




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
__A        =    False
__A        =    False







def 			lowerCamelCase_		(       UpperCamelCase__				:      Namespace      )     ->       Optional[int]:





				"""simple docstring"""


				return TrainCommand(UpperCamelCase__      )







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			@staticmethod
			def       lowercase_    (      lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    parser.add_parser('train'			,						help='CLI tool to train a model on a task.'							)

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

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


			def __init__(      self			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    logging.get_logger('transformers-cli/training'							)

							__lowerCamelCase		       =    'tf' if is_tf_available() else 'torch'

							os.makedirs(args.output			,						exist_ok=lowerCamelCase__							)
							__lowerCamelCase		       =    args.output

							__lowerCamelCase		       =    args.column_label
							__lowerCamelCase		       =    args.column_text
							__lowerCamelCase		       =    args.column_id

							self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""							)
							if args.task == "text_classification":
											__lowerCamelCase		       =    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}"""							)
							__lowerCamelCase		       =    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			,						)
							__lowerCamelCase		       =    None
							if args.validation_data:
											self.logger.info(f"""Loading validation dataset from {args.validation_data}"""							)
											__lowerCamelCase		       =    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			,						)

							__lowerCamelCase		       =    args.validation_split
							__lowerCamelCase		       =    args.train_batch_size
							__lowerCamelCase		       =    args.valid_batch_size
							__lowerCamelCase		       =    args.learning_rate
							__lowerCamelCase		       =    args.adam_epsilon


			def       lowercase_    (      self							)    ->      int:



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


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							raise NotImplementedError






			def       lowercase_    (      self							)    ->      List[str]:



							'''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							)



'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[str]=2      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=3      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=64      ,       SCREAMING_SNAKE_CASE__   :	int=None							)  ->    Optional[int]:
  __lowerCAmelCase											=						np.random.default_rng(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						length
  __lowerCAmelCase											=						rng.normal(size=(length,)							).astype(np.floataa							)
  __lowerCAmelCase											=						a * self.x + b + rng.normal(scale=0.1      ,       size=(length,)							).astype(np.floataa							)
 def __len__(      self   :	Union[str, Any]							)  ->    Optional[Any]:
  return self.length





 def __getitem__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Optional[int]:
  return {"x": self.x[i], "y": self.y[i]}







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=False							)  ->    Any:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None							)  ->    str:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a[0] + self.b[0]







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=False							)  ->    Optional[Any]:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Any=None							)  ->    int:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a + self.b







def      UpperCamelCase_						(					snake_case_       :       List[str]						,		snake_case_       :       int = 16      )					->     int:






 '''simple docstring'''

 from datasets import load_dataset
 from transformers import AutoTokenizer

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""bert-base-cased"""      )
 __lowerCAmelCase											=						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
 __lowerCAmelCase											=						load_dataset("""csv"""						,		data_files=snake_case_      )
 __lowerCAmelCase											=						datasets["""train"""].unique("""label"""      )

 __lowerCAmelCase											=						{v: i for i, v in enumerate(snake_case_      )}

 def tokenize_function(snake_case_       :       Optional[int]      ):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase											=						tokenizer(
      examples["""sentence1"""]						,		examples["""sentence2"""]						,		truncation=snake_case_						,		max_length=snake_case_						,		padding="""max_length"""      )
  if "label" in examples:
   __lowerCAmelCase											=						[label_to_id[l] for l in examples["""label"""]]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __lowerCAmelCase											=						datasets.map(
     snake_case_						,		batched=snake_case_						,		remove_columns=["""sentence1""", """sentence2""", """label"""]						,		)

 def collate_fn(snake_case_       :       List[Any]      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(snake_case_						,		padding="""max_length"""						,		max_length=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""      )

 # Instantiate dataloaders.
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""train"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=2      )
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""validation"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=1      )

 return train_dataloader, eval_dataloader






