SNAKE_CASE			:Union[Split, str]	)       ->					List[InputExample]:




             '''simple docstring'''
             raise NotImplementedError






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:str	)       ->					List[str]:




             '''simple docstring'''
             raise NotImplementedError






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:List[InputExample]     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:int     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:str=False     ,						SCREAMING_SNAKE_CASE			:Optional[Any]="[CLS]"     ,						SCREAMING_SNAKE_CASE			:Optional[int]=1     ,						SCREAMING_SNAKE_CASE			:Any="[SEP]"     ,						SCREAMING_SNAKE_CASE			:List[Any]=False     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=False     ,						SCREAMING_SNAKE_CASE			:List[str]=0     ,						SCREAMING_SNAKE_CASE			:str=0     ,						SCREAMING_SNAKE_CASE			:Dict=-1_0_0     ,						SCREAMING_SNAKE_CASE			:Optional[int]=0     ,						SCREAMING_SNAKE_CASE			:Tuple=True     ,						)       ->					List[InputFeatures]:




             '''simple docstring'''
             _a    : str     					={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE	)}

             _a    : Tuple     					=[]
             for ex_index, example in enumerate(SCREAMING_SNAKE_CASE	):
                    if ex_index % 1_0_0_0_0 == 0:
                           logger.info("""Writing example %d of %d"""     ,						SCREAMING_SNAKE_CASE     ,						len(SCREAMING_SNAKE_CASE	)	)

                    _a    : Optional[Any]     					=[]
                    _a    : List[Any]     					=[]
                    for word, label in zip(example.words     ,						example.labels	):
                           _a    : Optional[int]     					=tokenizer.tokenize(SCREAMING_SNAKE_CASE	)

                           # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                           if len(SCREAMING_SNAKE_CASE	) > 0:
                                  tokens.extend(SCREAMING_SNAKE_CASE	)
                                  # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                                  label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(SCREAMING_SNAKE_CASE	) - 1)	)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                    _a    : Optional[int]     					=tokenizer.num_special_tokens_to_add()
                    if len(SCREAMING_SNAKE_CASE	) > max_seq_length - special_tokens_count:
                           _a    : List[Any]     					=tokens[: (max_seq_length - special_tokens_count)]
                           _a    : Tuple     					=label_ids[: (max_seq_length - special_tokens_count)]

                    # The convention in BERT is:
                    # (a) For sequence pairs:
                    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                    #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                    # (b) For single sequences:
                    #  tokens:   [CLS] the dog is hairy . [SEP]
                    #  type_ids:   0   0   0   0  0     0   0
                    #
                    # Where "type_ids" are used to indicate whether this is the first
                    # sequence or the second sequence. The embedding vectors for `type=0` and
                    # `type=1` were learned during pre-training and are added to the wordpiece
                    # embedding vector (and position vector). This is not *strictly* necessary
                    # since the [SEP] token unambiguously separates the sequences, but it makes
                    # it easier for the model to learn the concept of sequences.
                    #
                    # For classification tasks, the first vector (corresponding to [CLS]) is
                    # used as the "sentence vector". Note that this only makes sense because
                    # the entire model is fine-tuned.
                    tokens += [sep_token]
                    label_ids += [pad_token_label_id]
                    if sep_token_extra:
                           # roberta uses an extra separator b/w pairs of sentences
                           tokens += [sep_token]
                           label_ids += [pad_token_label_id]
                    _a    : Dict     					=[sequence_a_segment_id] * len(SCREAMING_SNAKE_CASE	)

                    if cls_token_at_end:
                           tokens += [cls_token]
                           label_ids += [pad_token_label_id]
                           segment_ids += [cls_token_segment_id]
                    else:
                           _a    : Any     					=[cls_token] + tokens
                           _a    : Dict     					=[pad_token_label_id] + label_ids
                           _a    : Union[str, Any]     					=[cls_token_segment_id] + segment_ids

                    _a    : List[str]     					=tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE	)

                    # The mask has 1 for real tokens and 0 for padding tokens. Only real
                    # tokens are attended to.
                    _a    : Optional[int]     					=[1 if mask_padding_with_zero else 0] * len(SCREAMING_SNAKE_CASE	)

                    # Zero-pad up to the sequence length.
                    _a    : Union[str, Any]     					=max_seq_length - len(SCREAMING_SNAKE_CASE	)
                    if pad_on_left:
                           _a    : Optional[Any]     					=([pad_token] * padding_length) + input_ids
                           _a    : Optional[int]     					=([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                           _a    : Union[str, Any]     					=([pad_token_segment_id] * padding_length) + segment_ids
                           _a    : Dict     					=([pad_token_label_id] * padding_length) + label_ids
                    else:
                           input_ids += [pad_token] * padding_length
                           input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                           segment_ids += [pad_token_segment_id] * padding_length
                           label_ids += [pad_token_label_id] * padding_length

                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length

                    if ex_index < 5:
                           logger.info("""*** Example ***"""	)
                           logger.info("""guid: %s"""     ,						example.guid	)
                           logger.info("""tokens: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in tokens]	)	)
                           logger.info("""input_ids: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in input_ids]	)	)
                           logger.info("""input_mask: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in input_mask]	)	)
                           logger.info("""segment_ids: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in segment_ids]	)	)
                           logger.info("""label_ids: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in label_ids]	)	)

                    if "token_type_ids" not in tokenizer.model_input_names:
                           _a    : Tuple     					=None

                    features.append(
                        InputFeatures(
                            input_ids=SCREAMING_SNAKE_CASE     ,						attention_mask=SCREAMING_SNAKE_CASE     ,						token_type_ids=SCREAMING_SNAKE_CASE     ,						label_ids=SCREAMING_SNAKE_CASE	)	)
             return features


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset







   class A__       (						UpperCAmelCase__      ):
         __UpperCamelCase       :  List[InputFeatures]
         __UpperCamelCase       :  int	       =							nn.CrossEntropyLoss().ignore_index






         def __init__( self			:Dict     ,						SCREAMING_SNAKE_CASE			:TokenClassificationTask     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:int=False     ,						SCREAMING_SNAKE_CASE			:Split = Split.train     ,						)       ->					List[str]:




                '''simple docstring'''
                # Load data features from cache or dataset file
                _a    : Optional[Any]     					=os.path.join(
                    SCREAMING_SNAKE_CASE     ,						"""cached_{}_{}_{}""".format(mode.value     ,						tokenizer.__class__.__name__     ,						str(SCREAMING_SNAKE_CASE	)	)     ,						)

                # Make sure only the first process in distributed training processes the dataset,
                # and the others will use the cache.
                _a    : List[str]     					=cached_features_file + """.lock"""
                with FileLock(SCREAMING_SNAKE_CASE	):
                       if os.path.exists(SCREAMING_SNAKE_CASE	) and not overwrite_cache:
                              logger.info(f"Loading features from cached file {cached_features_file}"	)
                              _a    : Any     					=torch.load(SCREAMING_SNAKE_CASE	)
                       else:
                              logger.info(f"Creating features from dataset file at {data_dir}"	)
                              _a    : Any     					=token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                              # TODO clean up all this to leverage built-in features of tokenizers
                              _a    : List[str]     					=token_classification_task.convert_examples_to_features(
                                  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						cls_token_at_end=bool(model_type in ["""xlnet"""]	)     ,						cls_token=tokenizer.cls_token     ,						cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,						sep_token=tokenizer.sep_token     ,						sep_token_extra=SCREAMING_SNAKE_CASE     ,						pad_on_left=bool(tokenizer.padding_side == """left"""	)     ,						pad_token=tokenizer.pad_token_id     ,						pad_token_segment_id=tokenizer.pad_token_type_id     ,						pad_token_label_id=self.pad_token_label_id     ,						)
                              logger.info(f"Saving features into cached file {cached_features_file}"	)
                              torch.save(self.features     ,						SCREAMING_SNAKE_CASE	)






         def __len__( self			:Optional[int]	)       ->					Union[str, Any]:




                '''simple docstring'''
                return len(self.features	)






         def __getitem__( self			:Dict     ,						SCREAMING_SNAKE_CASE			:int	)       ->					InputFeatures:




                '''simple docstring'''
                return self.features[i]


if is_tf_available():
   import tensorflow as tf







   class A__       :
         __UpperCamelCase       :  List[InputFeatures]
         __UpperCamelCase       :  int	       =							-100






         def __init__( self			:str     ,						SCREAMING_SNAKE_CASE			:TokenClassificationTask     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:str=False     ,						SCREAMING_SNAKE_CASE			:Split = Split.train     ,						)       ->					Any:




                '''simple docstring'''
                _a    : Tuple     					=token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                # TODO clean up all this to leverage built-in features of tokenizers
                _a    : List[Any]     					=token_classification_task.convert_examples_to_features(
                    SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						cls_token_at_end=bool(model_type in ["""xlnet"""]	)     ,						cls_token=tokenizer.cls_token     ,						cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,						sep_token=tokenizer.sep_token     ,						sep_token_extra=SCREAMING_SNAKE_CASE     ,						pad_on_left=bool(tokenizer.padding_side == """left"""	)     ,						pad_token=tokenizer.pad_token_id     ,						pad_token_segment_id=tokenizer.pad_token_type_id     ,						pad_token_label_id=self.pad_token_label_id     ,						)

                def gen():
                       for ex in self.features:
                              if ex.token_type_ids is None:
                                     yield (
                                         {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                         ex.label_ids,
                                     )
                              else:
                                     yield (
                                         {
                                             "input_ids": ex.input_ids,
                                             "attention_mask": ex.attention_mask,
                                             "token_type_ids": ex.token_type_ids,
                                         },
                                         ex.label_ids,
                                     )

                if "token_type_ids" not in tokenizer.model_input_names:
                       _a    : Union[str, Any]     					=tf.data.Dataset.from_generator(
                           SCREAMING_SNAKE_CASE     ,						({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,						(
                               {"""input_ids""": tf.TensorShape([None]	), """attention_mask""": tf.TensorShape([None]	)},
                               tf.TensorShape([None]	),
                           )     ,						)
                else:
                       _a    : Union[str, Any]     					=tf.data.Dataset.from_generator(
                           SCREAMING_SNAKE_CASE     ,						({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     ,						(
                               {
                                   """input_ids""": tf.TensorShape([None]	),
                                   """attention_mask""": tf.TensorShape([None]	),
                                   """token_type_ids""": tf.TensorShape([None]	),
                               },
                               tf.TensorShape([None]	),
                           )     ,						)






         def __UpperCAmelCase       ( self			:Tuple	)       ->					Any:




                '''simple docstring'''
                _a    : List[Any]     					=self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features	)	)	)

                return self.dataset






         def __len__( self			:str	)       ->					Optional[int]:




                '''simple docstring'''
                return len(self.features	)






         def __getitem__( self			:int     ,						SCREAMING_SNAKE_CASE			:str	)       ->					InputFeatures:




                '''simple docstring'''
                return self.features[i]


