




'''simple docstring'''





import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


UpperCamelCase_						:      Union[str, Any]       =						logging.getLogger(__name__)

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						"""token-classification"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          if type(_SCREAMING_SNAKE_CASE   ) == dict:
               _snake_case							=				Namespace(**_SCREAMING_SNAKE_CASE   )
          _snake_case							=				import_module("tasks"   )
          try:
               _snake_case							=				getattr(_SCREAMING_SNAKE_CASE ,hparams.task_type   )
               _snake_case							=				token_classification_task_clazz()
          except AttributeError:
               raise ValueError(
                   f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
                   f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""   )
          _snake_case							=				self.token_classification_task.get_labels(hparams.labels   )
          _snake_case							=				CrossEntropyLoss().ignore_index
          super().__init__(_SCREAMING_SNAKE_CASE ,len(self.labels   ) ,self.mode   )






     def     _lowercase     (			self ,**_SCREAMING_SNAKE_CASE   )							->      Tuple:
          return self.model(**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          _snake_case							=				{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
          if self.config.model_type != "distilbert":
               _snake_case							=				(
                   batch[2] if self.config.model_type in ["bert", "xlnet"] else None
               )  # XLM and RoBERTa don"t use token_type_ids

          _snake_case							=				self(**_SCREAMING_SNAKE_CASE   )
          _snake_case							=				outputs[0]
          # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
          return {"loss": loss}






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.hparams
          for mode in ["train", "dev", "test"]:
               _snake_case							=				self._feature_file(_SCREAMING_SNAKE_CASE   )
               if os.path.exists(_SCREAMING_SNAKE_CASE   ) and not args.overwrite_cache:
                    logger.info("Loading features from cached file %s" ,_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				torch.load(_SCREAMING_SNAKE_CASE   )
               else:
                    logger.info("Creating features from dataset file at %s" ,args.data_dir   )
                    _snake_case							=				self.token_classification_task.read_examples_from_file(args.data_dir ,_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				self.token_classification_task.convert_examples_to_features(
                        _SCREAMING_SNAKE_CASE ,self.labels ,args.max_seq_length ,self.tokenizer ,cls_token_at_end=bool(self.config.model_type in ["xlnet"]   ) ,cls_token=self.tokenizer.cls_token ,cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0 ,sep_token=self.tokenizer.sep_token ,sep_token_extra=_SCREAMING_SNAKE_CASE ,pad_on_left=bool(self.config.model_type in ["xlnet"]   ) ,pad_token=self.tokenizer.pad_token_id ,pad_token_segment_id=self.tokenizer.pad_token_type_id ,pad_token_label_id=self.pad_token_label_id ,)
                    logger.info("Saving features into cached file %s" ,_SCREAMING_SNAKE_CASE   )
                    torch.save(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False   )							->      DataLoader:
          _snake_case							=				self._feature_file(_SCREAMING_SNAKE_CASE   )
          logger.info("Loading features from cached file %s" ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.load(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.tensor([f.input_ids for f in features] ,dtype=torch.long   )
          _snake_case							=				torch.tensor([f.attention_mask for f in features] ,dtype=torch.long   )
          if features[0].token_type_ids is not None:
               _snake_case							=				torch.tensor([f.token_type_ids for f in features] ,dtype=torch.long   )
          else:
               _snake_case							=				torch.tensor([0 for f in features] ,dtype=torch.long   )
               # HACK(we will not use this anymore soon)
          _snake_case							=				torch.tensor([f.label_ids for f in features] ,dtype=torch.long   )
          return DataLoader(
              TensorDataset(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) ,batch_size=_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Any:
          """Compute validation""" ""
          _snake_case							=				{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
          if self.config.model_type != "distilbert":
               _snake_case							=				(
                   batch[2] if self.config.model_type in ["bert", "xlnet"] else None
               )  # XLM and RoBERTa don"t use token_type_ids
          _snake_case							=				self(**_SCREAMING_SNAKE_CASE   )
          _snake_case							, _snake_case							=				outputs[:2]
          _snake_case							=				logits.detach().cpu().numpy()
          _snake_case							=				inputs["labels"].detach().cpu().numpy()
          return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      int:
          _snake_case							=				torch.stack([x["val_loss"] for x in outputs]   ).mean()
          _snake_case							=				np.concatenate([x["pred"] for x in outputs] ,axis=0   )
          _snake_case							=				np.argmax(_SCREAMING_SNAKE_CASE ,axis=2   )
          _snake_case							=				np.concatenate([x["target"] for x in outputs] ,axis=0   )

          _snake_case							=				dict(enumerate(self.labels   )   )
          _snake_case							=				[[] for _ in range(out_label_ids.shape[0]   )]
          _snake_case							=				[[] for _ in range(out_label_ids.shape[0]   )]

          for i in range(out_label_ids.shape[0]   ):
               for j in range(out_label_ids.shape[1]   ):
                    if out_label_ids[i, j] != self.pad_token_label_id:
                         out_label_list[i].append(label_map[out_label_ids[i][j]]   )
                         preds_list[i].append(label_map[preds[i][j]]   )

          _snake_case							=				{
              "val_loss": val_loss_mean,
              "accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ),
              "precision": precision_score(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ),
              "recall": recall_score(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ),
              "f1": fa_score(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ),
          }

          _snake_case							=				dict(results.items()   )
          _snake_case							=				results
          return ret, preds_list, out_label_list






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      int:
          # when stable
          _snake_case							, _snake_case							, _snake_case							=				self._eval_end(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				ret["log"]
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[Any]:
          # updating to test_epoch_end instead of deprecated test_end
          _snake_case							, _snake_case							, _snake_case							=				self._eval_end(_SCREAMING_SNAKE_CASE   )

          # Converting to the dict required by pl
          # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
          # pytorch_lightning/trainer/logging.py#L139
          _snake_case							=				ret["log"]
          # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
          return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






     @staticmethod
     def     _lowercase     (			_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          # Add NER specific options
          BaseTransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          parser.add_argument(
              "--task_type" ,default="NER" ,type=_SCREAMING_SNAKE_CASE ,help="Task type to fine tune in training (e.g. NER, POS, etc)"   )
          parser.add_argument(
              "--max_seq_length" ,default=128 ,type=_SCREAMING_SNAKE_CASE ,help=(
                  "The maximum total input sequence length after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              ) ,)

          parser.add_argument(
              "--labels" ,default="" ,type=_SCREAMING_SNAKE_CASE ,help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used." ,)
          parser.add_argument(
              "--gpus" ,default=0 ,type=_SCREAMING_SNAKE_CASE ,help="The number of GPUs allocated for this, it is by default 0 meaning none" ,)

          parser.add_argument(
              "--overwrite_cache" ,action="store_true" ,help="Overwrite the cached training and evaluation sets"   )

          return parser


if __name__ == "__main__":
   UpperCamelCase_						:      List[Any]       =						argparse.ArgumentParser()
   add_generic_args(parser, os.getcwd())
   UpperCamelCase_						:      Optional[Any]       =						NERTransformer.add_model_specific_args(parser, os.getcwd())
   UpperCamelCase_						:      Optional[int]       =						parser.parse_args()
   UpperCamelCase_						:      Optional[Any]       =						NERTransformer(args)
   UpperCamelCase_						:      Union[str, Any]       =						generic_train(model, args)

   if args.do_predict:
      # See https://github.com/huggingface/transformers/issues/3159
      # pl use this default format to create a checkpoint:
      # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
      # /pytorch_lightning/callbacks/model_checkpoint.py#L322
      UpperCamelCase_						:      Dict       =						sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
      UpperCamelCase_						:      Tuple       =						model.load_from_checkpoint(checkpoints[-1])
      trainer.test(model)








'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def      __a     (       _UpperCamelCase:   Tuple   )						->			Union[str, Any]:

     """simple docstring"""




     _snake_case							=				os.path.join(args.tf_model_dir ,						"parameters.json"   )
     _snake_case							=				json.loads(open(_UpperCamelCase   ).read()   )
     if not params:
          raise ValueError(
              F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""   )
     if not args.output.endswith(".pt"   ):
          _snake_case							=				args.output + ".pt"
     _snake_case							=				OrderedDict()
     with tf.device("/CPU:0"   ):
          _snake_case							=				tf.train.load_checkpoint(args.tf_model_dir   )
          _snake_case							=				reader.get_variable_to_shape_map()
          for key_name in shapes.keys():
               _snake_case							=				reader.get_tensor(_UpperCamelCase   ).astype(np.floataa   )
               if key_name.endswith("/adam_m"   ) or key_name.endswith("/adam_v"   ):
                    continue
               if key_name.startswith("pasts/"   ):
                    if key_name.startswith("pasts/mlp"   ):
                         _snake_case							=				int(key_name[9]   )
                    elif key_name.startswith("pasts/out"   ):
                         _snake_case							=				8
                    _snake_case							=				"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                    _snake_case							=				vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    _snake_case							=				torch.tensor(_UpperCamelCase   )
               elif key_name.startswith("model/moe"   ):
                    _snake_case							=				int(key_name[9:].split("/"   )[0]   )
                    if key_name.endswith("/switch_gating/kernel"   ):
                         _snake_case							=				"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
                         _snake_case							=				vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                    elif key_name.endswith("/softmlp/kernel"   ):
                         _snake_case							=				"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
                         _snake_case							=				vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                    elif key_name.endswith("/wo/kernel"   ) or key_name.endswith("/wi/kernel"   ):
                         _snake_case							=				key_name[-9:-7]
                         for i in range(16   ):
                              _snake_case							=				"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
                              _snake_case							=				(
                                  vnp[i].transpose([1, 0]   ).copy()
                              )  # In Mesh-Tensorflow, it is one array, so it is divided
                              _snake_case							=				torch.tensor(_UpperCamelCase   )
               elif key_name.startswith("model/mlp"   ):
                    _snake_case							=				int(key_name[9:].split("/"   )[0]   )
                    if key_name.endswith("/p1/kernel"   ):
                         _snake_case							=				"model.blocks.%d.feed_forward.mlp.wi.weight" % player
                         _snake_case							=				vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                    elif key_name.endswith("/p1/bias"   ):
                         _snake_case							=				"model.blocks.%d.feed_forward.mlp.wi.bias" % player
                         _snake_case							=				vnp.copy()  # same because it is one dimensional
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                    elif key_name.endswith("/p2/kernel"   ):
                         _snake_case							=				"model.blocks.%d.feed_forward.mlp.wo.weight" % player
                         _snake_case							=				vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                    elif key_name.endswith("/p2/bias"   ):
                         _snake_case							=				"model.blocks.%d.feed_forward.mlp.wo.bias" % player
                         _snake_case							=				vnp.copy()  # same because it is one dimensional
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
               elif key_name.startswith("model/ln"   ):
                    _snake_case							=				int(key_name[8:].split("/"   )[0]   )
                    if key_name.endswith("/b"   ):
                         _snake_case							=				"model.blocks.%d.feed_forward.norm.bias" % player
                         _snake_case							=				vnp.copy()  # same because it is one dimensional
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                    elif key_name.endswith("/g"   ):
                         _snake_case							=				"model.blocks.%d.feed_forward.norm.weight" % player
                         _snake_case							=				vnp.copy()  # same because it is one dimensional
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
               elif key_name.startswith("model/att"   ):
                    _snake_case							=				int(key_name[9:].split("/"   )[0]   )
                    if key_name.endswith("/qkv/kernel"   ):
                         _snake_case							=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                         _snake_case							=				state[:, 0, :, :]
                         _snake_case							=				state[:, 1, :, :]
                         _snake_case							=				state[:, 2, :, :]
                         _snake_case							=				(
                             state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]   )
                             .transpose([1, 0]   )
                             .copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         _snake_case							=				(
                             state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]   )
                             .transpose([1, 0]   )
                             .copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         _snake_case							=				(
                             state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]   )
                             .transpose([1, 0]   )
                             .copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         _snake_case							=				"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                         _snake_case							=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                         _snake_case							=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                    elif key_name.endswith("/o/kernel"   ):
                         _snake_case							=				"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
                         _snake_case							=				(
                             vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]   ).transpose([1, 0]   ).copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
               elif key_name.startswith("model/an"   ):
                    _snake_case							=				int(key_name[8:].split("/"   )[0]   )
                    if key_name.endswith("/b"   ):
                         _snake_case							=				"model.blocks.%d.self_attn.norm.bias" % player
                         _snake_case							=				vnp.copy()  # same because it is one dimensional
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
                    elif key_name.endswith("/g"   ):
                         _snake_case							=				"model.blocks.%d.self_attn.norm.weight" % player
                         _snake_case							=				vnp.copy()  # same because it is one dimensional
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
               elif (
                   key_name.startswith("model/wte"   )
                   or key_name.startswith("model/wpe"   )
                   or key_name.startswith("model/ete"   )
               ):
                    _snake_case							=				{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
                        key_name[-3:]
                    ]
                    _snake_case							=				"model.%s.weight" % nlayer
                    _snake_case							=				vnp.copy()  # same in embedded
                    _snake_case							=				torch.tensor(_UpperCamelCase   )
                    if key_name.startswith("model/wte"   ):
                         _snake_case							=				"lm_head.weight"
                         _snake_case							=				vnp.copy()  # same in embedded
                         _snake_case							=				torch.tensor(_UpperCamelCase   )
               elif key_name.startswith("model/wob"   ):
                    _snake_case							=				"final_logits_bias"
                    _snake_case							=				vnp.copy()  # same in embedded
                    _snake_case							=				state.reshape((1, -1)   )
                    _snake_case							=				torch.tensor(_UpperCamelCase   )
               elif key_name == "model/dense/kernel":
                    _snake_case							=				"model.last_project.weight"
                    _snake_case							=				vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    _snake_case							=				torch.tensor(_UpperCamelCase   )
               elif key_name == "model/dense_1/bias":
                    _snake_case							=				"model.last_project.bias"
                    _snake_case							=				vnp.copy()  # same because it is one dimensional
                    _snake_case							=				torch.tensor(_UpperCamelCase   )
     torch.save(_UpperCamelCase ,						args.output   )


if __name__ == "__main__":
   UpperCamelCase_						:      Tuple       =						argparse.ArgumentParser(
       description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
   )
   parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
   parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
   UpperCamelCase_						:      Any       =						parser.parse_args()
   convert_tf_gptsan_to_pt(args)


