
import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE__   :			Dict												=    {
    "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
}

SCREAMING_SNAKE_CASE__   :			int												=    logging.get_logger(__name__)

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Any        =		"""mask2former"""
		a__       :						Dict        =		["""swin"""]
		a__       :						Any        =		{"""hidden_size""": """hidden_dim"""}






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Dict] = None							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 2_56							,			SCREAMING_SNAKE_CASE__		:							int = 10_24							,			SCREAMING_SNAKE_CASE__		:							str = "relu"							,			SCREAMING_SNAKE_CASE__		:							int = 6							,			SCREAMING_SNAKE_CASE__		:							int = 10							,			SCREAMING_SNAKE_CASE__		:							int = 8							,			SCREAMING_SNAKE_CASE__		:							float = 0.0							,			SCREAMING_SNAKE_CASE__		:							int = 20_48							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							int = 4							,			SCREAMING_SNAKE_CASE__		:							int = 2_55							,			SCREAMING_SNAKE_CASE__		:							int = 1_00							,			SCREAMING_SNAKE_CASE__		:							float = 0.1							,			SCREAMING_SNAKE_CASE__		:							float = 2.0							,			SCREAMING_SNAKE_CASE__		:							float = 5.0							,			SCREAMING_SNAKE_CASE__		:							float = 5.0							,			SCREAMING_SNAKE_CASE__		:							int = 1_25_44							,			SCREAMING_SNAKE_CASE__		:							float = 3.0							,			SCREAMING_SNAKE_CASE__		:							float = 0.75							,			SCREAMING_SNAKE_CASE__		:							float = 0.02							,			SCREAMING_SNAKE_CASE__		:							float = 1.0							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							List[int] = [4, 8, 16, 32]							,			SCREAMING_SNAKE_CASE__		:							bool = None							,			**SCREAMING_SNAKE_CASE__		:							Tuple							,			)     ->							str:
						if backbone_config is None:
										logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''  )
										__lowerCamelCase    					=	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=SCREAMING_SNAKE_CASE__							,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							,			)

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

						# 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  )}'''  )

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

						super().__init__(**SCREAMING_SNAKE_CASE__  )






		@classmethod
		def  __A       (		cls		:							Any							,			SCREAMING_SNAKE_CASE__		:							PretrainedConfig							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							List[Any]:
						return cls(
						    backbone_config=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							Any  )     ->							Dict[str, any]:
						__lowerCamelCase    					=	copy.deepcopy(self.__dict__  )
						__lowerCamelCase    					=	self.backbone_config.to_dict()
						__lowerCamelCase    					=	self.__class__.model_type
						return output





import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
					from transformers.trainer import DEFAULT_CALLBACKS

					from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							int  )     ->							Optional[int]:
						__lowerCamelCase    					=	[]






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							str							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Tuple:
						self.events.append('''on_init_end'''  )






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Any:
						self.events.append('''on_train_begin'''  )






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							Dict							,			**SCREAMING_SNAKE_CASE__		:							int  )     ->							Any:
						self.events.append('''on_train_end'''  )






		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							int:
						self.events.append('''on_epoch_begin'''  )






		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			**SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							Optional[Any]:
						self.events.append('''on_epoch_end'''  )






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			**SCREAMING_SNAKE_CASE__		:							str  )     ->							List[Any]:
						self.events.append('''on_step_begin'''  )






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Any							,			**SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							Tuple:
						self.events.append('''on_step_end'''  )






		def  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Union[str, Any]:
						self.events.append('''on_evaluate'''  )






		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str							,			**SCREAMING_SNAKE_CASE__		:							str  )     ->							str:
						self.events.append('''on_predict'''  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Tuple:
						self.events.append('''on_save'''  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			**SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							List[str]:
						self.events.append('''on_log'''  )







		def  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			**SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							str:
						self.events.append('''on_prediction_step'''  )





@require_torch
class    lowerCAmelCase__     (	unittest.TestCase			):






		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	tempfile.mkdtemp()






		def  __A       (		self		:							int  )     ->							List[str]:
						shutil.rmtree(self.output_dir  )






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							List[Any]=0							,			SCREAMING_SNAKE_CASE__		:							Any=0							,			SCREAMING_SNAKE_CASE__		:							List[str]=64							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=64							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=None							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=False							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Union[str, Any]:
						# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
						# its set to False since the tests later on depend on its value.
						__lowerCamelCase    					=	RegressionDataset(length=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	RegressionDataset(length=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	RegressionModelConfig(a=SCREAMING_SNAKE_CASE__							,			b=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	RegressionPreTrainedModel(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	TrainingArguments(self.output_dir							,			disable_tqdm=SCREAMING_SNAKE_CASE__							,			report_to=[]							,			**SCREAMING_SNAKE_CASE__  )
						return Trainer(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			train_dataset=SCREAMING_SNAKE_CASE__							,			eval_dataset=SCREAMING_SNAKE_CASE__							,			callbacks=SCREAMING_SNAKE_CASE__							,			)






		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							Optional[int]:
						self.assertEqual(len(SCREAMING_SNAKE_CASE__  )							,			len(SCREAMING_SNAKE_CASE__  )  )

						# Order doesn't matter
						__lowerCamelCase    					=	sorted(SCREAMING_SNAKE_CASE__							,			key=lambda SCREAMING_SNAKE_CASE__  : cb.__name__ if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else cb.__class__.__name__  )
						__lowerCamelCase    					=	sorted(SCREAMING_SNAKE_CASE__							,			key=lambda SCREAMING_SNAKE_CASE__  : cb.__name__ if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else cb.__class__.__name__  )

						for cba, cba in zip(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
										if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) and isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
														self.assertEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										elif isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) and not isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
														self.assertEqual(SCREAMING_SNAKE_CASE__							,			cba.__class__  )
										elif not isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) and isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
														self.assertEqual(cba.__class__							,			SCREAMING_SNAKE_CASE__  )
										else:
														self.assertEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							Tuple:
						__lowerCamelCase    					=	['''on_init_end''', '''on_train_begin''']
						__lowerCamelCase    					=	0
						__lowerCamelCase    					=	len(trainer.get_eval_dataloader()  )
						__lowerCamelCase    					=	['''on_prediction_step'''] * len(trainer.get_eval_dataloader()  ) + ['''on_log''', '''on_evaluate''']
						for _ in range(trainer.state.num_train_epochs  ):
										expected_events.append('''on_epoch_begin'''  )
										for _ in range(SCREAMING_SNAKE_CASE__  ):
														step += 1
														expected_events += ["on_step_begin", "on_step_end"]
														if step % trainer.args.logging_steps == 0:
																		expected_events.append('''on_log'''  )
														if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
																		expected_events += evaluation_events.copy()
														if step % trainer.args.save_steps == 0:
																		expected_events.append('''on_save'''  )
										expected_events.append('''on_epoch_end'''  )
										if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
														expected_events += evaluation_events.copy()
						expected_events += ["on_log", "on_train_end"]
						return expected_events






		def  __A       (		self		:							Union[str, Any]  )     ->							int:
						__lowerCamelCase    					=	self.get_trainer()
						__lowerCamelCase    					=	DEFAULT_CALLBACKS.copy() + [ProgressCallback]
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						# Callbacks passed at init are added to the default callbacks
						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]  )
						expected_callbacks.append(SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
						__lowerCamelCase    					=	self.get_trainer(disable_tqdm=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	DEFAULT_CALLBACKS.copy() + [PrinterCallback]
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[Any]  )     ->							str:
						__lowerCamelCase    					=	DEFAULT_CALLBACKS.copy() + [ProgressCallback]
						__lowerCamelCase    					=	self.get_trainer()

						# We can add, pop, or remove by class name
						trainer.remove_callback(SCREAMING_SNAKE_CASE__  )
						expected_callbacks.remove(SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.get_trainer()
						__lowerCamelCase    					=	trainer.pop_callback(SCREAMING_SNAKE_CASE__  )
						self.assertEqual(cb.__class__							,			SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						trainer.add_callback(SCREAMING_SNAKE_CASE__  )
						expected_callbacks.insert(0							,			SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						# We can also add, pop, or remove by instance
						__lowerCamelCase    					=	self.get_trainer()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[0]
						trainer.remove_callback(SCREAMING_SNAKE_CASE__  )
						expected_callbacks.remove(SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.get_trainer()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[0]
						__lowerCamelCase    					=	trainer.pop_callback(SCREAMING_SNAKE_CASE__  )
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						trainer.add_callback(SCREAMING_SNAKE_CASE__  )
						expected_callbacks.insert(0							,			SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )







		def  __A       (		self		:							Union[str, Any]  )     ->							Any:
						import warnings

						# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
						warnings.simplefilter(action='''ignore'''							,			category=SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						# Independent log/save/eval
						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]							,			logging_steps=5  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]							,			save_steps=5  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]							,			eval_steps=5							,			evaluation_strategy='''steps'''  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]							,			evaluation_strategy='''epoch'''  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						# A bit of everything
						__lowerCamelCase    					=	self.get_trainer(
						    callbacks=[MyTestTrainerCallback]							,			logging_steps=3							,			save_steps=10							,			eval_steps=5							,			evaluation_strategy='''steps'''							,			)
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						# warning should be emitted for duplicated callbacks
						with patch('''transformers.trainer_callback.logger.warning'''  ) as warn_mock:
										__lowerCamelCase    					=	self.get_trainer(
										    callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]							,			)
										assert str(SCREAMING_SNAKE_CASE__  ) in warn_mock.call_args[0][0]



