


'''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
__A	=False
__A	=False







def     _UpperCamelCase      (		UpperCamelCase__					):
			return TrainCommand(UpperCamelCase__					)


class 		_snake_case					(     a__	):




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     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-0_8							,							help="""Epsilon for Adam optimizer.""")
								train_parser.set_defaults(func=_lowerCamelCase)




					def __init__(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					int        =     logging.get_logger("""transformers-cli/training""")

								UpperCAmelCase__  :					Tuple        =     """tf""" if is_tf_available() else """torch"""

								os.makedirs(args.output							,							exist_ok=_lowerCamelCase)
								UpperCAmelCase__  :					str        =     args.output

								UpperCAmelCase__  :					Optional[Any]        =     args.column_label
								UpperCAmelCase__  :					Any        =     args.column_text
								UpperCAmelCase__  :					Dict        =     args.column_id

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

								UpperCAmelCase__  :					Dict        =     args.validation_split
								UpperCAmelCase__  :					List[str]        =     args.train_batch_size
								UpperCAmelCase__  :					Optional[int]        =     args.valid_batch_size
								UpperCAmelCase__  :					Optional[Any]        =     args.learning_rate
								UpperCAmelCase__  :					Optional[int]        =     args.adam_epsilon




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




					def 				snake_case__						(      self):
								raise NotImplementedError




					def 				snake_case__						(      self):
								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'''



from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)


class 		_snake_case					(     a__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=0):
								UpperCAmelCase__  :					Dict        =     1.0 if scale is None else scale
								UpperCAmelCase__  :					Dict        =     0.0 if loc is None else loc

								super().__init__(_lowerCamelCase							,							[AffineTransform(loc=self.loc							,							scale=self.scale							,							event_dim=_lowerCamelCase)])




					@property
					def 				snake_case__						(      self):
								return self.base_dist.mean * self.scale + self.loc




					@property
					def 				snake_case__						(      self):
								return self.base_dist.variance * self.scale**2




					@property
					def 				snake_case__						(      self):
								return self.variance.sqrt()


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase):
								super().__init__(**_lowerCamelCase)
								UpperCAmelCase__  :					int        =     args_dim
								UpperCAmelCase__  :					Optional[int]        =     nn.ModuleList([nn.Linear(_lowerCamelCase							,							_lowerCamelCase) for dim in args_dim.values()])
								UpperCAmelCase__  :					Optional[Any]        =     domain_map




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[int]        =     [proj(_lowerCamelCase) for proj in self.proj]

								return self.domain_map(*_lowerCamelCase)


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__  :					Optional[int]        =     function




					def 				snake_case__						(      self							,							_lowerCamelCase							,							*_lowerCamelCase):
								return self.function(_lowerCamelCase							,							*_lowerCamelCase)


class 		_snake_case					:
					lowerCAmelCase      :type
					lowerCAmelCase      :int
					lowerCAmelCase      :Dict[str, int]




					def __init__(      self							,							_lowerCamelCase = 1):
								UpperCAmelCase__  :					Optional[Any]        =     dim
								UpperCAmelCase__  :					int        =     {k: dim * self.args_dim[k] for k in self.args_dim}




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.dim == 1:
											return self.distribution_class(*_lowerCamelCase)
								else:
											return Independent(self.distribution_class(*_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__  :					Dict        =     self._base_distribution(_lowerCamelCase)
								if loc is None and scale is None:
											return distr
								else:
											return AffineTransformed(_lowerCamelCase							,							loc=_lowerCamelCase							,							scale=_lowerCamelCase							,							event_dim=self.event_dim)




					@property
					def 				snake_case__						(      self):
								return () if self.dim == 1 else (self.dim,)




					@property
					def 				snake_case__						(      self):
								return len(self.event_shape)




					@property
					def 				snake_case__						(      self):
								return 0.0




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return ParameterProjection(
								    in_features=_lowerCamelCase							,							args_dim=self.args_dim							,							domain_map=LambdaLayer(self.domain_map)							,							)




					def 				snake_case__						(      self							,							*_lowerCamelCase):
								raise NotImplementedError()




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								return (x + torch.sqrt(torch.square(_lowerCamelCase) + 4.0)) / 2.0


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"df": 1, "loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					StudentT




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								UpperCAmelCase__  :					Any        =     2.0 + cls.squareplus(_lowerCamelCase)
								return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					Normal




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								return loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"total_count": 1, "logits": 1}
					lowerCAmelCase      :type   =					NegativeBinomial




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase)
								return total_count.squeeze(-1), logits.squeeze(-1)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     distr_args
								if self.dim == 1:
											return self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)
								else:
											return Independent(self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     distr_args

								if scale is not None:
											# See scaling property of Gamma.
											logits += scale.log()

								return self._base_distribution((total_count, logits))