
'''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
UpperCAmelCase_       :   Dict   =       {
    '/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_       (	_lowerCAmelCase   :    Any							):



      """simple docstring"""




      _lowerCamelCase				:   int								=   list(s_dict.keys()							)
      for key in keys:
            _lowerCamelCase				:   List[Any]								=   r".*/layers_(\d+)"
            _lowerCamelCase				:   int								=   key
            if re.match(_lowerCAmelCase				,	_lowerCAmelCase							):
                  _lowerCamelCase				:   Optional[Any]								=   re.sub(r"layers_(\d+)"				,	r"block/\1/layer"				,	_lowerCAmelCase							)

            _lowerCamelCase				:   str								=   r"(encoder|decoder)\/"

            if re.match(_lowerCAmelCase				,	_lowerCAmelCase							):
                  _lowerCamelCase				:   Optional[int]								=   re.match(_lowerCAmelCase				,	_lowerCAmelCase							).groups()
                  if groups[0] == "encoder":
                        _lowerCamelCase				:   Tuple								=   re.sub(r"/mlp/"				,	r"/1/mlp/"				,	_lowerCAmelCase							)
                        _lowerCamelCase				:   Any								=   re.sub(r"/pre_mlp_layer_norm/"				,	r"/1/layer_norm/"				,	_lowerCAmelCase							)

                  elif groups[0] == "decoder":
                        _lowerCamelCase				:   int								=   re.sub(r"/mlp/"				,	r"/2/mlp/"				,	_lowerCAmelCase							)
                        _lowerCamelCase				:   int								=   re.sub(r"/pre_mlp_layer_norm/"				,	r"/2/layer_norm/"				,	_lowerCAmelCase							)

        # 2. Convert other classic mappings
            for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                  if old_key in new_key:
                        _lowerCamelCase				:   Optional[int]								=   new_key.replace(_lowerCAmelCase				,	_lowerCAmelCase							)

            print(F'{key} -> {new_key}'							)
            _lowerCamelCase				:   Any								=   s_dict.pop(_lowerCAmelCase							)

      if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            _lowerCamelCase				:   Dict								=   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:
            _lowerCamelCase				:   Dict								=   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:
                  _lowerCamelCase				:   List[str]								=   s_dict[key].shape[0]
                  _lowerCamelCase				:   Optional[Any]								=   s_dict[key]
                  for idx in range(_lowerCAmelCase							):
                        _lowerCamelCase				:   int								=   expert_weihts[idx]
                        print(F'{key} -> {key.replace("expert/"				,	"nested fstring"							)}'							)

                  s_dict.pop(_lowerCAmelCase							)

      return s_dict


UpperCAmelCase_       :   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 		A_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      import regex as re

      with open(_lowerCAmelCase				,	"r"							) as f:
            _lowerCamelCase				:   int								=   f.read()

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

      _lowerCamelCase				:   List[str]								=   re.findall(r"(.*activations) = \(\'(.*)\',\)"				,	_lowerCAmelCase							)[0]
      _lowerCamelCase				:   Union[str, Any]								=   str(activation[1]							)

      _lowerCamelCase				:   Any								=   num_experts
      _lowerCamelCase				:   str								=   SwitchTransformersConfig(**_lowerCAmelCase							)
      return config







def 		A_       (	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    str=None				,	_lowerCAmelCase   :    List[Any]="./"				,	_lowerCAmelCase   :    Tuple=8							):



      """simple docstring"""




      print(F'Loading flax weights from : {flax_checkpoint_path}'							)
      _lowerCamelCase				:   str								=   checkpoints.load_tax_checkpoint(_lowerCAmelCase							)

      if gin_file is not None:
            _lowerCamelCase				:   List[Any]								=   convert_gin_to_config(_lowerCAmelCase				,	_lowerCAmelCase							)
      else:
            _lowerCamelCase				:   Dict								=   SwitchTransformersConfig.from_pretrained(_lowerCAmelCase							)

      _lowerCamelCase				:   int								=   SwitchTransformersForConditionalGeneration(_lowerCAmelCase							)

      _lowerCamelCase				:   List[Any]								=   flax_params["target"]
      _lowerCamelCase				:   Tuple								=   flatten_dict(_lowerCAmelCase				,	sep="/"							)
      _lowerCamelCase				:   Optional[Any]								=   rename_keys(_lowerCAmelCase							)
      _lowerCamelCase				:   int								=   unflatten_dict(_lowerCAmelCase				,	sep="/"							)

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

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


if __name__ == "__main__":
 UpperCAmelCase_       :   str   =       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')
 UpperCAmelCase_       :   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'''






from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_       :   Union[str, Any]   =       logging.get_logger(__name__)

UpperCAmelCase_       :   Optional[int]   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_       :   Union[str, Any]   =       {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

UpperCAmelCase_       :   Union[str, Any]   =       {
    'facebook/blenderbot_small-90M': 512,
}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		BlenderbotSmallTokenizer



    def __init__(	self      :       Optional[Any],__A      :       str=None,__A      :       str=None,__A      :       int="<|endoftext|>",__A      :       List[str]="<|endoftext|>",__A      :       Optional[Any]="<|endoftext|>",__A      :       Union[str, Any]=False,__A      :       List[Any]=True,**__A      :       str,):
          super().__init__(
              ByteLevelBPETokenizer(
                  vocab=__A,merges=__A,add_prefix_space=__A,trim_offsets=__A,),bos_token=__A,eos_token=__A,unk_token=__A,**__A,)
          _lowerCamelCase				:   List[Any]								=   add_prefix_space



    def   lowerCamelCase_  (	self      :       Any,__A      :       Optional[Any],__A      :       Optional[int]=None     ):
          _lowerCamelCase				:   Any								=   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
                return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   Any								=   [self.sep_token_id]
          _lowerCamelCase				:   Union[str, Any]								=   [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 + sep + token_ids_a + sep     ) * [0]