



"""simple docstring"""
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


_A													=   logging.get_logger(__name__)

_A													=   {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}





class 		__UpperCAmelCase				(	_a					):







 """simple docstring"""




 _snake_case       :							List[str]      	=	'conditional_detr'
 _snake_case       :							List[str]      	=	['past_key_values']
 _snake_case       :							Optional[int]      	=	{
     'hidden_size': 'd_model',
     'num_attention_heads': 'encoder_attention_heads',
 }





 def __init__(				self					: Dict						, A_					: str=True						, A_					: Optional[int]=None						, A_					: List[str]=3						, A_					: List[str]=3_00						, A_					: Tuple=6						, A_					: Dict=20_48						, A_					: int=8						, A_					: Union[str, Any]=6						, A_					: Optional[int]=20_48						, A_					: Tuple=8						, A_					: List[str]=0.0						, A_					: Dict=0.0						, A_					: int=True						, A_					: Tuple="relu"						, A_					: List[str]=2_56						, A_					: List[Any]=0.1						, A_					: str=0.0						, A_					: Tuple=0.0						, A_					: Optional[int]=0.02						, A_					: List[Any]=1.0						, A_					: Union[str, Any]=False						, A_					: int="sine"						, A_					: Optional[Any]="resnet50"						, A_					: Union[str, Any]=True						, A_					: List[str]=False						, A_					: Dict=2						, A_					: Any=5						, A_					: Dict=2						, A_					: List[Any]=1						, A_					: str=1						, A_					: List[Any]=2						, A_					: str=5						, A_					: Optional[Any]=2						, A_					: int=0.25						, **A_					: Optional[Any]						, )->	List[str]:


   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  						=							CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
     elif isinstance(lowerCAmelCase_						, lowerCAmelCase_	):
       __UpperCamelCase  						=							backbone_config.get("model_type"	)
       __UpperCamelCase  						=							CONFIG_MAPPING[backbone_model_type]
       __UpperCamelCase  						=							config_class.from_dict(lowerCAmelCase_	)

   __UpperCamelCase  						=							use_timm_backbone
   __UpperCamelCase  						=							backbone_config
   __UpperCamelCase  						=							num_channels
   __UpperCamelCase  						=							num_queries
   __UpperCamelCase  						=							d_model
   __UpperCamelCase  						=							encoder_ffn_dim
   __UpperCamelCase  						=							encoder_layers
   __UpperCamelCase  						=							encoder_attention_heads
   __UpperCamelCase  						=							decoder_ffn_dim
   __UpperCamelCase  						=							decoder_layers
   __UpperCamelCase  						=							decoder_attention_heads
   __UpperCamelCase  						=							dropout
   __UpperCamelCase  						=							attention_dropout
   __UpperCamelCase  						=							activation_dropout
   __UpperCamelCase  						=							activation_function
   __UpperCamelCase  						=							init_std
   __UpperCamelCase  						=							init_xavier_std
   __UpperCamelCase  						=							encoder_layerdrop
   __UpperCamelCase  						=							decoder_layerdrop
   __UpperCamelCase  						=							encoder_layers
   __UpperCamelCase  						=							auxiliary_loss
   __UpperCamelCase  						=							position_embedding_type
   __UpperCamelCase  						=							backbone
   __UpperCamelCase  						=							use_pretrained_backbone
   __UpperCamelCase  						=							dilation
   # Hungarian matcher
   __UpperCamelCase  						=							class_cost
   __UpperCamelCase  						=							bbox_cost
   __UpperCamelCase  						=							giou_cost
   # Loss coefficients
   __UpperCamelCase  						=							mask_loss_coefficient
   __UpperCamelCase  						=							dice_loss_coefficient
   __UpperCamelCase  						=							cls_loss_coefficient
   __UpperCamelCase  						=							bbox_loss_coefficient
   __UpperCamelCase  						=							giou_loss_coefficient
   __UpperCamelCase  						=							focal_alpha
   super().__init__(is_encoder_decoder=lowerCAmelCase_						, **lowerCAmelCase_	)





 @property
 def  A  (				self					: List[Any]	)->	int:
   return self.encoder_attention_heads





 @property
 def  A  (				self					: Tuple	)->	int:
   return self.d_model





 def  A  (				self					: Tuple	)->	int:
   __UpperCamelCase  						=							copy.deepcopy(self.__dict__	)
   if self.backbone_config is not None:
     __UpperCamelCase  						=							self.backbone_config.to_dict()
   __UpperCamelCase  						=							self.__class__.model_type
   return output





class 		__UpperCAmelCase				(	_a					):







 """simple docstring"""




 _snake_case       :							Dict      	=	version.parse('1.11'					)





 @property
 def  A  (				self					: int	)->	Mapping[str, Mapping[int, str]]:
   return OrderedDict(
       [
           ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ("pixel_mask", {0: "batch"}),
       ]	)





 @property
 def  A  (				self					: str	)->	float:
   return 1e-5





 @property
 def  A  (				self					: str	)->	int:
   return 12





'''simple docstring'''


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    PreTrainedTokenizer					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[int] = None					,		):





							'''simple docstring'''


							_a      	=							{}

							if train_file is not None:
														_a      	=							[train_file]
							if eval_file is not None:
														_a      	=							[eval_file]
							if test_file is not None:
														_a      	=							[test_file]

							_a      	=							datasets.load_dataset('''csv'''					,		data_files=UpperCamelCase )
							_a      	=							list(ds[list(files.keys() )[0]].features.keys() )
							_a      	=							features_name.pop(UpperCamelCase )
							_a      	=							list(set(ds[list(files.keys() )[0]][label_name] ) )
							_a      	=							{label: i for i, label in enumerate(UpperCamelCase )}
							_a      	=							tokenizer.model_input_names
							_a      	=							{}

							if len(UpperCamelCase ) == 1:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        example[features_name[0]]					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length''' )					,		batched=UpperCamelCase					,		)
							elif len(UpperCamelCase ) == 2:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length'''					,		)					,		batched=UpperCamelCase					,		)

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														_a      	=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														_a      	=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														_a      	=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

							return train_ds, val_ds, test_ds, labelaid


_snake_case : str  				=						logging.getLogger(__name__)

@dataclass
class      A       :
							lowercase_							     =							field(metadata={'help': 'Which column contains the label'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the training file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the development file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the test file'}       )
							lowercase_							     =							field(
							    default=128	,metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }	,)
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Overwrite the cached training and evaluation sets'}       )



@dataclass
class      A       :
							lowercase_							     =							field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'Set this flag to use fast tokenization.'}       )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,)



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
							_a	,				_a	,				_a      	=							parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir )
							    and os.listdir(training_args.output_dir )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
														    ''' --overwrite_output_dir to overcome.''' )

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,		datefmt='''%m/%d/%Y %H:%M:%S'''					,		level=logging.INFO					,		)
							logger.info(
							    f'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '
							    f'16-bits training: {training_args.fpaa}' )
							logger.info(f'Training/evaluation parameters {training_args}' )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							_a      	=							AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,		cache_dir=model_args.cache_dir					,		)

							_a	,				_a	,				_a	,				_a      	=							get_tfds(
							    train_file=data_args.train_file					,		eval_file=data_args.dev_file					,		test_file=data_args.test_file					,		tokenizer=UpperCamelCase					,		label_column_id=data_args.label_column_id					,		max_seq_length=data_args.max_seq_length					,		)

							_a      	=							AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,		num_labels=len(UpperCamelCase )					,		labelaid=UpperCamelCase					,		idalabel={id: label for label, id in labelaid.items()}					,		finetuning_task='''text-classification'''					,		cache_dir=model_args.cache_dir					,		)

							with training_args.strategy.scope():
														_a      	=							TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path					,		from_pt=bool('''.bin''' in model_args.model_name_or_path )					,		config=UpperCamelCase					,		cache_dir=model_args.cache_dir					,		)

							def compute_metrics(UpperCamelCase			:    EvalPrediction ) -> Dict:
														_a      	=							np.argmax(p.predictions					,		axis=1 )

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							_a      	=							TFTrainer(
							    model=UpperCamelCase					,		args=UpperCamelCase					,		train_dataset=UpperCamelCase					,		eval_dataset=UpperCamelCase					,		compute_metrics=UpperCamelCase					,		)

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir )

							# Evaluation
							_a      	=							{}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***''' )
														_a      	=							trainer.evaluate()
														_a      	=							os.path.join(training_args.output_dir					,		'''eval_results.txt''' )

														with open(UpperCamelCase					,		'''w''' ) as writer:
																					logger.info('''***** Eval results *****''' )

																					for key, value in result.items():
																												logger.info(f'  {key} = {value}' )
																												writer.write(f'{key} = {value}\n' )

																					results.update(UpperCamelCase )

							return results


if __name__ == "__main__":
				main()

