import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


a_    					=       logging.getLogger(__name__)





class 		_lowercase						(					snake_case_       ):

 def __init__( self	:							List[str]   ,      snake_case	:							int   ,      snake_case	:							List[str]   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]=None			)   ->						str:

  """simple docstring"""





  super().__init__(
      snake_case   ,      question_encoder_tokenizer=snake_case   ,      generator_tokenizer=snake_case   ,      index=snake_case   ,      init_retrieval=snake_case   ,      )
  UpperCamelCase_					:						Optional[int] =					None

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							int			)   ->						Optional[Any]:

  """simple docstring"""





  logger.info('initializing retrieval'			)

  # initializing a separate process group for retrieval as the default
  # nccl backend doesn't support gather/scatter operations while gloo
  # is too slow to replace nccl for the core gpu communication
  if dist.is_initialized():
   logger.info('dist initialized'			)
   # needs to be set manually
   UpperCamelCase_					:						Tuple =					self._infer_socket_ifname()
   # avoid clash with the NCCL port
   UpperCamelCase_					:						Optional[Any] =					str(distributed_port + 1			)
   UpperCamelCase_					:						List[Any] =					dist.new_group(ranks=snake_case   ,      backend='gloo'			)

  # initialize retriever only on the main worker
  if not dist.is_initialized() or self._is_main():
   logger.info('dist not initialized / main'			)
   self.index.init_index()

  # all processes wait untill the retriever is initialized by the main process
  if dist.is_initialized():
   torch.distributed.barrier(group=self.process_group			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						List[str]:

  """simple docstring"""





  return dist.get_rank(group=self.process_group			) == 0

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple   ,      snake_case	:							Optional[Any]   ,      snake_case	:							Dict   ,      snake_case	:							Optional[int]=torch.floataa			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					torch.empty(snake_case   ,      dtype=snake_case			)
  dist.scatter(snake_case   ,      src=0   ,      scatter_list=snake_case   ,      group=self.process_group			)
  return target_tensor

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					psutil.net_if_addrs()
  # a hacky way to deal with varying network interface names
  UpperCamelCase_					:						Optional[int] =					next((addr for addr in addrs if addr.startswith('e'			))   ,      snake_case			)
  return ifname


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							np.ndarray   ,      snake_case	:							int			)   ->						Tuple[np.ndarray, List[dict]]:

  """simple docstring"""





  if not dist.is_initialized():
   UpperCamelCase_, UpperCamelCase_					:						Dict =					self._main_retrieve(snake_case   ,      snake_case			)
   return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(snake_case			)

  # distributed training
  UpperCamelCase_					:						List[str] =					dist.get_world_size(group=self.process_group			)

  # gather logic
  UpperCamelCase_					:						Tuple =					None
  if self._is_main():
   UpperCamelCase_					:						Tuple =					[torch.empty(question_hidden_states.shape   ,      dtype=torch.floataa			) for _ in range(snake_case			)]
  dist.gather(torch.tensor(snake_case			)   ,      dst=0   ,      gather_list=snake_case   ,      group=self.process_group			)

  # scatter logic
  UpperCamelCase_					:						List[Any] =					question_hidden_states.shape[0]
  UpperCamelCase_					:						List[Any] =					[]
  UpperCamelCase_					:						Optional[Any] =					[]
  if self._is_main():
   assert len(snake_case			) == world_size
   UpperCamelCase_, UpperCamelCase_					:						Tuple =					self._main_retrieve(torch.cat(snake_case			).numpy()   ,      snake_case			)
   UpperCamelCase_, UpperCamelCase_					:						Tuple =					torch.tensor(snake_case			), torch.tensor(snake_case			)
   UpperCamelCase_					:						Optional[Any] =					self._chunk_tensor(snake_case   ,      snake_case			)
   UpperCamelCase_					:						Any =					self._chunk_tensor(snake_case   ,      snake_case			)
  UpperCamelCase_					:						Any =					self._scattered(snake_case   ,      [n_queries, n_docs]   ,      target_type=torch.intaa			)
  UpperCamelCase_					:						str =					self._scattered(snake_case   ,      [n_queries, n_docs, question_hidden_states.shape[1]]			)

  return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(snake_case			)




import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser(
       description=(
           'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
           ' Distillation'
       )
   )
   parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
   parser.add_argument('--model_name', default='roberta-large', type=str)
   parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
   parser.add_argument('--vocab_transform', action='store_true')
   a_    					=       parser.parse_args()

   if args.model_type == "roberta":
      a_    					=       RobertaForMaskedLM.from_pretrained(args.model_name)
      a_    					=       'roberta'
   elif args.model_type == "gpt2":
      a_    					=       GPTaLMHeadModel.from_pretrained(args.model_name)
      a_    					=       'transformer'

   a_    					=       model.state_dict()
   a_    					=       {}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         a_    					=       state_dict[F"""{prefix}.{param_name}"""]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         a_    					=       F"""{prefix}.embeddings.{w}.weight"""
         a_    					=       state_dict[param_name]
      for w in ["weight", "bias"]:
         a_    					=       F"""{prefix}.embeddings.LayerNorm.{w}"""
         a_    					=       state_dict[param_name]

    # Transformer Blocks #
   a_    					=       0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         a_    					=       state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         a_    					=       state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            a_    					=       state_dict[F"""lm_head.dense.{w}"""]
            a_    					=       state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         a_    					=       state_dict[F"""{prefix}.ln_f.{w}"""]
      a_    					=       state_dict['lm_head.weight']

   print(F"""N layers selected for distillation: {std_idx}""")
   print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)



