




"""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
_lowerCamelCase     :	Dict         =				False
_lowerCamelCase     :	str         =				False

def 						lowercase_					(						_UpperCAmelCase		):




	"""simple docstring"""






	return TrainCommand(__lowerCAmelCase		)

class 				lowercase					(			A__):







				@staticmethod
				def     a_     (							_lowerCamelCase      :    ArgumentParser				):



					"""simple docstring"""

					A_     : List[Any]		      =						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      :    Any					,   _lowerCamelCase      :    Namespace				):



					"""simple docstring"""

					A_     : Any		      =						logging.get_logger('''transformers-cli/training'''				)

					A_     : Dict		      =						"tf" if is_tf_available() else "torch"

					os.makedirs(args.output					,   exist_ok=_lowerCamelCase				)
					A_     : Optional[int]		      =						args.output

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

					self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""				)
					if args.task == "text_classification":
						A_     : Dict		      =						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}"""				)
					A_     : int		      =						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					,   )
					A_     : Optional[int]		      =						None
					if args.validation_data:
						self.logger.info(F"""Loading validation dataset from {args.validation_data}"""				)
						A_     : int		      =						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					,   )

					A_     : str		      =						args.validation_split
					A_     : List[Any]		      =						args.train_batch_size
					A_     : Any		      =						args.valid_batch_size
					A_     : Any		      =						args.learning_rate
					A_     : Tuple		      =						args.adam_epsilon







				def     a_     (							self      :    List[str]				):



					"""simple docstring"""

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







				def     a_     (							self      :    Tuple				):



					"""simple docstring"""

					raise NotImplementedError







				def     a_     (							self      :    Tuple				):



					"""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				)













import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase	:							Tuple				=logging.get_logger(__name__)

lowerCamelCase	:							Optional[Any]				={
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Optional[Any]        = '''conditional_detr'''
 _lowerCAmelCase       :       List[Any]        = ['''past_key_values''']
 _lowerCAmelCase       :       List[str]        = {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }







 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Tuple=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Tuple=3							,   SCREAMING_SNAKE_CASE					:   Any=3_00							,   SCREAMING_SNAKE_CASE					:   Tuple=6							,   SCREAMING_SNAKE_CASE					:   int=20_48							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=8							,   SCREAMING_SNAKE_CASE					:   int=6							,   SCREAMING_SNAKE_CASE					:   Dict=20_48							,   SCREAMING_SNAKE_CASE					:   Optional[int]=8							,   SCREAMING_SNAKE_CASE					:   Dict=0.0							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   int="relu"							,   SCREAMING_SNAKE_CASE					:   Any=2_56							,   SCREAMING_SNAKE_CASE					:   int=0.1							,   SCREAMING_SNAKE_CASE					:   int=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=0.0							,   SCREAMING_SNAKE_CASE					:   List[str]=0.0_2							,   SCREAMING_SNAKE_CASE					:   Optional[int]=1.0							,   SCREAMING_SNAKE_CASE					:   List[str]=False							,   SCREAMING_SNAKE_CASE					:   int="sine"							,   SCREAMING_SNAKE_CASE					:   str="resnet50"							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   Tuple=False							,   SCREAMING_SNAKE_CASE					:   Tuple=2							,   SCREAMING_SNAKE_CASE					:   Tuple=5							,   SCREAMING_SNAKE_CASE					:   List[str]=2							,   SCREAMING_SNAKE_CASE					:   List[str]=1							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=1							,   SCREAMING_SNAKE_CASE					:   str=2							,   SCREAMING_SNAKE_CASE					:   Any=5							,   SCREAMING_SNAKE_CASE					:   int=2							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.2_5							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   ):


      '''simple docstring'''






      if backbone_config is not None and use_timm_backbone:
           raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."						)

      if not use_timm_backbone:
           if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
           elif isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Union[str, Any]       				=  backbone_config.get("model_type"						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING[backbone_model_type]
                UpperCamelCase__			:   Any       				=  config_class.from_dict(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  use_timm_backbone
      UpperCamelCase__			:   List[Any]       				=  backbone_config
      UpperCamelCase__			:   Tuple       				=  num_channels
      UpperCamelCase__			:   List[Any]       				=  num_queries
      UpperCamelCase__			:   Dict       				=  d_model
      UpperCamelCase__			:   Any       				=  encoder_ffn_dim
      UpperCamelCase__			:   List[str]       				=  encoder_layers
      UpperCamelCase__			:   List[str]       				=  encoder_attention_heads
      UpperCamelCase__			:   Optional[int]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  decoder_attention_heads
      UpperCamelCase__			:   int       				=  dropout
      UpperCamelCase__			:   Optional[int]       				=  attention_dropout
      UpperCamelCase__			:   Any       				=  activation_dropout
      UpperCamelCase__			:   int       				=  activation_function
      UpperCamelCase__			:   int       				=  init_std
      UpperCamelCase__			:   List[Any]       				=  init_xavier_std
      UpperCamelCase__			:   List[str]       				=  encoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  decoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  encoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  auxiliary_loss
      UpperCamelCase__			:   List[str]       				=  position_embedding_type
      UpperCamelCase__			:   Optional[Any]       				=  backbone
      UpperCamelCase__			:   Optional[int]       				=  use_pretrained_backbone
      UpperCamelCase__			:   List[Any]       				=  dilation
      # Hungarian matcher
      UpperCamelCase__			:   List[str]       				=  class_cost
      UpperCamelCase__			:   Union[str, Any]       				=  bbox_cost
      UpperCamelCase__			:   int       				=  giou_cost
      # Loss coefficients
      UpperCamelCase__			:   str       				=  mask_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  dice_loss_coefficient
      UpperCamelCase__			:   int       				=  cls_loss_coefficient
      UpperCamelCase__			:   Tuple       				=  bbox_loss_coefficient
      UpperCamelCase__			:   Any       				=  giou_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  focal_alpha
      super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 @property
 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      return self.encoder_attention_heads







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return self.d_model







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  copy.deepcopy(self.__dict__						)
      if self.backbone_config is not None:
           UpperCamelCase__			:   List[str]       				=  self.backbone_config.to_dict()
      UpperCamelCase__			:   int       				=  self.__class__.model_type
      return output







class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = version.parse('''1.11'''       )







 @property
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ("pixel_mask", {0: "batch"}),
          ]						)







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return 1e-5







 @property
 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      return 12