'''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
snake_case	=      False
snake_case	=      False







def       A_ (			_lowerCamelCase      :				Namespace     ):
      return TrainCommand(UpperCamelCase__     )





class 							SCREAMING_SNAKE_CASE (					snake_case_			):
  """simple docstring"""







  @staticmethod
  def 	a (	__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=__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 :       Any       ,       __lowerCAmelCase :       Tuple       ):


        """simple docstring"""



        _lowerCAmelCase		 = logging.get_logger('transformers-cli/training'       )

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

        os.makedirs(args.output       ,       exist_ok=__lowercase       )
        _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 	a (	self :       str       ):


        """simple docstring"""



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



  def 	a (	self :       List[str]       ):


        """simple docstring"""



        raise NotImplementedError



  def 	a (	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'''
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin

@dataclass
class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		torch.FloatTensor


class     _lowerCamelCase       (     snake_case_     ,		snake_case_					):




 '''simple docstring'''


 @register_to_config
 def __init__(   self   ,   __lowercase = 16   ,   __lowercase = 88   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = 1   ,   __lowercase = 0.0   ,   __lowercase = 32   ,   __lowercase = None   ,   __lowercase = False   ,   __lowercase = None   ,   __lowercase = "geglu"   ,   __lowercase = True   ,   __lowercase = True   ,   ):

  """simple docstring"""







  super().__init__()
  __A	:		Optional[int]	  =      num_attention_heads
  __A	:		Dict	  =      attention_head_dim
  __A	:		Optional[int]	  =      num_attention_heads * attention_head_dim

  __A	:		Optional[Any]	  =      in_channels

  __A	:		Optional[int]	  =      torch.nn.GroupNorm(num_groups=__lowercase   ,   num_channels=__lowercase   ,   eps=1E-6   ,   affine=__lowercase    )
  __A	:		Optional[Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )

  # 3. Define transformers blocks
  __A	:		List[Any]	  =      nn.ModuleList(
      [
          BasicTransformerBlock(
              __lowercase   ,   __lowercase   ,   __lowercase   ,   dropout=__lowercase   ,   cross_attention_dim=__lowercase   ,   activation_fn=__lowercase   ,   attention_bias=__lowercase   ,   double_self_attention=__lowercase   ,   norm_elementwise_affine=__lowercase   ,   )
          for d in range(__lowercase    )
      ]    )

  __A	:		Union[str, Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=1   ,   __lowercase=None   ,   __lowercase = True   ,   ):

  """simple docstring"""







  __A  ,__A  ,__A  ,__A	:		int	  =      hidden_states.shape
  __A	:		Union[str, Any]	  =      batch_frames // num_frames

  __A	:		Union[str, Any]	  =      hidden_states

  __A	:		List[str]	  =      hidden_states[None, :].reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   2   ,   1   ,   3   ,   4    )

  __A	:		Dict	  =      self.norm(__lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   3   ,   4   ,   2   ,   1    ).reshape(batch_size * height * width   ,   __lowercase   ,   __lowercase    )

  __A	:		Optional[Any]	  =      self.proj_in(__lowercase    )

  # 2. Blocks
  for block in self.transformer_blocks:
   __A	:		List[Any]	  =      block(
       __lowercase   ,   encoder_hidden_states=__lowercase   ,   timestep=__lowercase   ,   cross_attention_kwargs=__lowercase   ,   class_labels=__lowercase   ,   )

  # 3. Output
  __A	:		Optional[int]	  =      self.proj_out(__lowercase    )
  __A	:		int	  =      (
      hidden_states[None, None, :]
      .reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
      .permute(0   ,   3   ,   4   ,   1   ,   2    )
      .contiguous()
  )
  __A	:		List[str]	  =      hidden_states.reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )

  __A	:		str	  =      hidden_states + residual

  if not return_dict:
   return (output,)

  return TransformerTemporalModelOutput(sample=__lowercase    )






