

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      (			A__	:   Any     )    ->      Union[str, Any]:





      """simple docstring"""



      _lowercase	       =list(s_dict.keys()     )
      for key in keys:
            _lowercase	       =r'.*/layers_(\d+)'
            _lowercase	       =key
            if re.match(A__      ,   A__     ):
                  _lowercase	       =re.sub(r'layers_(\d+)'      ,   r'block/\1/layer'      ,   A__     )

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

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

                  elif groups[0] == "decoder":
                        _lowercase	       =re.sub(r'/mlp/'      ,   r'/2/mlp/'      ,   A__     )
                        _lowercase	       =re.sub(r'/pre_mlp_layer_norm/'      ,   r'/2/layer_norm/'      ,   A__     )

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

            print(F'''{key} -> {new_key}'''     )
            _lowercase	       =s_dict.pop(A__     )

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

                  s_dict.pop(A__     )

      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      (			A__	:   List[Any]      ,   A__	:   Union[str, Any]     )    ->      List[str]:





      """simple docstring"""



      import regex as re

      with open(A__      ,   'r'     ) as f:
            _lowercase	       =f.read()

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

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

      _lowercase	       =num_experts
      _lowercase	       =SwitchTransformersConfig(**A__     )
      return config
def      a      (			A__	:   Optional[int]      ,   A__	:   Union[str, Any]      ,   A__	:   int=None      ,   A__	:   str="./"      ,   A__	:   Tuple=8     )    ->      str:





      """simple docstring"""



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

      if gin_file is not None:
            _lowercase	       =convert_gin_to_config(A__      ,   A__     )
      else:
            _lowercase	       =SwitchTransformersConfig.from_pretrained(A__     )

      _lowercase	       =SwitchTransformersForConditionalGeneration(A__     )

      _lowercase	       =flax_params['target']
      _lowercase	       =flatten_dict(A__      ,   sep='/'     )
      _lowercase	       =rename_keys(A__     )
      _lowercase	       =unflatten_dict(A__      ,   sep='/'     )

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

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


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 logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowercase_									=   logging.getLogger(__name__)





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase=None							,   lowerCAmelCase=None					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =self.layer[current_layer](lowerCAmelCase							,   lowerCAmelCase							,   head_mask[current_layer]					)

        _lowercase	       =layer_outputs[0]

        return hidden_states





@add_start_docstrings(
    """The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."""			,			SCREAMING_SNAKE_CASE			,			)
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)

        _lowercase	       =BertEncoderWithPabee(lowerCAmelCase					)

        self.init_weights()
        _lowercase	       =0
        _lowercase	       =0
        _lowercase	       =0

        _lowercase	       =0




  def   A__     (				self							,   lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =threshold




  def   A__     (				self							,   lowerCAmelCase					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =patience




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =0
        _lowercase	       =0




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =self.inference_layers_num / self.inference_instances_num
        _lowercase	       =(
            F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
            F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
        )
        print(lowerCAmelCase					)




  @add_start_docstrings_to_model_forward(lowerCAmelCase					)
  def   A__     (				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=False							,   )  -> str:
        '''simple docstring'''






        if input_ids is not None and inputs_embeds is not None:
              raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'					)
        elif input_ids is not None:
              _lowercase	       =input_ids.size()
        elif inputs_embeds is not None:
              _lowercase	       =inputs_embeds.size()[:-1]
        else:
              raise ValueError('You have to specify either input_ids or inputs_embeds'					)

        _lowercase	       =input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
              _lowercase	       =torch.ones(lowerCAmelCase							,   device=lowerCAmelCase					)
        if token_type_ids is None:
              _lowercase	       =torch.zeros(lowerCAmelCase							,   dtype=torch.long							,   device=lowerCAmelCase					)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        _lowercase	       =self.get_extended_attention_mask(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
              _lowercase       ,       _lowercase       ,       _lowercase	       =encoder_hidden_states.size()
              _lowercase	       =(encoder_batch_size, encoder_sequence_length)
              if encoder_attention_mask is None:
                    _lowercase	       =torch.ones(lowerCAmelCase							,   device=lowerCAmelCase					)
              _lowercase	       =self.invert_attention_mask(lowerCAmelCase					)
        else:
              _lowercase	       =None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        _lowercase	       =self.get_head_mask(lowerCAmelCase							,   self.config.num_hidden_layers					)

        _lowercase	       =self.embeddings(
            input_ids=lowerCAmelCase							,   position_ids=lowerCAmelCase							,   token_type_ids=lowerCAmelCase							,   inputs_embeds=lowerCAmelCase					)
        _lowercase	       =embedding_output

        if self.training:
              _lowercase	       =[]
              for i in range(self.config.num_hidden_layers					):
                    _lowercase	       =self.encoder.adaptive_forward(
                        lowerCAmelCase							,   current_layer=lowerCAmelCase							,   attention_mask=lowerCAmelCase							,   head_mask=lowerCAmelCase					)

                    _lowercase	       =self.pooler(lowerCAmelCase					)
                    _lowercase	       =output_layers[i](output_dropout(lowerCAmelCase					)					)
                    res.append(lowerCAmelCase					)
        elif self.patience == 0:  # Use all layers for inference
              _lowercase	       =self.encoder(
                  lowerCAmelCase							,   attention_mask=lowerCAmelCase							,   head_mask=lowerCAmelCase							,   encoder_hidden_states=lowerCAmelCase							,   encoder_attention_mask=lowerCAmelCase							,   )
              _lowercase	       =self.pooler(encoder_outputs[0]					)
              _lowercase	       =[output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase					)]
        else:
              _lowercase	       =0
              _lowercase	       =None
              _lowercase	       =0
              for i in range(self.config.num_hidden_layers					):
                    calculated_layer_num += 1
                    _lowercase	       =self.encoder.adaptive_forward(
                        lowerCAmelCase							,   current_layer=lowerCAmelCase							,   attention_mask=lowerCAmelCase							,   head_mask=lowerCAmelCase					)

                    _lowercase	       =self.pooler(lowerCAmelCase					)
                    _lowercase	       =output_layers[i](lowerCAmelCase					)
                    if regression:
                          _lowercase	       =logits.detach()
                          if patient_result is not None:
                                _lowercase	       =patient_result.detach()
                          if (patient_result is not None) and torch.abs(patient_result - labels					) < self.regression_threshold:
                                patient_counter += 1
                          else:
                                _lowercase	       =0
                    else:
                          _lowercase	       =logits.detach().argmax(dim=1					)
                          if patient_result is not None:
                                _lowercase	       =patient_result.detach().argmax(dim=1					)
                          if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase					)					):
                                patient_counter += 1
                          else:
                                _lowercase	       =0

                    _lowercase	       =logits
                    if patient_counter == self.patience:
                          break
              _lowercase	       =[patient_result]
              self.inference_layers_num += calculated_layer_num
              self.inference_instances_num += 1

        return res





@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """			,			SCREAMING_SNAKE_CASE			,			)
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase					)  -> List[Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)
        _lowercase	       =config.num_labels

        _lowercase	       =BertModelWithPabee(lowerCAmelCase					)
        _lowercase	       =nn.Dropout(config.hidden_dropout_prob					)
        _lowercase	       =nn.ModuleList(
            [nn.Linear(config.hidden_size							,   self.config.num_labels					) for _ in range(config.num_hidden_layers					)]					)

        self.init_weights()




  @add_start_docstrings_to_model_forward(lowerCAmelCase					)
  def   A__     (				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   )  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.bert(
            input_ids=lowerCAmelCase							,   attention_mask=lowerCAmelCase							,   token_type_ids=lowerCAmelCase							,   position_ids=lowerCAmelCase							,   head_mask=lowerCAmelCase							,   inputs_embeds=lowerCAmelCase							,   output_dropout=self.dropout							,   output_layers=self.classifiers							,   regression=self.num_labels == 1							,   )

        _lowercase	       =(logits[-1],)

        if labels is not None:
              _lowercase	       =None
              _lowercase	       =0
              for ix, logits_item in enumerate(lowerCAmelCase					):
                    if self.num_labels == 1:
                          #  We are doing regression
                          _lowercase	       =MSELoss()
                          _lowercase	       =loss_fct(logits_item.view(-1					)							,   labels.view(-1					)					)
                    else:
                          _lowercase	       =CrossEntropyLoss()
                          _lowercase	       =loss_fct(logits_item.view(-1							,   self.num_labels					)							,   labels.view(-1					)					)
                    if total_loss is None:
                          _lowercase	       =loss
                    else:
                          total_loss += loss * (ix + 1)
                    total_weights += ix + 1
              _lowercase	       =(total_loss / total_weights,) + outputs

        return outputs


