


'''simple docstring'''



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def      _a     (						_lowercase :     int      , _lowercase :     Optional[Any]      , _lowercase :     Tuple      , _lowercase :     Dict      , _lowercase :     Dict      , _lowercase :     List[Any] = None      , ):





     '''simple docstring'''






     __UpperCAmelCase     :			int  							=      {}

     if train_file is not None:
          __UpperCAmelCase     :			Dict  							=      [train_file]
     if eval_file is not None:
          __UpperCAmelCase     :			Union[str, Any]  							=      [eval_file]
     if test_file is not None:
          __UpperCAmelCase     :			Optional[Any]  							=      [test_file]

     __UpperCAmelCase     :			int  							=      datasets.load_dataset('''csv'''      , data_files=__snake_case  )
     __UpperCAmelCase     :			Tuple  							=      list(ds[list(files.keys()  )[0]].features.keys()  )
     __UpperCAmelCase     :			str  							=      features_name.pop(__snake_case  )
     __UpperCAmelCase     :			Union[str, Any]  							=      list(set(ds[list(files.keys()  )[0]][label_name]  )  )
     __UpperCAmelCase     :			Dict  							=      {label: i for i, label in enumerate(__snake_case  )}
     __UpperCAmelCase     :			Any  							=      tokenizer.model_input_names
     __UpperCAmelCase     :			Union[str, Any]  							=      {}

     if len(__snake_case  ) == 1:
          for k in files.keys():
               __UpperCAmelCase     :			List[Any]  							=      ds[k].map(
                   lambda _lowercase  : tokenizer.batch_encode_plus(
                       example[features_name[0]]      , truncation=__snake_case      , max_length=__snake_case      , padding='''max_length'''  )      , batched=__snake_case      , )
     elif len(__snake_case  ) == 2:
          for k in files.keys():
               __UpperCAmelCase     :			Dict  							=      ds[k].map(
                   lambda _lowercase  : tokenizer.batch_encode_plus(
                       (example[features_name[0]], example[features_name[1]])      , truncation=__snake_case      , max_length=__snake_case      , padding='''max_length'''      , )      , batched=__snake_case      , )

     def gen_train():
          for ex in transformed_ds[datasets.Split.TRAIN]:
               __UpperCAmelCase     :			Any  							=      {k: v for k, v in ex.items() if k in input_names}
               __UpperCAmelCase     :			str  							=      labelaid[ex[label_name]]
               yield (d, label)

     def gen_val():
          for ex in transformed_ds[datasets.Split.VALIDATION]:
               __UpperCAmelCase     :			str  							=      {k: v for k, v in ex.items() if k in input_names}
               __UpperCAmelCase     :			Any  							=      labelaid[ex[label_name]]
               yield (d, label)

     def gen_test():
          for ex in transformed_ds[datasets.Split.TEST]:
               __UpperCAmelCase     :			Union[str, Any]  							=      {k: v for k, v in ex.items() if k in input_names}
               __UpperCAmelCase     :			Optional[Any]  							=      labelaid[ex[label_name]]
               yield (d, label)

     __UpperCAmelCase     :			str  							=      (
         tf.data.Dataset.from_generator(
             __snake_case      , ({k: tf.intaa for k in input_names}, tf.intaa)      , ({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))      , )
         if datasets.Split.TRAIN in transformed_ds
         else None
     )

     if train_ds is not None:
          __UpperCAmelCase     :			Optional[Any]  							=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]  )  )  )

     __UpperCAmelCase     :			int  							=      (
         tf.data.Dataset.from_generator(
             __snake_case      , ({k: tf.intaa for k in input_names}, tf.intaa)      , ({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))      , )
         if datasets.Split.VALIDATION in transformed_ds
         else None
     )

     if val_ds is not None:
          __UpperCAmelCase     :			Optional[int]  							=      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]  )  )  )

     __UpperCAmelCase     :			Tuple  							=      (
         tf.data.Dataset.from_generator(
             __snake_case      , ({k: tf.intaa for k in input_names}, tf.intaa)      , ({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))      , )
         if datasets.Split.TEST in transformed_ds
         else None
     )

     if test_ds is not None:
          __UpperCAmelCase     :			Tuple  							=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]  )  )  )

     return train_ds, val_ds, test_ds, labelaid


__UpperCAmelCase      :str			    =       logging.getLogger(__name__)



@dataclass
class      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       field(metadata={"help": "Which column contains the label"}	)
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       field(default=__SCREAMING_SNAKE_CASE     ,  metadata={"help": "The path of the training file"}	)
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       field(default=__SCREAMING_SNAKE_CASE     ,  metadata={"help": "The path of the development file"}	)
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       field(default=__SCREAMING_SNAKE_CASE     ,  metadata={"help": "The path of the test file"}	)
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       field(
         default=1_2_8     ,  metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,  )
     SCREAMING_SNAKE_CASE	:			Tuple							       =       field(
         default=__SCREAMING_SNAKE_CASE     ,  metadata={"help": "Overwrite the cached training and evaluation sets"}	)



@dataclass
class      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       field(
         default=__SCREAMING_SNAKE_CASE     ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
     SCREAMING_SNAKE_CASE	:			Dict							       =       field(
         default=__SCREAMING_SNAKE_CASE     ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       field(default=__SCREAMING_SNAKE_CASE     ,  metadata={"help": "Set this flag to use fast tokenization."}	)
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       field(
         default=__SCREAMING_SNAKE_CASE     ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,  )




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			List[Any]  							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
     __UpperCAmelCase     :			Dict  							=      parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir  )
         and os.listdir(training_args.output_dir  )
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
              ''' --overwrite_output_dir to overcome.'''  )

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''      , datefmt='''%m/%d/%Y %H:%M:%S'''      , level=logging.INFO      , )
     logger.info(
         F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1  )}, '
         F'16-bits training: {training_args.fpaa}'  )
     logger.info(F'Training/evaluation parameters {training_args}'  )

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     __UpperCAmelCase     :			Union[str, Any]  							=      AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      , cache_dir=model_args.cache_dir      , )

     __UpperCAmelCase     :			Union[str, Any]  							=      get_tfds(
         train_file=data_args.train_file      , eval_file=data_args.dev_file      , test_file=data_args.test_file      , tokenizer=__snake_case      , label_column_id=data_args.label_column_id      , max_seq_length=data_args.max_seq_length      , )

     __UpperCAmelCase     :			Optional[int]  							=      AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path      , num_labels=len(__snake_case  )      , labelaid=__snake_case      , idalabel={id: label for label, id in labelaid.items()}      , finetuning_task='''text-classification'''      , cache_dir=model_args.cache_dir      , )

     with training_args.strategy.scope():
          __UpperCAmelCase     :			Optional[int]  							=      TFAutoModelForSequenceClassification.from_pretrained(
              model_args.model_name_or_path      , from_pt=bool('''.bin''' in model_args.model_name_or_path  )      , config=__snake_case      , cache_dir=model_args.cache_dir      , )

     def compute_metrics(_lowercase :     Optional[int]  ) -> Dict:
          __UpperCAmelCase     :			str  							=      np.argmax(p.predictions      , axis=1  )

          return {"acc": (preds == p.label_ids).mean()}

     # Initialize our Trainer
     __UpperCAmelCase     :			str  							=      TFTrainer(
         model=__snake_case      , args=__snake_case      , train_dataset=__snake_case      , eval_dataset=__snake_case      , compute_metrics=__snake_case      , )

     # Training
     if training_args.do_train:
          trainer.train()
          trainer.save_model()
          tokenizer.save_pretrained(training_args.output_dir  )

     # Evaluation
     __UpperCAmelCase     :			str  							=      {}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''  )
          __UpperCAmelCase     :			int  							=      trainer.evaluate()
          __UpperCAmelCase     :			List[str]  							=      os.path.join(training_args.output_dir      , '''eval_results.txt'''  )

          with open(__snake_case      , '''w'''  ) as writer:
               logger.info('''***** Eval results *****'''  )

               for key, value in result.items():
                    logger.info(F'  {key} = {value}'  )
                    writer.write(F'{key} = {value}\n'  )

               results.update(__snake_case  )

     return results


if __name__ == "__main__":
      main()



'''simple docstring'''



import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase      :Any			    =       logging.get_logger(__name__)

__UpperCAmelCase      :Dict			    =       [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]




def      _a     (						_lowercase :     Tuple  ):





     '''simple docstring'''






     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __UpperCAmelCase     :			Any  							=      k.replace(_lowercase      , _lowercase  )

     if k.startswith('''encoder'''  ):
          __UpperCAmelCase     :			str  							=      k.replace('''.attn'''      , '''.self_attn'''  )
          __UpperCAmelCase     :			Any  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[str]  							=      k.replace('''norm2'''      , '''final_layer_norm'''  )
     elif k.startswith('''decoder'''  ):
          __UpperCAmelCase     :			int  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			Union[str, Any]  							=      k.replace('''norm2'''      , '''encoder_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[Any]  							=      k.replace('''norm3'''      , '''final_layer_norm'''  )
     return k




def      _a     (						_lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			int  							=      [
         '''model.encoder.layernorm_embedding.weight''',
         '''model.encoder.layernorm_embedding.bias''',
         '''model.decoder.layernorm_embedding.weight''',
         '''model.decoder.layernorm_embedding.bias''',
     ]
     for k in keys:
          __UpperCAmelCase     :			Any  							=      sd.pop(_lowercase  )
          __UpperCAmelCase     :			Optional[int]  							=      k.replace('''layernorm_embedding'''      , '''layer_norm'''  )
          assert new_k not in sd
          __UpperCAmelCase     :			List[str]  							=      v


__UpperCAmelCase      :str			    =       ["START"]




@torch.no_grad()
def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Optional[int]      , _lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Any  							=      torch.load(_lowercase      , map_location='''cpu'''  )
     __UpperCAmelCase     :			List[str]  							=      model['''model''']
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotConfig.from_json_file(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotForConditionalGeneration(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      m.model.state_dict().keys()
     __UpperCAmelCase     :			int  							=      []
     __UpperCAmelCase     :			List[str]  							=      {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          __UpperCAmelCase     :			int  							=      rename_state_dict_key(_lowercase  )
          if new_k not in valid_keys:
               failures.append([k, new_k]  )
          else:
               __UpperCAmelCase     :			Union[str, Any]  							=      v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(_lowercase  )
     m.model.load_state_dict(_lowercase      , strict=_lowercase  )
     m.half()
     m.save_pretrained(_lowercase  )


if __name__ == "__main__":
      __UpperCAmelCase      :Optional[int]			    =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
      parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
      parser.add_argument(
          "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
      )
      __UpperCAmelCase      :Tuple			    =       parser.parse_args()
      convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)