





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
_UpperCAmelCase				:       List[str]	   =				False
_UpperCAmelCase				:       Dict	   =				False


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     int:
    return TrainCommand(_UpperCAmelCase )


class    lowerCAmelCase							(					__UpperCamelCase			):
 @staticmethod
 def      A_	(      UpperCAmelCase  :      ArgumentParser	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[int]					 =		parser.add_parser('train'			,     help='CLI tool to train a model on a task.'	)

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

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

     lowerCamelCase__    :      Optional[int]					 =		'tf' if is_tf_available() else 'torch'

     os.makedirs(args.output			,     exist_ok=UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		args.output

     lowerCamelCase__    :      List[str]					 =		args.column_label
     lowerCamelCase__    :      int					 =		args.column_text
     lowerCamelCase__    :      Any					 =		args.column_id

     self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""	)
     if args.task == "text_classification":
         lowerCamelCase__    :      Optional[int]					 =		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__    :      Optional[Any]					 =		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__    :      List[str]					 =		None
     if args.validation_data:
         self.logger.info(F"""Loading validation dataset from {args.validation_data}"""	)
         lowerCamelCase__    :      Union[str, Any]					 =		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__    :      Optional[Any]					 =		args.validation_split
     lowerCamelCase__    :      Any					 =		args.train_batch_size
     lowerCamelCase__    :      Any					 =		args.valid_batch_size
     lowerCamelCase__    :      Dict					 =		args.learning_rate
     lowerCamelCase__    :      Union[str, Any]					 =		args.adam_epsilon
 def      A_	(      self  :      Optional[Any]	)			->	Optional[int]:
     if self.framework == "tf":
         return self.run_tf()
     return self.run_torch()
 def      A_	(      self  :      int	)			->	Optional[Any]:
     raise NotImplementedError







 def      A_	(      self  :      str	)			->	str:
     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 flax.linen as nn
import jax
import jax.numpy as jnp

class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      Any	)			->	Any:
     lowerCamelCase__    :      str					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      int			,     UpperCAmelCase  :      Dict	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		hidden_states.shape
     lowerCamelCase__    :      Union[str, Any]					 =		jax.image.resize(
         UpperCAmelCase			,     shape=(batch, height * 2, width * 2, channels)			,     method='nearest'			,     )
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(2, 2)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]	)			->	Optional[Any]:
     # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
     # hidden_states = jnp.pad(hidden_states, pad_width=pad)
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		0.0
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels if self.out_channels is None else self.out_channels

     lowerCamelCase__    :      Tuple					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      int					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Union[str, Any]					 =		nn.Dense(UpperCAmelCase			,     dtype=self.dtype	)

     lowerCamelCase__    :      Union[str, Any]					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      List[Any]					 =		nn.Dropout(self.dropout_prob	)
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

     lowerCamelCase__    :      Union[str, Any]					 =		None
     if use_nin_shortcut:
         lowerCamelCase__    :      Dict					 =		nn.Conv(
             UpperCAmelCase			,     kernel_size=(1, 1)			,     strides=(1, 1)			,     padding='VALID'			,     dtype=self.dtype			,     )







 def __call__(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int=True	)			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_states
     lowerCamelCase__    :      List[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		self.conva(UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		self.time_emb_proj(nn.swish(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[str]					 =		jnp.expand_dims(jnp.expand_dims(UpperCAmelCase			,     1	)			,     1	)
     lowerCamelCase__    :      List[str]					 =		hidden_states + temb

     lowerCamelCase__    :      Optional[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		self.dropout(UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		self.conva(UpperCAmelCase	)

     if self.conv_shortcut is not None:
         lowerCamelCase__    :      Dict					 =		self.conv_shortcut(UpperCAmelCase	)

     return hidden_states + residual





