



import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""detr"""
       _lowerCamelCase			    =				["""past_key_values"""]
       _lowerCamelCase			    =				{
           """hidden_size""": """d_model""",
           """num_attention_heads""": """encoder_attention_heads""",
       }



       def __init__(     self     ,lowercase=True     ,lowercase=None     ,lowercase=3     ,lowercase=100     ,lowercase=6     ,lowercase=2048     ,lowercase=8     ,lowercase=6     ,lowercase=2048     ,lowercase=8     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=True     ,lowercase="relu"     ,lowercase=256     ,lowercase=0.1     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=0.02     ,lowercase=1.0     ,lowercase=False     ,lowercase="sine"     ,lowercase="resnet50"     ,lowercase=True     ,lowercase=False     ,lowercase=1     ,lowercase=5     ,lowercase=2     ,lowercase=1     ,lowercase=1     ,lowercase=5     ,lowercase=2     ,lowercase=0.1     ,**lowercase     ,):

             """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(lowercase     ,lowercase):
                         UpperCAmelCase_  :	Optional[Any]				      =		backbone_config.get("model_type")
                         UpperCAmelCase_  :	Optional[int]				      =		CONFIG_MAPPING[backbone_model_type]
                         UpperCAmelCase_  :	int				      =		config_class.from_dict(lowercase)
                   # set timm attributes to None
                   UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[Any]				      =		None, None, None

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



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.encoder_attention_heads



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.d_model



       @classmethod
       def  A_   (     cls     ,lowercase     ,**lowercase):

             """simple docstring"""





             return cls(backbone_config=lowercase     ,**lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		copy.deepcopy(self.__dict__)
             if output["backbone_config"] is not None:
                   UpperCAmelCase_  :	Dict				      =		self.backbone_config.to_dict()
             UpperCAmelCase_  :	int				      =		self.__class__.model_type
             return output


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				version.parse("""1.11"""  )



       @property
       def  A_   (     self):

             """simple docstring"""





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



       @property
       def  A_   (     self):

             """simple docstring"""





             return 1E-5



       @property
       def  A_   (     self):

             """simple docstring"""





             return 12





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         =			False
__lowerCamelCase         =			False







def    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      return TrainCommand(__snake_case    )





class   snake_case_ (lowercase__  ):




       """simple docstring"""






       @staticmethod
       def  A_   (     lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		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     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		logging.get_logger("transformers-cli/training")

             UpperCAmelCase_  :	List[Any]				      =		"tf" if is_tf_available() else "torch"

             os.makedirs(args.output     ,exist_ok=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		args.output

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

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
             if args.task == "text_classification":
                   UpperCAmelCase_  :	List[Any]				      =		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_  :	str				      =		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_  :	str				      =		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_  :	List[str]				      =		args.validation_split
             UpperCAmelCase_  :	Tuple				      =		args.train_batch_size
             UpperCAmelCase_  :	Optional[int]				      =		args.valid_batch_size
             UpperCAmelCase_  :	Dict				      =		args.learning_rate
             UpperCAmelCase_  :	List[str]				      =		args.adam_epsilon



       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             raise NotImplementedError



       def  A_   (     self):

             """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)
