



from __future__ import absolute_import, division, print_function, unicode_literals

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

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy






@add_start_docstrings(
    'The RoBERTa Model transformer with early exiting (DeeRoBERTa). '	,      __SCREAMING_SNAKE_CASE	,      )
class      UpperCAmelCase_  (    __SCREAMING_SNAKE_CASE						):
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			RobertaConfig
      __SCREAMING_SNAKE_CASE						:       int								=			'roberta'



      def __init__(					self					:     Optional[int]					,     A					:     Tuple						):
           super().__init__(UpperCamelCase__						)

           _UpperCAmelCase      :     Optional[int]        =   RobertaEmbeddings(UpperCamelCase__						)
           self.init_weights()







@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '	,      __SCREAMING_SNAKE_CASE	,      )
class      UpperCAmelCase_  (    __SCREAMING_SNAKE_CASE						):
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			RobertaConfig
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			'roberta'



      def __init__(					self					:     List[str]					,     A					:     List[Any]						):
           super().__init__(UpperCamelCase__						)
           _UpperCAmelCase      :     Union[str, Any]        =   config.num_labels
           _UpperCAmelCase      :     Optional[Any]        =   config.num_hidden_layers

           _UpperCAmelCase      :     List[str]        =   DeeRobertaModel(UpperCamelCase__						)
           _UpperCAmelCase      :     Dict        =   nn.Dropout(config.hidden_dropout_prob						)
           _UpperCAmelCase      :     Dict        =   nn.Linear(config.hidden_size					,     self.config.num_labels						)



      @add_start_docstrings_to_model_forward(UpperCamelCase__						)
      def 						snake_case_ (					self					:     List[Any]					,     A					:     Tuple=None					,     A					:     str=None					,     A					:     Tuple=None					,     A					:     List[Any]=None					,     A					:     List[str]=None					,     A					:     int=None					,     A					:     Tuple=None					,     A					:     str=-1					,     A					:     List[str]=False					,     ):
           _UpperCAmelCase      :     List[str]        =   self.num_layers
           try:
                _UpperCAmelCase      :     Dict        =   self.roberta(
                    UpperCamelCase__					,     attention_mask=UpperCamelCase__					,     token_type_ids=UpperCamelCase__					,     position_ids=UpperCamelCase__					,     head_mask=UpperCamelCase__					,     inputs_embeds=UpperCamelCase__					,     )

                _UpperCAmelCase      :     Any        =   outputs[1]

                _UpperCAmelCase      :     str        =   self.dropout(UpperCamelCase__						)
                _UpperCAmelCase      :     List[Any]        =   self.classifier(UpperCamelCase__						)
                _UpperCAmelCase      :     Union[str, Any]        =   (logits,) + outputs[2:]  # add hidden states and attention if they are here
           except HighwayException as e:
                _UpperCAmelCase      :     Dict        =   e.message
                _UpperCAmelCase      :     Dict        =   e.exit_layer
                _UpperCAmelCase      :     str        =   outputs[0]

           if not self.training:
                _UpperCAmelCase      :     int        =   entropy(UpperCamelCase__						)
                _UpperCAmelCase      :     Tuple        =   []
                _UpperCAmelCase      :     Dict        =   []
           if labels is not None:
                if self.num_labels == 1:
                     #  We are doing regression
                     _UpperCAmelCase      :     Dict        =   MSELoss()
                     _UpperCAmelCase      :     Optional[Any]        =   loss_fct(logits.view(-1						)					,     labels.view(-1						)						)
                else:
                     _UpperCAmelCase      :     int        =   CrossEntropyLoss()
                     _UpperCAmelCase      :     str        =   loss_fct(logits.view(-1					,     self.num_labels						)					,     labels.view(-1						)						)

                # work with highway exits
                _UpperCAmelCase      :     str        =   []
                for highway_exit in outputs[-1]:
                     _UpperCAmelCase      :     List[str]        =   highway_exit[0]
                     if not self.training:
                          highway_logits_all.append(UpperCamelCase__						)
                          highway_entropy.append(highway_exit[2]						)
                     if self.num_labels == 1:
                          #  We are doing regression
                          _UpperCAmelCase      :     int        =   MSELoss()
                          _UpperCAmelCase      :     Optional[int]        =   loss_fct(highway_logits.view(-1						)					,     labels.view(-1						)						)
                     else:
                          _UpperCAmelCase      :     Tuple        =   CrossEntropyLoss()
                          _UpperCAmelCase      :     Any        =   loss_fct(highway_logits.view(-1					,     self.num_labels						)					,     labels.view(-1						)						)
                     highway_losses.append(UpperCamelCase__						)

                if train_highway:
                     _UpperCAmelCase      :     Tuple        =   (sum(highway_losses[:-1]						),) + outputs
                     # exclude the final highway, of course
                else:
                     _UpperCAmelCase      :     int        =   (loss,) + outputs
           if not self.training:
                _UpperCAmelCase      :     Optional[Any]        =   outputs + ((original_entropy, highway_entropy), exit_layer)
                if output_layer >= 0:
                     _UpperCAmelCase      :     str        =   (
                         (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                     )  # use the highway of the last layer

           return outputs  # (loss), logits, (hidden_states), (attentions), entropy





"""simple docstring"""




import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


_lowerCAmelCase    : List[str]							=							[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict    )  ->       List[str]:





     '''simple docstring'''

     for pegasus_name, hf_name in PATTERNS:
          _UpperCAmelCase      :     str        =   k.replace(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     return k

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						dict						,  SCREAMING_SNAKE_CASE__     :						dict    )  ->       PegasusForConditionalGeneration:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   DEFAULTS.copy()
     cfg_kwargs.update(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusConfig(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   torch_model.model.state_dict()
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     for k, v in tf_weights.items():
          _UpperCAmelCase      :     Union[str, Any]        =   rename_state_dict_key(SCREAMING_SNAKE_CASE__    )
          if new_k not in sd:
               raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'    )

          if "dense" in k or "proj" in new_k:
               _UpperCAmelCase      :     Any        =   v.T
          _UpperCAmelCase      :     str        =   torch.tensor(SCREAMING_SNAKE_CASE__						,  dtype=sd[new_k].dtype    )
          assert v.shape == sd[new_k].shape, f'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
     # make sure embedding.padding_idx is respected
     _UpperCAmelCase      :     Tuple        =   torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]    )
     _UpperCAmelCase      :     Any        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   {k: torch.zeros_like(SCREAMING_SNAKE_CASE__    ) for k, v in sd.items() if k.endswith("bias"    ) and k not in mapping}
     mapping.update(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE__						,  strict=SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     int        =   [
         k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
     ]
     assert unexpected_missing == [], f'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], f'no matches found for the following tf keys {extra}'
     return torch_model

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict="./ckpt/aeslc/model.ckpt-32000"    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   tf.train.list_variables(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     _UpperCAmelCase      :     Optional[Any]        =   ["Adafactor", "global_step"]
     for name, shape in tqdm(SCREAMING_SNAKE_CASE__						,  desc="converting tf checkpoint to dict"    ):
          _UpperCAmelCase      :     Union[str, Any]        =   any(pat in name for pat in ignore_name    )
          if skip_key:
               continue
          _UpperCAmelCase      :     int        =   tf.train.load_variable(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Dict        =   array
     return tf_weights

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   Path(SCREAMING_SNAKE_CASE__    ).parent.name
     _UpperCAmelCase      :     Tuple        =   task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
     _UpperCAmelCase      :     Dict        =   PegasusTokenizer.from_pretrained("sshleifer/pegasus"						,  model_max_length=SCREAMING_SNAKE_CASE__    )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(SCREAMING_SNAKE_CASE__    )

     # convert model
     _UpperCAmelCase      :     Union[str, Any]        =   get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   task_specific_params[f'summarization_{dataset}']
     if dataset == "large":
          _UpperCAmelCase      :     Optional[int]        =   task_specific_params
     _UpperCAmelCase      :     str        =   convert_pegasus(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     torch_model.save_pretrained(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   torch_model.state_dict()
     sd.pop("model.decoder.embed_positions.weight"    )
     sd.pop("model.encoder.embed_positions.weight"    )
     torch.save(SCREAMING_SNAKE_CASE__						,  Path(SCREAMING_SNAKE_CASE__    ) / "pytorch_model.bin"    )


if __name__ == "__main__":
 _lowerCAmelCase    : Union[str, Any]							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
 parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
 _lowerCAmelCase    : Union[str, Any]							=							parser.parse_args()
 if args.save_dir is None:
  _lowerCAmelCase    : Tuple							=							Path(args.tf_ckpt_path).parent.name
  _lowerCAmelCase    : Dict							=							os.path.join("pegasus", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
