"""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
lowercase__					       =	{
    '''/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 						__a   (	_SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
  # the original model
  a__:     int										=	list(s_dict.keys()   )
  for key in keys:
    a__:     Union[str, Any]										=	r'.*/layers_(\d+)'
    a__:     Dict										=	key
    if re.match(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      a__:     List[str]										=	re.sub(r'layers_(\d+)'		,     r'block/\1/layer'		,     _SCREAMING_SNAKE_CASE   )

    a__:     List[Any]										=	r'(encoder|decoder)\/'

    if re.match(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      a__:     Tuple										=	re.match(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ).groups()
      if groups[0] == "encoder":
        a__:     Union[str, Any]										=	re.sub(r'/mlp/'		,     r'/1/mlp/'		,     _SCREAMING_SNAKE_CASE   )
        a__:     Any										=	re.sub(r'/pre_mlp_layer_norm/'		,     r'/1/layer_norm/'		,     _SCREAMING_SNAKE_CASE   )

      elif groups[0] == "decoder":
        a__:     Optional[int]										=	re.sub(r'/mlp/'		,     r'/2/mlp/'		,     _SCREAMING_SNAKE_CASE   )
        a__:     Optional[int]										=	re.sub(r'/pre_mlp_layer_norm/'		,     r'/2/layer_norm/'		,     _SCREAMING_SNAKE_CASE   )

        # 2. Convert other classic mappings
    for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
      if old_key in new_key:
        a__:     str										=	new_key.replace(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

    print(F'{key} -> {new_key}'   )
    a__:     Dict										=	s_dict.pop(_SCREAMING_SNAKE_CASE   )

  if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
    a__:     Any										=	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__:     Tuple										=	s_dict[key]
      for idx in range(_SCREAMING_SNAKE_CASE   ):
        a__:     str										=	expert_weihts[idx]
        print(F'{key} -> {key.replace("expert/"		,     "nested fstring"   )}'   )

      s_dict.pop(_SCREAMING_SNAKE_CASE   )

  return s_dict


lowercase__					       =	{
    '''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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  # Convert a google style config to the hugging face fromat
  import regex as re

  with open(_SCREAMING_SNAKE_CASE		,     'r'   ) as f:
    a__:     List[str]										=	f.read()

  a__:     int										=	re.findall(r'(.*) = ([0-9.]*)'		,     _SCREAMING_SNAKE_CASE   )
  a__:     Optional[Any]										=	{}
  for param, value in regex_match:
    if param in GIN_TO_CONFIG_MAPPING and value != "":
      a__:     str										=	float(_SCREAMING_SNAKE_CASE   ) if '.' in value else int(_SCREAMING_SNAKE_CASE   )

  a__:     Any										=	re.findall(r'(.*activations) = \(\'(.*)\',\)'		,     _SCREAMING_SNAKE_CASE   )[0]
  a__:     List[Any]										=	str(activation[1]   )

  a__:     List[str]										=	num_experts
  a__:     Union[str, Any]										=	SwitchTransformersConfig(**_SCREAMING_SNAKE_CASE   )
  return config







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE="./"		,     _SCREAMING_SNAKE_CASE=8   )	->List[Any]:
  # Initialise PyTorch model

  print(F'Loading flax weights from : {flax_checkpoint_path}'   )
  a__:     List[Any]										=	checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE   )

  if gin_file is not None:
    a__:     str										=	convert_gin_to_config(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  else:
    a__:     int										=	SwitchTransformersConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )

  a__:     Union[str, Any]										=	SwitchTransformersForConditionalGeneration(_SCREAMING_SNAKE_CASE   )

  a__:     Optional[Any]										=	flax_params['target']
  a__:     Optional[int]										=	flatten_dict(_SCREAMING_SNAKE_CASE		,     sep='/'   )
  a__:     Union[str, Any]										=	rename_keys(_SCREAMING_SNAKE_CASE   )
  a__:     Any										=	unflatten_dict(_SCREAMING_SNAKE_CASE		,     sep='/'   )

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

  print(F'Save PyTorch model to {pytorch_dump_path}'   )
  pt_model.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
 lowercase__					       =	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')
 lowercase__					       =	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,
 )









import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				argparse.ArgumentParser()

					# Required parameters
					parser.add_argument(
					    '--src_path'					,						type=a					,						default='biencoder-nq-dev.json'					,						help='Path to raw DPR training data'					,						)
					parser.add_argument(
					    '--evaluation_set'					,						type=a					,						help='where to store parsed evaluation_set file'					,						)
					parser.add_argument(
					    '--gold_data_path'					,						type=a					,						help='where to store parsed gold_data_path file'					,						)
					__A	:						Optional[int]       =				parser.parse_args()

					with open(args.src_path					,						'r'  ) as src_file, open(args.evaluation_set					,						'w'  ) as eval_file, open(
					    args.gold_data_path					,						'w'  ) as gold_file:
										__A	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[context['title'] for context in dpr_record['positive_ctxs']]
															eval_file.write(question + '\n'  )
															gold_file.write('\t'.join(a  ) + '\n'  )


if __name__ == "__main__":
				main()




