

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
SCREAMING_SNAKE_CASE_            = False
SCREAMING_SNAKE_CASE_            = False

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Namespace	)				->    int:
				return TrainCommand(lowerCAmelCase	)





class 			a					(		UpperCAmelCase			):


						@staticmethod
						def     _UpperCAmelCase			(    A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      parser.add_parser("train"  ,   help="CLI tool to train a model on a task."		)

										train_parser.add_argument(
										    "--train_data"  ,   type=A_  ,   required=A_  ,   help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."  ,   )
										train_parser.add_argument(
										    "--column_label"  ,   type=A_  ,   default=0  ,   help="Column of the dataset csv file with example labels."		)
										train_parser.add_argument(
										    "--column_text"  ,   type=A_  ,   default=1  ,   help="Column of the dataset csv file with example texts."		)
										train_parser.add_argument(
										    "--column_id"  ,   type=A_  ,   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=A_  ,   default=""  ,   help="path to validation dataset."		)
										train_parser.add_argument(
										    "--validation_split"  ,   type=A_  ,   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=A_  ,   default="./"  ,   help="path to saved the trained model."		)

										train_parser.add_argument(
										    "--task"  ,   type=A_  ,   default="text_classification"  ,   help="Task to train the model on."		)
										train_parser.add_argument(
										    "--model"  ,   type=A_  ,   default="bert-base-uncased"  ,   help="Model's name or path to stored model."		)
										train_parser.add_argument("--train_batch_size"  ,   type=A_  ,   default=32  ,   help="Batch size for training."		)
										train_parser.add_argument("--valid_batch_size"  ,   type=A_  ,   default=64  ,   help="Batch size for validation."		)
										train_parser.add_argument("--learning_rate"  ,   type=A_  ,   default=3e-5  ,   help="Learning rate."		)
										train_parser.add_argument("--adam_epsilon"  ,   type=A_  ,   default=1e-08  ,   help="Epsilon for Adam optimizer."		)
										train_parser.set_defaults(func=A_		)


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      logging.get_logger("transformers-cli/training"		)

										_UpperCAmelCase    :						Union[str, Any]							=      "tf" if is_tf_available() else "torch"

										os.makedirs(args.output  ,   exist_ok=A_		)
										_UpperCAmelCase    :						Dict							=      args.output

										_UpperCAmelCase    :						Dict							=      args.column_label
										_UpperCAmelCase    :						Any							=      args.column_text
										_UpperCAmelCase    :						List[str]							=      args.column_id

										self.logger.info(f'Loading {args.task} pipeline for {args.model}'		)
										if args.task == "text_classification":
														_UpperCAmelCase    :						Dict							=      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}'		)
										_UpperCAmelCase    :						int							=      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  ,   )
										_UpperCAmelCase    :						Union[str, Any]							=      None
										if args.validation_data:
														self.logger.info(f'Loading validation dataset from {args.validation_data}'		)
														_UpperCAmelCase    :						List[str]							=      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  ,   )

										_UpperCAmelCase    :						Any							=      args.validation_split
										_UpperCAmelCase    :						int							=      args.train_batch_size
										_UpperCAmelCase    :						int							=      args.valid_batch_size
										_UpperCAmelCase    :						Dict							=      args.learning_rate
										_UpperCAmelCase    :						Optional[Any]							=      args.adam_epsilon


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if self.framework == "tf":
														return self.run_tf()
										return self.run_torch()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										raise NotImplementedError





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										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		)





import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_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    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      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__":
					SCREAMING_SNAKE_CASE_            = 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'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


