





'''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:					Tuple							=				{
    """/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 _lowerCAmelCase     (      _lowerCAmelCase    )->     str:
 # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
 # the original model
 __UpperCAmelCase					=   list(s_dict.keys()    )
 for key in keys:
  __UpperCAmelCase					=   r'.*/layers_(\d+)'
  __UpperCAmelCase					=   key
  if re.match(_lowerCAmelCase       ,    _lowerCAmelCase    ):
   __UpperCAmelCase					=   re.sub(r'layers_(\d+)'       ,    r'block/\1/layer'       ,    _lowerCAmelCase    )

  __UpperCAmelCase					=   r'(encoder|decoder)\/'

  if re.match(_lowerCAmelCase       ,    _lowerCAmelCase    ):
   __UpperCAmelCase					=   re.match(_lowerCAmelCase       ,    _lowerCAmelCase    ).groups()
   if groups[0] == "encoder":
    __UpperCAmelCase					=   re.sub(r'/mlp/'       ,    r'/1/mlp/'       ,    _lowerCAmelCase    )
    __UpperCAmelCase					=   re.sub(r'/pre_mlp_layer_norm/'       ,    r'/1/layer_norm/'       ,    _lowerCAmelCase    )

   elif groups[0] == "decoder":
    __UpperCAmelCase					=   re.sub(r'/mlp/'       ,    r'/2/mlp/'       ,    _lowerCAmelCase    )
    __UpperCAmelCase					=   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:
    __UpperCAmelCase					=   new_key.replace(_lowerCAmelCase       ,    _lowerCAmelCase    )

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

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

   s_dict.pop(_lowerCAmelCase    )

 return s_dict


_A:					Dict							=				{
    """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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 # Convert a google style config to the hugging face fromat
 import regex as re

 with open(_lowerCAmelCase       ,    'r'    ) as f:
  __UpperCAmelCase					=   f.read()

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

 __UpperCAmelCase					=   re.findall(r'(.*activations) = \(\'(.*)\',\)'       ,    _lowerCAmelCase    )[0]
 __UpperCAmelCase					=   str(activation[1]    )

 __UpperCAmelCase					=   num_experts
 __UpperCAmelCase					=   SwitchTransformersConfig(**_lowerCAmelCase    )
 return config







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

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

 if gin_file is not None:
  __UpperCAmelCase					=   convert_gin_to_config(_lowerCAmelCase       ,    _lowerCAmelCase    )
 else:
  __UpperCAmelCase					=   SwitchTransformersConfig.from_pretrained(_lowerCAmelCase    )

 __UpperCAmelCase					=   SwitchTransformersForConditionalGeneration(_lowerCAmelCase    )

 __UpperCAmelCase					=   flax_params['target']
 __UpperCAmelCase					=   flatten_dict(_lowerCAmelCase       ,    sep='/'    )
 __UpperCAmelCase					=   rename_keys(_lowerCAmelCase    )
 __UpperCAmelCase					=   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__":
    _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:					Union[str, Any]							=				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 random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A:					int							=				logging.getLogger(__name__)






class UpperCAmelCase							:



    def __init__(  self					):
     __UpperCAmelCase					=   False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A					):
     if not self.initialized:
      __UpperCAmelCase					=   RagRetriever(
          __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
      __UpperCAmelCase					=   True



    def 					__lowerCamelCase						(  self					):
     self.retriever.index.init_index()




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.retriever._main_retrieve(__A     ,			__A					)
     return doc_ids, retrieved_doc_embeds






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None					):
     if index is not None and index.is_initialized() and len(__A					) > 0:
      raise ValueError(
          'When using Ray for distributed fine-tuning, '
          'you\'ll need to provide the paths instead, '
          'as the dataset and the index are loaded '
          'separately. More info in examples/rag/use_own_knowledge_dataset.py '					)
     super().__init__(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
     __UpperCAmelCase					=   retrieval_workers
     if len(self.retrieval_workers					) > 0:
      ray.get(
          [
              worker.create_rag_retriever.remote(__A     ,			__A     ,			__A     ,			__A					)
              for worker in self.retrieval_workers
          ]					)



    def 					__lowerCamelCase						(  self					):
     logger.info('initializing retrieval'					)

     if len(self.retrieval_workers					) > 0:
      ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]					)
     else:
      # Non-distributed training. Load index into this same process.
      self.index.init_index()



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     if len(self.retrieval_workers					) > 0:
      # Select a random retrieval actor.
      __UpperCAmelCase					=   self.retrieval_workers[random.randint(0     ,			len(self.retrieval_workers					) - 1					)]
      __UpperCAmelCase					,    __UpperCAmelCase					=   ray.get(random_worker.retrieve.remote(__A     ,			__A					)					)
     else:
      __UpperCAmelCase					,    __UpperCAmelCase					=   self._main_retrieve(__A     ,			__A					)
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__A					)



    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A=None     ,			**__A					):
     return super(__A     ,			cls					).get_tokenizers(__A     ,			__A     ,			**__A					)




    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					=   kwargs.pop('config'     ,			__A					) or RagConfig.from_pretrained(__A     ,			**__A					)
     __UpperCAmelCase					=   RagTokenizer.from_pretrained(__A     ,			config=__A					)
     __UpperCAmelCase					=   rag_tokenizer.question_encoder
     __UpperCAmelCase					=   rag_tokenizer.generator
     if indexed_dataset is not None:
      __UpperCAmelCase					=   'custom'
      __UpperCAmelCase					=   CustomHFIndex(config.retrieval_vector_size     ,			__A					)
     else:
      __UpperCAmelCase					=   cls._build_index(__A					)
     return cls(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			retrieval_workers=__A     ,			index=__A     ,			)





