





'''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__							:							Optional[Any] =False
a__							:							str =False
def 				lowercase__   (   __lowercase						:  Namespace     ) ->      int:



       """simple docstring"""






       return TrainCommand(__lowercase     )


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 @staticmethod
 def        _lowerCamelCase					(       __A       :							ArgumentParser	):
        __UpperCamelCase       =  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=3_2							,	help='Batch size for training.'	)
        train_parser.add_argument('--valid_batch_size'							,	type=__A							,	default=6_4							,	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       :							Any							,	__A       :							Namespace	):
        __UpperCamelCase       =  logging.get_logger('transformers-cli/training'	)

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

        os.makedirs(args.output							,	exist_ok=__A	)
        __UpperCamelCase       =  args.output

        __UpperCamelCase       =  args.column_label
        __UpperCamelCase       =  args.column_text
        __UpperCamelCase       =  args.column_id

        self.logger.info(f'''Loading {args.task} pipeline for {args.model}'''	)
        if args.task == "text_classification":
               __UpperCamelCase       =  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       =  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       =  None
        if args.validation_data:
               self.logger.info(f'''Loading validation dataset from {args.validation_data}'''	)
               __UpperCamelCase       =  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       =  args.validation_split
        __UpperCamelCase       =  args.train_batch_size
        __UpperCamelCase       =  args.valid_batch_size
        __UpperCamelCase       =  args.learning_rate
        __UpperCamelCase       =  args.adam_epsilon






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






 def        _lowerCamelCase					(       self       :							List[Any]	):
        raise NotImplementedError






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        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 os

import numpy
import onnx
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Union[str, Any]     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  a.name
       __UpperCamelCase       =  b.name

       __UpperCamelCase       =  ''
       __UpperCamelCase       =  ''

       __UpperCamelCase       =  a == b

       __UpperCamelCase       =  name_a
       __UpperCamelCase       =  name_b

       return res
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  List[Any]     ) ->      Optional[int]:



       """simple docstring"""






       for i, input_name in enumerate(node_proto.input     ):
              if input_name == name:
                     node_proto.input.insert(__lowercase				,  __lowercase     )
                     node_proto.input.pop(i + 1     )

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
              _graph_replace_input_with(node_proto.attribute[1].g				,  __lowercase				,  __lowercase     )
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[Any]				,  __lowercase						:  Dict     ) ->      int:



       """simple docstring"""






       for n in graph_proto.node:
              _node_replace_input_with(__lowercase				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  str     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  list(model.graph.initializer     )
       __UpperCamelCase       =  list(model_without_ext.graph.initializer     )
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              __UpperCamelCase       =  inits[i].name
              __UpperCamelCase       =  inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]     )

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph				,  __lowercase				,  __lowercase     )


def 				lowercase__   (   __lowercase						:  Dict     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  os.path.dirname(__lowercase     )
       __UpperCamelCase       =  os.path.basename(__lowercase     )

       __UpperCamelCase       =  onnx.load(os.path.join(__lowercase				,  __lowercase     )     )

       __UpperCamelCase       =  list(model.graph.initializer     )

       __UpperCamelCase       =  set()
       __UpperCamelCase       =  {}
       __UpperCamelCase       =  []

       __UpperCamelCase       =  0

       for i in range(len(__lowercase     )     ):
              if i in dup_set:
                     continue

              for j in range(i + 1				,  len(__lowercase     )     ):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]				,  inits[j]     ):
                            dup_set.add(__lowercase     )
                            dup_set.add(__lowercase     )

                            __UpperCamelCase       =  inits[j].data_type
                            __UpperCamelCase       =  numpy.prod(inits[j].dims     )
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print('unexpected data type: '				,  __lowercase     )
                            total_reduced_size += mem_size

                            __UpperCamelCase       =  inits[i].name
                            __UpperCamelCase       =  inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(__lowercase     )
                            else:
                                   __UpperCamelCase       =  [name_j]
                            ind_to_replace.append((j, i)     )

       print('total reduced size: '				,  total_reduced_size / 1024 / 1024 / 1024				,  'GB'     )

       __UpperCamelCase       =  sorted(__lowercase     )
       _remove_dup_initializers_from_model(__lowercase				,  __lowercase				,  __lowercase     )

       __UpperCamelCase       =  'optimized_' + model_file_name
       __UpperCamelCase       =  os.path.join(__lowercase				,  __lowercase     )
       onnx.save(__lowercase				,  __lowercase     )

       return new_model
