




'''simple docstring'''


import copy
from typing import Dict, List, Optional

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


_snake_case : Any  				=						{
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_snake_case : Optional[int]  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							'mask2former'
							lowercase_							     =							['swin']
							lowercase_							     =							{'hidden_size': 'hidden_dim'}







							def __init__(    self			:    Any				,     lowerCAmelCase_			:    Optional[Dict] = None				,     lowerCAmelCase_			:    int = 2_56				,     lowerCAmelCase_			:    int = 2_56				,     lowerCAmelCase_			:    int = 2_56				,     lowerCAmelCase_			:    int = 10_24				,     lowerCAmelCase_			:    str = "relu"				,     lowerCAmelCase_			:    int = 6				,     lowerCAmelCase_			:    int = 10				,     lowerCAmelCase_			:    int = 8				,     lowerCAmelCase_			:    float = 0.0				,     lowerCAmelCase_			:    int = 20_48				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    int = 4				,     lowerCAmelCase_			:    int = 2_55				,     lowerCAmelCase_			:    int = 1_00				,     lowerCAmelCase_			:    float = 0.1				,     lowerCAmelCase_			:    float = 2.0				,     lowerCAmelCase_			:    float = 5.0				,     lowerCAmelCase_			:    float = 5.0				,     lowerCAmelCase_			:    int = 1_25_44				,     lowerCAmelCase_			:    float = 3.0				,     lowerCAmelCase_			:    float = 0.7_5				,     lowerCAmelCase_			:    float = 0.0_2				,     lowerCAmelCase_			:    float = 1.0				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    List[int] = [4, 8, 16, 32]				,     lowerCAmelCase_			:    bool = None				,     **lowerCAmelCase_			:    int				,     )     ->				Optional[Any]:


														"""simple docstring"""


														if backbone_config is None:
																					logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''   )
																					_a      	=							CONFIG_MAPPING['''swin'''](
																					    image_size=2_24				,     in_channels=3				,     patch_size=4				,     embed_dim=96				,     depths=[2, 2, 18, 2]				,     num_heads=[3, 6, 12, 24]				,     window_size=7				,     drop_path_rate=0.3				,     use_absolute_embeddings=lowerCAmelCase_				,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,     )

														if isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ):
																					_a      	=							backbone_config.pop('''model_type'''   )
																					_a      	=							CONFIG_MAPPING[backbone_model_type]
																					_a      	=							config_class.from_dict(lowerCAmelCase_   )

														# verify that the backbone is supported
														if backbone_config.model_type not in self.backbones_supported:
																					logger.warning_once(
																					    F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
																					    F'Supported model types: {",".join(self.backbones_supported   )}'   )

														_a      	=							backbone_config
														_a      	=							feature_size
														_a      	=							mask_feature_size
														_a      	=							hidden_dim
														_a      	=							encoder_feedforward_dim
														_a      	=							activation_function
														_a      	=							encoder_layers
														_a      	=							decoder_layers
														_a      	=							num_attention_heads
														_a      	=							dropout
														_a      	=							dim_feedforward
														_a      	=							pre_norm
														_a      	=							enforce_input_projection
														_a      	=							common_stride
														_a      	=							ignore_value
														_a      	=							num_queries
														_a      	=							no_object_weight
														_a      	=							class_weight
														_a      	=							mask_weight
														_a      	=							dice_weight
														_a      	=							train_num_points
														_a      	=							oversample_ratio
														_a      	=							importance_sample_ratio
														_a      	=							init_std
														_a      	=							init_xavier_std
														_a      	=							use_auxiliary_loss
														_a      	=							feature_strides
														_a      	=							output_auxiliary_logits
														_a      	=							decoder_layers

														super().__init__(**lowerCAmelCase_   )







							@classmethod
							def   __lowerCAmelCase     (    cls			:    Any				,     lowerCAmelCase_			:    PretrainedConfig				,     **lowerCAmelCase_			:    List[str]   )     ->				Optional[int]:


														"""simple docstring"""


														return cls(
														    backbone_config=lowerCAmelCase_				,     **lowerCAmelCase_				,     )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict[str, any]:


														"""simple docstring"""


														_a      	=							copy.deepcopy(self.__dict__   )
														_a      	=							self.backbone_config.to_dict()
														_a      	=							self.__class__.model_type
														return output







'''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()

