





'''simple docstring'''

import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
  raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
__lowerCAmelCase							:     Optional[int]		     =False
__lowerCAmelCase							:     Tuple		     =False





def 						UpperCamelCase    (							_lowerCamelCase : Namespace	):
    return TrainCommand(_lowerCamelCase	)






class        UpperCAmelCase     (	UpperCamelCase__    ):
 @staticmethod
 def 					UpperCAmelCase_							(    lowercase_				:ArgumentParser							)->							str:
     A__    =      parser.add_parser("train"						,	help="CLI tool to train a model on a task."							)

     train_parser.add_argument(
         "--train_data"						,	type=lowercase_						,	required=lowercase_						,	help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."						,	)
     train_parser.add_argument(
         "--column_label"						,	type=lowercase_						,	default=0						,	help="Column of the dataset csv file with example labels."							)
     train_parser.add_argument(
         "--column_text"						,	type=lowercase_						,	default=1						,	help="Column of the dataset csv file with example texts."							)
     train_parser.add_argument(
         "--column_id"						,	type=lowercase_						,	default=2						,	help="Column of the dataset csv file with example ids."							)
     train_parser.add_argument(
         "--skip_first_row"						,	action="store_true"						,	help="Skip the first row of the csv file (headers)."							)

     train_parser.add_argument("--validation_data"						,	type=lowercase_						,	default=""						,	help="path to validation dataset."							)
     train_parser.add_argument(
         "--validation_split"						,	type=lowercase_						,	default=0.1						,	help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."						,	)

     train_parser.add_argument("--output"						,	type=lowercase_						,	default="./"						,	help="path to saved the trained model."							)

     train_parser.add_argument(
         "--task"						,	type=lowercase_						,	default="text_classification"						,	help="Task to train the model on."							)
     train_parser.add_argument(
         "--model"						,	type=lowercase_						,	default="bert-base-uncased"						,	help="Model's name or path to stored model."							)
     train_parser.add_argument("--train_batch_size"						,	type=lowercase_						,	default=32						,	help="Batch size for training."							)
     train_parser.add_argument("--valid_batch_size"						,	type=lowercase_						,	default=64						,	help="Batch size for validation."							)
     train_parser.add_argument("--learning_rate"						,	type=lowercase_						,	default=3E-5						,	help="Learning rate."							)
     train_parser.add_argument("--adam_epsilon"						,	type=lowercase_						,	default=1E-08						,	help="Epsilon for Adam optimizer."							)
     train_parser.set_defaults(func=lowercase_							)
 def __init__(    self				:List[str]						,	lowercase_				:Namespace							)->							Dict:
     A__    =      logging.get_logger("transformers-cli/training"							)

     A__    =      "tf" if is_tf_available() else "torch"

     os.makedirs(args.output						,	exist_ok=lowercase_							)
     A__    =      args.output

     A__    =      args.column_label
     A__    =      args.column_text
     A__    =      args.column_id

     self.logger.info(F"Loading {args.task} pipeline for {args.model}"							)
     if args.task == "text_classification":
         A__    =      TextClassificationPipeline.from_pretrained(args.model							)
     elif args.task == "token_classification":
         raise NotImplementedError
     elif args.task == "question_answering":
         raise NotImplementedError

     self.logger.info(F"Loading dataset from {args.train_data}"							)
     A__    =      Processor.create_from_csv(
         args.train_data						,	column_label=args.column_label						,	column_text=args.column_text						,	column_id=args.column_id						,	skip_first_row=args.skip_first_row						,	)
     A__    =      None
     if args.validation_data:
         self.logger.info(F"Loading validation dataset from {args.validation_data}"							)
         A__    =      Processor.create_from_csv(
             args.validation_data						,	column_label=args.column_label						,	column_text=args.column_text						,	column_id=args.column_id						,	skip_first_row=args.skip_first_row						,	)

     A__    =      args.validation_split
     A__    =      args.train_batch_size
     A__    =      args.valid_batch_size
     A__    =      args.learning_rate
     A__    =      args.adam_epsilon
 def 					UpperCAmelCase_							(    self				:Dict							)->							Union[str, Any]:
     if self.framework == "tf":
         return self.run_tf()
     return self.run_torch()
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Any:
     raise NotImplementedError



 def 					UpperCAmelCase_							(    self				:int							)->							List[Any]:
     self.pipeline.fit(
         self.train_dataset						,	validation_data=self.valid_dataset						,	validation_split=self.validation_split						,	learning_rate=self.learning_rate						,	adam_epsilon=self.adam_epsilon						,	train_batch_size=self.train_batch_size						,	valid_batch_size=self.valid_batch_size						,	)

     # Save trained pipeline
     self.pipeline.save_pretrained(self.output							)






'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase							:     Union[str, Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Optional[int]		     =[
    ["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 						UpperCamelCase    (							_lowerCamelCase : int	):
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A__    =      k.replace(_lowerCamelCase						,	_lowerCamelCase	)

    if k.startswith("encoder"	):
        A__    =      k.replace(".attn"						,	".self_attn"	)
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"final_layer_norm"	)
    elif k.startswith("decoder"	):
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"encoder_attn_layer_norm"	)
        A__    =      k.replace("norm3"						,	"final_layer_norm"	)
    return k





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      [
        "model.encoder.layernorm_embedding.weight",
        "model.encoder.layernorm_embedding.bias",
        "model.decoder.layernorm_embedding.weight",
        "model.decoder.layernorm_embedding.bias",
    ]
    for k in keys:
        A__    =      sd.pop(_lowerCamelCase	)
        A__    =      k.replace("layernorm_embedding"						,	"layer_norm"	)
        assert new_k not in sd
        A__    =      v


__lowerCAmelCase							:     Optional[int]		     =["START"]





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    A__    =      model["model"]
    A__    =      BlenderbotConfig.from_json_file(_lowerCamelCase	)
    A__    =      BlenderbotForConditionalGeneration(_lowerCamelCase	)
    A__    =      m.model.state_dict().keys()
    A__    =      []
    A__    =      {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

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


if __name__ == "__main__":
  __lowerCAmelCase							:     Union[str, Any]		     =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"
  )
  __lowerCAmelCase							:     Union[str, Any]		     =parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



