


import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 			UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_="attention"				):



     """simple docstring"""






     snake_case		 =				params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
     snake_case		 =				params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
     snake_case		 =				params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
     snake_case		 =				params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
     return k, o, q, v
def 			UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=False				):



     """simple docstring"""






     if split_mlp_wi:
          snake_case		 =				params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
          snake_case		 =				params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
          snake_case		 =				(wi_a, wi_a)
     else:
          snake_case		 =				params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

     snake_case		 =				params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
     return wi, wo
def 			UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_				):



     """simple docstring"""






     return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def 			UpperCAmelCase__						(UpperCamelCase_							,*, UpperCamelCase_							,UpperCamelCase_				):



     """simple docstring"""






     snake_case		 =				traverse_util.flatten_dict(variables['''target''']				)
     snake_case		 =				{'''/'''.join(A__				): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     snake_case		 =				'''encoder/layers_0/mlp/wi_0/kernel''' in old
     print('''Split MLP:'''							,A__				)

     snake_case		 =				collections.OrderedDict()

     # Shared embeddings.
     snake_case		 =				old['''token_embedder/embedding''']

     # Encoder.
     for i in range(A__				):
          # Block i, layer 0 (Self Attention).
          snake_case		 =				tax_layer_norm_lookup(A__							,A__							,'''encoder'''							,'''pre_attention_layer_norm'''				)
          snake_case ,  snake_case ,  snake_case ,  snake_case		 =				tax_attention_lookup(A__							,A__							,'''encoder'''							,'''attention'''				)
          snake_case		 =				layer_norm
          snake_case		 =				k.T
          snake_case		 =				o.T
          snake_case		 =				q.T
          snake_case		 =				v.T

          # Block i, layer 1 (MLP).
          snake_case		 =				tax_layer_norm_lookup(A__							,A__							,'''encoder'''							,'''pre_mlp_layer_norm'''				)
          snake_case ,  snake_case		 =				tax_mlp_lookup(A__							,A__							,'''encoder'''							,A__				)
          snake_case		 =				layer_norm
          if split_mlp_wi:
               snake_case		 =				wi[0].T
               snake_case		 =				wi[1].T
          else:
               snake_case		 =				wi.T
          snake_case		 =				wo.T

     snake_case		 =				old[
         '''encoder/relpos_bias/rel_embedding'''
     ].T
     snake_case		 =				old['''encoder/encoder_norm/scale''']

     if not is_encoder_only:
          # Decoder.
          for i in range(A__				):
               # Block i, layer 0 (Self Attention).
               snake_case		 =				tax_layer_norm_lookup(A__							,A__							,'''decoder'''							,'''pre_self_attention_layer_norm'''				)
               snake_case ,  snake_case ,  snake_case ,  snake_case		 =				tax_attention_lookup(A__							,A__							,'''decoder'''							,'''self_attention'''				)
               snake_case		 =				layer_norm
               snake_case		 =				k.T
               snake_case		 =				o.T
               snake_case		 =				q.T
               snake_case		 =				v.T

               # Block i, layer 1 (Cross Attention).
               snake_case		 =				tax_layer_norm_lookup(A__							,A__							,'''decoder'''							,'''pre_cross_attention_layer_norm'''				)
               snake_case ,  snake_case ,  snake_case ,  snake_case		 =				tax_attention_lookup(A__							,A__							,'''decoder'''							,'''encoder_decoder_attention'''				)
               snake_case		 =				layer_norm
               snake_case		 =				k.T
               snake_case		 =				o.T
               snake_case		 =				q.T
               snake_case		 =				v.T

               # Block i, layer 2 (MLP).
               snake_case		 =				tax_layer_norm_lookup(A__							,A__							,'''decoder'''							,'''pre_mlp_layer_norm'''				)
               snake_case ,  snake_case		 =				tax_mlp_lookup(A__							,A__							,'''decoder'''							,A__				)
               snake_case		 =				layer_norm
               if split_mlp_wi:
                    snake_case		 =				wi[0].T
                    snake_case		 =				wi[1].T
               else:
                    snake_case		 =				wi.T
               snake_case		 =				wo.T

          snake_case		 =				old['''decoder/decoder_norm/scale''']
          snake_case		 =				old[
              '''decoder/relpos_bias/rel_embedding'''
          ].T

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               snake_case		 =				old['''decoder/logits_dense/kernel'''].T

     return new
def 			UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_				):



     """simple docstring"""






     snake_case		 =				collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

     # Add what is missing.
     if "encoder.embed_tokens.weight" not in state_dict:
          snake_case		 =				state_dict['''shared.weight''']

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               snake_case		 =				state_dict['''shared.weight''']

          if "lm_head.weight" not in state_dict:  # For old 1.0 models.
               print('''Using shared word embeddings as lm_head.'''				)
               snake_case		 =				state_dict['''shared.weight''']

     return state_dict
def 			UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_				):



     """simple docstring"""






     snake_case		 =				checkpoints.load_tax_checkpoint(A__				)
     snake_case		 =				convert_tax_to_pytorch(A__							,num_layers=config.num_layers							,is_encoder_only=A__				)
     snake_case		 =				make_state_dict(A__							,A__				)
     model.load_state_dict(A__							,strict=A__				)
def 			UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = False				):



     """simple docstring"""






     snake_case		 =				TaConfig.from_json_file(A__				)
     print(F'''Building PyTorch model from configuration: {config}'''				)
     # Non-v1.1 checkpoints could also use T5Model, but this works for all.
     # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
     if is_encoder_only:
          snake_case		 =				TaEncoderModel(A__				)
     else:
          snake_case		 =				TaForConditionalGeneration(A__				)

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(A__							,A__							,A__							,A__				)

     # Save pytorch-model
     print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
     model.save_pretrained(A__				)

     # Verify that we can load the checkpoint.
     model.from_pretrained(A__				)
     print('''Done'''				)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE			: Optional[Any]			      =    argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
 # Required parameters
 parser.add_argument(
     "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
 )
 _SCREAMING_SNAKE_CASE			: Any			      =    parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
 )







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


